BBC Sport asked Forest fan Saveen Johal a few questions about successes, hopes and fears... Given our current position, our campaign can and should be topped off with a Champions League place. Having finished previous seasons just above the relegation zone, we seem to have comfortably embedded ...

'Great shame if Forest were to miss out on European football' - Yahoo Sports

Calvin Bassey's first goal of the season handed Fulham their first Premier League win at Craven Cottage in 2025, but the scoreline didn't quite reflect how dominant their performance was against third-placed Nottingham Forest. Fulham had 24 shots throughout the match, the most any side has ...

Fulham were 'dominant' against Forest - Yahoo Sports

The 29-year-old joined the club in summer 2024 after spending time with Aston Villa and Sheffield United and has made eleven appearances in all competitions so far this season.Haywood now joins the Wo...

Haywood joins Nottingham Forest on loan - Yahoo Sports

Among the frontrunners, RGTI stands out as a developer of QC systems, quantum processors, and the operator of Forest — a cloud-based platform for quantum algorithm development.

‘The Nvidia of the Quantum Realm,’ Says Top Investor About Rigetti Computing Stock - TipRanks.com

Matheus Nunes says there will be no magic formula to get City winning consistently again.After a 3-0 victory over Nottingham Forest in midweek ended a run of four Premier League defeats, we were held ...

Nunes: No magic needed, just hard work - Yahoo Sports

To protect forests, scientists have developed Forest 4.0, an intelligent forest data processing model ... ... Nov. 25, 2024  Physical reservoir computing (PRC) utilizing synaptic devices shows significant promise for edge AI. Researchers from the Tokyo University of Science have introduced ...

ScienceDaily: Your source for the latest research news

Black Forest Labs, the German artificial intelligence startup that helps Elon Musk’s Grok produce images, is in talks to raise as much as $200 million from investors including Andreessen Horowitz less than four months after it launched.

A16z in Talks to Lead $200 Million Round in Black Forest Labs, Startup Behind AI Images on Grok

Today, on the margins of the G20, a group of public and private sector leaders launched the Brazil Restoration & Bioeconomy Finance Coalition (BRB

Brazil Restoration & Bioeconomy Finance Coalition Launched to Mobilize $10 Billion for Forest Conservation and Bioeconomy by 2030 | Business Wire

Exmouth's Tiny Forest is recognised for its contribution to the environment and rural England.

Tiny Devon forest wins two Royal Horticultural Society awards - BBC News

Forest

forest

One Decision Tree Is a Risk—A Forest? That’s Smart Machine Learning

1. Forget about hardware; the real money is in software For months, headlines have fixated on Huawei's limitations in producing advanced 7nm chips, while Apple excels with its cutting-edge 3nm technology. However, this focus on hardware overlooks a critical lesson from history: software, not hardware, drives  profits. Bill Gates built the Microsoft empire not by building hardware—outsourced to IBM—but by dominating the software business. Today, Microsoft’s software business dwarfs IBM's historical combined hardware and software projects. Huawei’s HarmonyOS NEXT, already claimed to be the world’s third-largest mobile OS with over a billion installations, represents a significant milestone. While currently present mostly in China, the system has the potential to expand to other markets, particularly in the Global South. HarmonyOS could appeal to nations seeking to reduce reliance on U.S.-based technology. A large user base often translates into substantial revenue streams over time. Moreover, Huawei is working on extending its OS to personal computers under the name HarmonyOS PC, with a target release in Q3 2025. Even if this initiative gains traction solely within China, particularly in government systems, it could reduce the dominance of Microsoft Windows and Apple macOS, cutting into their lucrative revenue streams. 2. Know-How and Engineering Workforce Unlike hardware chip technology, software OS tools and expertise are widely accessible, starting with the open-source availability of Android OS. This means the necessary know-how is either readily available or easy to acquire. China also boasts a large skilled engineering workforce—reportedly four times larger than that of the U.S.—providing Huawei with ample talent at a competitive cost to develop its new OS. With sufficient time and resources, Huawei is likely to succeed in its HarmonyOS project. 3. Marketing or Reality? Reports suggest that HarmonyOS is facing some challenges. According to a media report, even the migration of China’s own WeChat app to the platform has encountered difficulties. This is not entirely surprising, as building a stable operating system is a highly complex and demanding task. Even Steve Jobs famously staged parts of his iPhone presentation during its early development stages. It required time and engineering effort for Apple to refine and perfect the product. Huawei faces a similar trajectory. If it commits to the project with enough talent, funding, and patience, HarmonyOS could mature into a serious alternative to Microsoft and Apple’s software dominance. 4 References [1] HarmonyOS [2] HarmonyOS NEXT [3] China's first fully home-grown mobile operating system HarmonyOS NEXT launched [4] https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ [5] HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS [6] Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? [7] Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says [8] In Huawei’s battle against Apple, chips are still a weakness, says analyst [9] HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman [10] Huawei HarmonyOS PC rumored to delay till Q3 2025 [11] HarmonyOS PC UI design allegedly surfaced on Huawei Developer website [12] The Global Distribution of STEM Graduates: Which Countries Lead the Way? [13] How Steve Jobs Faked His Way Through Unveiling the iPhone 1. Forget about hardware; the real money is in software For months, headlines have fixated on Huawei's limitations in producing advanced 7nm chips, while Apple excels with its cutting-edge 3nm technology. However, this focus on hardware overlooks a critical lesson from history: software, not hardware, drives  profits. Bill Gates built the Microsoft empire not by building hardware—outsourced to IBM—but by dominating the software business. Today, Microsoft’s software business dwarfs IBM's historical combined hardware and software projects. Huawei’s HarmonyOS NEXT , already claimed to be the world’s third-largest mobile OS with over a billion installations, represents a significant milestone. While currently present mostly in China, the system has the potential to expand to other markets, particularly in the Global South. HarmonyOS could appeal to nations seeking to reduce reliance on U.S.-based technology. A large user base often translates into substantial revenue streams over time. HarmonyOS NEXT world’s third-largest mobile OS Moreover, Huawei is working on extending its OS to personal computers under the name HarmonyOS PC , with a target release in Q3 2025 . Even if this initiative gains traction solely within China, particularly in government systems, it could reduce the dominance of Microsoft Windows and Apple macOS, cutting into their lucrative revenue streams. extending its OS to personal computers HarmonyOS PC Q3 2025 2. Know-How and Engineering Workforce Unlike hardware chip technology, software OS tools and expertise are widely accessible, starting with the open-source availability of Android OS. This means the necessary know-how is either readily available or easy to acquire. China also boasts a large skilled engineering workforce —reportedly four times larger than that of the U.S.—providing Huawei with ample talent at a competitive cost to develop its new OS. With sufficient time and resources, Huawei is likely to succeed in its HarmonyOS project. large skilled engineering workforce 3. Marketing or Reality? Reports suggest that HarmonyOS is facing some challenges. According to a media report, even the migration of China’s own WeChat app to the platform has encountered difficulties. This is not entirely surprising, as building a stable operating system is a highly complex and demanding task. Even Steve Jobs famously staged parts of his iPhone presentation during its early development stages. It required time and engineering effort for Apple to refine and perfect the product. Huawei faces a similar trajectory. If it commits to the project with enough talent, funding, and patience, HarmonyOS could mature into a serious alternative to Microsoft and Apple’s software dominance. 4 References [1] HarmonyOS HarmonyOS [2] HarmonyOS NEXT HarmonyOS NEXT [3] China's first fully home-grown mobile operating system HarmonyOS NEXT launched China's first fully home-grown mobile operating system HarmonyOS NEXT launched [4] https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ https://www.huaweicentral.com/category/harmonyos/harmonyos-next/ [5] HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS HarmonyOS NEXT becomes the world’s third major mobile OS, joining Android and iOS [6] Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? Huawei’s HarmonyOS NEXT faces major test with WeChat: Will it pass? [7] Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says Huawei’s China-Made 7nm Chip ‘Years Behind US’, Raimondo Says [8] In Huawei’s battle against Apple, chips are still a weakness, says analyst In Huawei’s battle against Apple, chips are still a weakness, says analyst [9] HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman HuaweiNext-gen Huawei PCs will ditch Windows for HarmonyOS: Chairman [10] Huawei HarmonyOS PC rumored to delay till Q3 2025 Huawei HarmonyOS PC rumored to delay till Q3 2025 [11] HarmonyOS PC UI design allegedly surfaced on Huawei Developer website HarmonyOS PC UI design allegedly surfaced on Huawei Developer website [12] The Global Distribution of STEM Graduates: Which Countries Lead the Way? The Global Distribution of STEM Graduates: Which Countries Lead the Way? [13] How Steve Jobs Faked His Way Through Unveiling the iPhone How Steve Jobs Faked His Way Through Unveiling the iPhone

Experts Skeptical of Huawei Silicon May Be Missing the Forest for the Trees

الخبراء المتشككون في السيليكون الخاص بشركة هواوي ربما يغفلون عن حقيقة الأمر

“Huawei Silicon”a şübhə ilə yanaşan ekspertlər ağaclar üçün meşədən məhrum ola bilərlər

Stručnjaci skeptični prema Huawei Silicijum možda nedostaje šuma za drveće

Los expertos escépticos sobre el silicio de Huawei podrían estar perdiendo de vista el bosque detrás de los árboles

מומחים הספקנים לגבי סיליקון Huawei עשויים להחמיץ את היער בשביל העצים

ファーウェイのシリコンに懐疑的な専門家は、木を見て森を見ずかもしれない

Pakar Ragu-ragu Huawei Silicon Mungkin Hilang Hutan untuk Pokok

Ditsebi tše di Belaelago Silicon ya Huawei e ka ba e Hlolwa Sethokgwa bakeng sa Dihlare

Стручњаци скептични према Хуавеи силицијуму можда недостаје шума за дрвеће

Hünärmenler Huawei Silikonyna şübheli garaýarlar, agaçlar üçin tokaýy ýitirip bilerler

Huawei Silikonuna Şüpheyle Yaklaşan Uzmanlar Ormanı Ağaçlardan Kaçırıyor Olabilir

Huawei kremniyiga shubha bilan qaraydigan ekspertlar daraxtlar uchun o‘rmon yetishmasligi mumkin

Authors:
(1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France;
(2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France;
(3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.2. The Signature Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature B. K-SIF and SIF Algorithms C. Additional Numerical Experiments 4.1. Parameters Sensitivity Analysis We investigate the behavior of K-SIF and SIF with respect to their two main parameters: the depth of the signature k and the number of split windows ω. For the sake of place, the experiment on the depth is postponed in Section C.1 in the Appendix. The Role of the Signature Split Window. The number of split windows allows the extraction of information over specific intervals (randomly selected) of the underlying data. Thus, at each tree node, the focus will be on a particular portion of the data, which is the same across all the sample curves for comparison purposes. This approach ensures that the analysis is performed on comparable sections of the data, providing a systematic way to examine and compare different intervals or features across the sample curves. We explore the role of this parameter with two different datasets that reproduce two types of anomaly scenarios. The first considers isolated anomalies in a small interval, while the second contains persistent ones across all the function parametrization. In this way, we observe the behavior of K-SIF and SIF with respect to different types of anomalies. The first dataset is constructed as follows. We simulate 100 constant functions. We then select at random 90% of these curves and Gaussian noise on a sub-interval; for the remaining 10% of the curves, we add Gaussian noise on another sub-interval, different from the first one. More precisely: • 90% of the curves, considered as normal, are generated according to with ε(t) ∼ N (0, 1), b ∼ U([0, 100]) and U representing the uniform distribution. • 10% of the curves, considered as abnormal, are generated according to where ε(t) ∼ N (0, 1) and b ∼ U([0, 100]). We simulate at random 90% of the paths with µ = 0, σ = 0.5, and consider them as normal data. Then, the remaining 10% are simulated with drift µ = 0.2, standard deviation σ = 0.4, and considered abnormal data. We compute K-SIF with different numbers of split windows, varying from 1 to 10, with a truncation level set equal to 2 and N = 1, 000 the number of trees. The experiment is repeated 100 times, and we report the averaged AUC under the ROC curves in Figure 1 for both datasets and three pre-selected dictionaries. For the first dataset, where anomalies manifest in a small portion of the functions, increasing the number of splits significantly enhances the algorithm’s performance in detecting anomalies. The performance improvement shows a plateau after nine split windows. In the case of the second dataset with persistent anomalies, a higher number of split windows has a marginal impact on the algorithm’s performance, maintaining satisfactory results. Therefore, without prior knowledge about the data, opting for a relatively high number of split windows, such as 10, would ensure robust performance in both scenarios. Additionally, a more significant number of split windows enables the computation of the signature on a smaller portion of the functions, leading to improved computational efficiency. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Authors: Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Background & Preliminaries 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.1. Functional Isolation Forest 2.2. The Signature Method 2.2. The Signature Method 3. Signature Isolation Forest Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature A. Additional Information About the Signature B. K-SIF and SIF Algorithms B. K-SIF and SIF Algorithms C. Additional Numerical Experiments C. Additional Numerical Experiments 4.1. Parameters Sensitivity Analysis We investigate the behavior of K-SIF and SIF with respect to their two main parameters: the depth of the signature k and the number of split windows ω. For the sake of place, the experiment on the depth is postponed in Section C.1 in the Appendix. The Role of the Signature Split Window. The number of split windows allows the extraction of information over specific intervals (randomly selected) of the underlying data. Thus, at each tree node, the focus will be on a particular portion of the data, which is the same across all the sample curves for comparison purposes. This approach ensures that the analysis is performed on comparable sections of the data, providing a systematic way to examine and compare different intervals or features across the sample curves. The Role of the Signature Split Window. We explore the role of this parameter with two different datasets that reproduce two types of anomaly scenarios. The first considers isolated anomalies in a small interval, while the second contains persistent ones across all the function parametrization. In this way, we observe the behavior of K-SIF and SIF with respect to different types of anomalies. The first dataset is constructed as follows. We simulate 100 constant functions. We then select at random 90% of these curves and Gaussian noise on a sub-interval; for the remaining 10% of the curves, we add Gaussian noise on another sub-interval, different from the first one. More precisely: • 90% of the curves, considered as normal, are generated according to with ε(t) ∼ N (0, 1), b ∼ U([0, 100]) and U representing the uniform distribution. • 10% of the curves, considered as abnormal, are generated according to where ε(t) ∼ N (0, 1) and b ∼ U([0, 100]). We simulate at random 90% of the paths with µ = 0, σ = 0.5, and consider them as normal data. Then, the remaining 10% are simulated with drift µ = 0.2, standard deviation σ = 0.4, and considered abnormal data. We compute K-SIF with different numbers of split windows, varying from 1 to 10, with a truncation level set equal to 2 and N = 1, 000 the number of trees. The experiment is repeated 100 times, and we report the averaged AUC under the ROC curves in Figure 1 for both datasets and three pre-selected dictionaries. For the first dataset, where anomalies manifest in a small portion of the functions, increasing the number of splits significantly enhances the algorithm’s performance in detecting anomalies. The performance improvement shows a plateau after nine split windows. In the case of the second dataset with persistent anomalies, a higher number of split windows has a marginal impact on the algorithm’s performance, maintaining satisfactory results. Therefore, without prior knowledge about the data, opting for a relatively high number of split windows, such as 10, would ensure robust performance in both scenarios. Additionally, a more significant number of split windows enables the computation of the signature on a smaller portion of the functions, leading to improved computational efficiency. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Decoding Split Window Sensitivity in Signature Isolation Forests

Afkodning af delt vinduesfølsomhed i signaturisolationsskove

Dekodierung der Split-Window-Empfindlichkeit in Signaturisolationswäldern

Descifrando la sensibilidad de la ventana dividida en bosques de aislamiento de firmas

Az osztott ablak érzékenységének dekódolása a Signature Isolation Forestsben

Decodifica della sensibilità della finestra divisa nelle foreste di isolamento delle firme

署名分離フォレストにおける分割ウィンドウの感度のデコード

Decoding Split Window Sensitivity ka Dithokgweng tša go Ikarola tša Mosaeno

Декодирование чувствительности разделенного окна в лесах изоляции сигнатур

Decoding Gutandukanya Idirishya Sensitivity mumashyamba yo kwigunga

Kusimbua Unyeti wa Dirisha la Kugawanyika katika Misitu ya Kutengwa kwa Sahihi

دستخطی تنہائی کے جنگلات میں اسپلٹ ونڈو کی حساسیت کو ڈی کوڈنگ کرنا

Ukuqonda Ukwahlulwa Kwefestile Yeefestile KuMsayino Wokwahlulwa Kwamahlathi

Authors:
(1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France;
(2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France;
(3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.2. The Signature Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature B. K-SIF and SIF Algorithms C. Additional Numerical Experiments 3. Signature Isolation Forest Method With Kernel Signature Isolation Forest (K-SIF), we aim to leverage the truncated kernel signature (Kiraly and Oberhauser, 2019) to overcome the linearity constraint imposed by the inner product in FIF. In contrast to FIF, which explores only one function characteristic at each node using a unique inner product with the function sampled in the dictionary, K-SIF captures significantly more information. This is achieved by computing several coefficient signatures, summarizing multiple data attributes at each node. The ability to explore non-linear directions in the feature space through the kernel signature makes the algorithm more efficient for an equivalent computational cost (refer to Figure 9 in the Appendix With Signature Isolation Forest (SIF), the aim is to leverage the coordinate signature and remove any restrictions imposed by FIF and K-SIF. We replace the use of a dictionary D and rely on the coordinate signature only to detect anomalies. The intuition is to provide a more straightforward, entirely data-driven solution and remove any a priori required choice. The construction procedures of (K-)SIF are now described. Splitting Criterion. The difference between a ksi-tree and a si-tree lies in the splitting criterion computed at each node of both trees. Below, we detail the splitting procedures occurring at a particular internal node for both algorithms. The following step is to choose uniformly and at random a split value γ such that The children datasets are given as A ksi-tree is built by iterating this procedure until all training data curves are isolated. Signature Isolation Forest. In contrast to ksi-trees, the definition of a si-tree will rely on using a different split criterion, expressed according to the following procedure. Once again, the depth of the truncation level k has to be chosen a priori, but no dictionaries nor distribution has to be set. Here, at each internal node (p, q), a coordinate (i1, . . . , iℓ) is chosen randomly and uniformly in the set The children datasets are given as A si-tree is also built by iterating this procedure until all training data curves are isolated. The two algorithms of K-SIF and SIF are summarized in the Appendix B. where c(m) is the average path length of unsuccessful searches in a binary search tree and m the size of the subsample linked to each tree. Parameters of K-SIF and SIF. For both algorithms, key parameters typical of isolation forest-based methods, such as the number of trees N or the subsample size m, must be pre-selected (Liu et al., 2008). The truncated level k of the signature depth must also be chosen. In the case of K-SIF, similar to FIF, selecting a dictionary D and a distribution ν is required. We opt to implement three standard dictionaries (also utilized in FIF): ‘Brownian’, representing a traditional Brownian motion path; ‘Cosine’, employing a cosine basis; and ‘Gaussian wavelets’, utilizing a Gaussian wavelets basis. In contrast, SIF does not require any of these sensitive parameters. We follow the approach outlined in Morrill et al. (2019) to enhance the algorithms’ performance by introducing a split window parameter ω. This implies that, at each tree node, the truncated signature is computed on a randomly selected portion of the functions with a size of ⌊p/ω⌋. 4. Numerical Experiments This section presents a series of numerical experiments supporting the proposed class of AD methods. We organize the experiments into three categories. First, a parameter sensitivity analysis sheds light on the behavior of the parameters algorithms and provides insights for achieving optimal performances. Subsequently, we compare (K-)SIF and FIF, illustrating the validity of such methods and showcasing the power of the signature in the context of AD solutions. Finally, we perform a benchmark that compares several existing AD methods on various real datasets. This benchmark aims to demonstrate the results in real applications and assess the overall performance of these algorithms relative to other AD methods documented in the literature. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Authors: Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Background & Preliminaries 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.1. Functional Isolation Forest 2.2. The Signature Method 2.2. The Signature Method 3. Signature Isolation Forest Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature A. Additional Information About the Signature B. K-SIF and SIF Algorithms B. K-SIF and SIF Algorithms C. Additional Numerical Experiments C. Additional Numerical Experiments 3. Signature Isolation Forest Method With Kernel Signature Isolation Forest (K-SIF), we aim to leverage the truncated kernel signature (Kiraly and Oberhauser, 2019) to overcome the linearity constraint imposed by the inner product in FIF. In contrast to FIF, which explores only one function characteristic at each node using a unique inner product with the function sampled in the dictionary, K-SIF captures significantly more information. This is achieved by computing several coefficient signatures, summarizing multiple data attributes at each node. The ability to explore non-linear directions in the feature space through the kernel signature makes the algorithm more efficient for an equivalent computational cost (refer to Figure 9 in the Appendix With Signature Isolation Forest (SIF), the aim is to leverage the coordinate signature and remove any restrictions imposed by FIF and K-SIF. We replace the use of a dictionary D and rely on the coordinate signature only to detect anomalies. The intuition is to provide a more straightforward, entirely data-driven solution and remove any a priori required choice. The construction procedures of (K-)SIF are now described. Splitting Criterion. The difference between a ksi-tree and a si-tree lies in the splitting criterion computed at each node of both trees. Below, we detail the splitting procedures occurring at a particular internal node for both algorithms. Splitting Criterion. The following step is to choose uniformly and at random a split value γ such that The children datasets are given as A ksi -tree is built by iterating this procedure until all training data curves are isolated. ksi Signature Isolation Forest. In contrast to ksi-trees, the definition of a si-tree will rely on using a different split criterion, expressed according to the following procedure. Once again, the depth of the truncation level k has to be chosen a priori, but no dictionaries nor distribution has to be set. Here, at each internal node (p, q), a coordinate (i1, . . . , iℓ) is chosen randomly and uniformly in the set The children datasets are given as A si-tree is also built by iterating this procedure until all training data curves are isolated. The two algorithms of K-SIF and SIF are summarized in the Appendix B. where c(m) is the average path length of unsuccessful searches in a binary search tree and m the size of the subsample linked to each tree. c(m) m Parameters of K-SIF and SIF. For both algorithms, key parameters typical of isolation forest-based methods, such as the number of trees N or the subsample size m, must be pre-selected (Liu et al., 2008). The truncated level k of the signature depth must also be chosen. In the case of K-SIF, similar to FIF, selecting a dictionary D and a distribution ν is required. We opt to implement three standard dictionaries (also utilized in FIF): ‘Brownian’, representing a traditional Brownian motion path; ‘Cosine’, employing a cosine basis; and ‘Gaussian wavelets’, utilizing a Gaussian wavelets basis. In contrast, SIF does not require any of these sensitive parameters. We follow the approach outlined in Morrill et al. (2019) to enhance the algorithms’ performance by introducing a split window parameter ω. This implies that, at each tree node, the truncated signature is computed on a randomly selected portion of the functions with a size of ⌊p/ω⌋. Parameters of K-SIF and SIF. 4. Numerical Experiments This section presents a series of numerical experiments supporting the proposed class of AD methods. We organize the experiments into three categories. First, a parameter sensitivity analysis sheds light on the behavior of the parameters algorithms and provides insights for achieving optimal performances. Subsequently, we compare (K-)SIF and FIF, illustrating the validity of such methods and showcasing the power of the signature in the context of AD solutions. Finally, we perform a benchmark that compares several existing AD methods on various real datasets. This benchmark aims to demonstrate the results in real applications and assess the overall performance of these algorithms relative to other AD methods documented in the literature. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Redefining Anomaly Detection with Signature Isolation Forests

Authors:
(1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France;
(2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France;
(3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.2. The Signature Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature B. K-SIF and SIF Algorithms C. Additional Numerical Experiments 2. Background & Preliminaries 2.1. Functional Isolation Forest Consider H the functional Hilbert space equipped with a inner product ⟨., .⟩H such that any x ∈ H is a real function defined on [0, 1]. A Functional Isolation Forest is created through an assembly of functional isolation trees (F-itrees). Each F-itree is constructed via a series of random splits from a subsample (of size m) of Xn. The abnormality score for an observation x is then determined as a monotonically decreasing transformation of x’s average depth across the trees. The core concept lies in the randomness of the splits, where an observation markedly different from others is more likely to be isolated from Xn, appearing at shallower levels in the F-itrees. The F-itrees are built based on a predetermined dictionary D ⊂ H, encompassing both deterministic and/or stochastic functions capturing pertinent data properties, which may also be a subset of Xn. Before each random univariate split, all node observations are projected onto a line defined by a randomly selected element from the dictionary D. The selection of a suitable dictionary plays a pivotal role in shaping the FIF score construction. The projection criterion at each node of each F-itree is defined as: This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Authors: Authors: (1) Guillaume Staerman, INRIA, CEA, Univ. Paris-Saclay, France; (2) Marta Campi, CERIAH, Institut de l’Audition, Institut Pasteur, France; (3) Gareth W. Peters, Department of Statistics & Applied Probability, University of California Santa Barbara, USA. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Background & Preliminaries 2. Background & Preliminaries 2.1. Functional Isolation Forest 2.1. Functional Isolation Forest 2.2. The Signature Method 2.2. The Signature Method 3. Signature Isolation Forest Method 3. Signature Isolation Forest Method 4. Numerical Experiments 4. Numerical Experiments 4.1. Parameters Sensitivity Analysis 4.1. Parameters Sensitivity Analysis 4.2. Advantages of (K-)SIF over FIF 4.2. Advantages of (K-)SIF over FIF 4.3. Real-data Anomaly Detection Benchmark 4.3. Real-data Anomaly Detection Benchmark 5. Discussion & Conclusion, Impact Statements, and References 5. Discussion & Conclusion, Impact Statements, and References Appendix A. Additional Information About the Signature A. Additional Information About the Signature B. K-SIF and SIF Algorithms B. K-SIF and SIF Algorithms C. Additional Numerical Experiments C. Additional Numerical Experiments 2. Background & Preliminaries 2.1. Functional Isolation Forest Consider H the functional Hilbert space equipped with a inner product ⟨., .⟩H such that any x ∈ H is a real function defined on [0, 1]. A Functional Isolation Forest is created through an assembly of functional isolation trees (F-itrees). Each F-itree is constructed via a series of random splits from a subsample (of size m) of Xn. The abnormality score for an observation x is then determined as a monotonically decreasing transformation of x’s average depth across the trees. The core concept lies in the randomness of the splits, where an observation markedly different from others is more likely to be isolated from Xn, appearing at shallower levels in the F-itrees. The F-itrees are built based on a predetermined dictionary D ⊂ H, encompassing both deterministic and/or stochastic functions capturing pertinent data properties, which may also be a subset of Xn. Before each random univariate split, all node observations are projected onto a line defined by a randomly selected element from the dictionary D. The selection of a suitable dictionary plays a pivotal role in shaping the FIF score construction. The projection criterion at each node of each F-itree is defined as: This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

How Functional Isolation Forest Detects Anomalies

What is the Signature Isolation Forest? 

MIAMI, Florida, August 15th, 2024, Chainwire/Floki has partnered with Nottingham Forest football club, which currently is playing the English Premier League (EPL). Announced on Thursday, Floki will be the official cryptocurrency partner of Nottingham Forest and have extensive marketing and promotional rights. “We are proud to join the ranks of the Premier League with a club that is an institution in world Football,” a media representative of Floki said. “Just as Nottingham Forest are building a future as a dominant force in world football, Floki is on the same journey to establish itself as one of the most successful brands in its sector, constantly challenging and innovating with brands like Valhalla.” https://www.youtube.com/embed/jwCh0CdGSEM During all home games, the Floki brand mark will be featured for three minutes on camera-facing LED boards from kick-off to the final whistle. This ensures that the Floki brand is seen by both stadium audiences and TV viewers. The Floki logo will also be prominently displayed. It will appear in four slots on official Premier League interview backdrops at each home match and in four spaces on press conference backdrops used throughout the season. Floki will have a strong presence on Nottingham Forest's social media channels. The brand will be mentioned in posts related to full-time scores for both the men’s and women’s senior teams and in all substitution announcements. Additionally, Floki will have a full-page presence in the matchday programmes distributed at each home game, and its logo will be displayed on the front cover. This helps connect Floki to the matchday experience and the fans. Fans can look forward to exciting offerings, such as signed merchandise. At the end of the season, Floki will offer fans the chance to participate in a ‘play on the pitch’ experience at the stadium. Paul Bell, Nottingham Forest's Chief Operating Officer (COO), said, “We are delighted to welcome Floki as an official partner and look forward to working closely with them as we head into the 2024/25 season. As one of the most innovative companies within this sector, we're excited to work collaboratively with the Floki community.” Popularity of EPL English Premier League (EPL) is the most popular sports league in the world. It has more viewers than both the UEFA Champions League and the Bundesliga combined. In the 2022/23 season, the EPL had a total audience of 3.23 billion people. These games were broadcast to 712 million households in 190 countries. According to Nielsen, 2.01 billion viewers watched live match broadcasts over the season. Additionally, 1.22 billion viewers tuned into highlight shows and other related programming. English Premier League clubs also have a massive following on social media with over half a billion followers spread across Facebook, Instagram, TikTok, X, and YouTube. Nottingham Forest's History Founded in 1865, Nottingham Forest is a club with a rich history. It is one of only eight clubs, including Liverpool, Ajax, Bayern Munich, and Real Madrid, to have won back-to-back European championships.Nottingham Forest also has a strong online presence, with over 4.5 million followers on various social media platforms. This includes one million fans on Facebook, 670,000 followers on X, one million followers on Instagram, 1.2 million followers on TikTok, and 184,000 subscribers on YouTube. About Floki Floki is the people’s cryptocurrency and utility token of the Floki Ecosystem. Floki aims to become the world’s most known and used cryptocurrency, focusing on utility, philanthropy, community, and marketing. Floki currently has over 490,000 holders and a strong brand recognized globally thanks to strategic marketing partnerships. Website | YouTube | Telegram | Instagram | TikTok | Discord | Facebook | Reddit | Twitch Contact Community Relations Officer Pedro Vidal Floki Marketing@floki.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here MIAMI, Florida, August 15th, 2024, Chainwire/Floki has partnered with Nottingham Forest football club, which currently is playing the English Premier League (EPL). Announced on Thursday, Floki will be the official cryptocurrency partner of Nottingham Forest and have extensive marketing and promotional rights. “We are proud to join the ranks of the Premier League with a club that is an institution in world Football,” a media representative of Floki said. “We are proud to join the ranks of the Premier League with a club that is an institution in world Football,” a media representative of Floki said. “Just as Nottingham Forest are building a future as a dominant force in world football, Floki is on the same journey to establish itself as one of the most successful brands in its sector, constantly challenging and innovating with brands like Valhalla.” “Just as Nottingham Forest are building a future as a dominant force in world football, Floki is on the same journey to establish itself as one of the most successful brands in its sector, constantly challenging and innovating with brands like Valhalla.” https://www.youtube.com/embed/jwCh0CdGSEM https://www.youtube.com/embed/jwCh0CdGSE M https://www.youtube.com/embed/jwCh0CdGSE During all home games, the Floki brand mark will be featured for three minutes on camera-facing LED boards from kick-off to the final whistle. This ensures that the Floki brand is seen by both stadium audiences and TV viewers. The Floki logo will also be prominently displayed. It will appear in four slots on official Premier League interview backdrops at each home match and in four spaces on press conference backdrops used throughout the season. Floki will have a strong presence on Nottingham Forest's social media channels. The brand will be mentioned in posts related to full-time scores for both the men’s and women’s senior teams and in all substitution announcements. Additionally, Floki will have a full-page presence in the matchday programmes distributed at each home game, and its logo will be displayed on the front cover. This helps connect Floki to the matchday experience and the fans. Fans can look forward to exciting offerings, such as signed merchandise. At the end of the season, Floki will offer fans the chance to participate in a ‘play on the pitch’ experience at the stadium. Paul Bell, Nottingham Forest's Chief Operating Officer (COO), said, “We are delighted to welcome Floki as an official partner and look forward to working closely with them as we head into the 2024/25 season. As one of the most innovative companies within this sector, we're excited to work collaboratively with the Floki community.” Paul Bell, Nottingham Forest's Chief Operating Officer (COO), said, “We are delighted to welcome Floki as an official partner and look forward to working closely with them as we head into the 2024/25 season. As one of the most innovative companies within this sector, we're excited to work collaboratively with the Floki community.” Popularity of EPL English Premier League (EPL) is the most popular sports league in the world. It has more viewers than both the UEFA Champions League and the Bundesliga combined. In the 2022/23 season, the EPL had a total audience of 3.23 billion people. These games were broadcast to 712 million households in 190 countries. According to Nielsen, 2.01 billion viewers watched live match broadcasts over the season. Additionally, 1.22 billion viewers tuned into highlight shows and other related programming. English Premier League clubs also have a massive following on social media with over half a billion followers spread across Facebook, Instagram, TikTok, X, and YouTube. Nottingham Forest's History Founded in 1865, Nottingham Forest is a club with a rich history. It is one of only eight clubs, including Liverpool, Ajax, Bayern Munich, and Real Madrid, to have won back-to-back European championships.Nottingham Forest also has a strong online presence, with over 4.5 million followers on various social media platforms. This includes one million fans on Facebook, 670,000 followers on X, one million followers on Instagram, 1.2 million followers on TikTok, and 184,000 subscribers on YouTube. About Floki Floki is the people’s cryptocurrency and utility token of the Floki Ecosystem. Floki aims to become the world’s most known and used cryptocurrency, focusing on utility, philanthropy, community, and marketing. Floki currently has over 490,000 holders and a strong brand recognized globally thanks to strategic marketing partnerships. Floki Floki Website | YouTube | Telegram | Instagram | TikTok | Discord | Facebook | Reddit | Twitch Website Website YouTube YouTube Telegram Telegram Instagram Instagram TikTok TikTok Discord Discord Facebook Facebook Reddit Reddit Twitch Twitch Contact Community Relations Officer Pedro Vidal Floki Marketing@floki.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here

Floki Becomes Official Cryptocurrency Partner Of Nottingham Forest F.C.

ফ্লোকি নটিংহাম ফরেস্ট এফসি-এর অফিসিয়াল ক্রিপ্টোকারেন্সি পার্টনার হয়ে উঠেছে

Floki wird offizieller Kryptowährungspartner von Nottingham Forest F.C.

Floki se convierte en socio oficial de criptomonedas del Nottingham Forest F.C.

Floki devient le partenaire officiel de crypto-monnaie du Nottingham Forest F.C.

फ्लोकी नॉटिंघम फॉरेस्ट एफ.सी. का आधिकारिक क्रिप्टोकरेंसी पार्टनर बन गया है।

Floki がノッティンガム・フォレスト FC の公式暗号通貨パートナーに

플로키, 노팅엄 포레스트 FC의 공식 암호화폐 파트너가 되다

Floki se torna parceiro oficial de criptomoeda do Nottingham Forest F.C.

Floki становится официальным криптовалютным партнером футбольного клуба Nottingham Forest.

Floki, Nottingham Forest FC'nin Resmi Kripto Para Ortağı Oldu

Floki trở thành đối tác tiền điện tử chính thức của Nottingham Forest F.C.

Floki 成为诺丁汉森林足球俱乐部官方加密货币合作伙伴

Author:
(1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Table of Links Abstract Background Data Methodology Results Discussion and Conclusion, and References A Appendix Tables and Figures 3 Methodology The identification strategy used in this study is Difference in Differences with Dummy Outcomes (Price and Wolfers, 2010), which focuses on coach and artist gender interaction. Specifically, we estimate the following linear probability model: The regression model in the difference-in-differences framework does not capture potential heterogeneous effects across covariates. There could be a positive effect on some groups in the covariates but a negative or no effect on other groups. For example, the coaches in the initial stage of the blind audition could elicit own-gender bias, but in the later stage, opposite-gender bias. The standard regression model will provide a weighted average of effect size, even if an effect varies across a population represented by different groups. One plausible solution is to estimate heterogeneous treatment effects. Instead of just estimating one effect, I estimate a distribution of effect size for a given unit with a given set of attributes and what their impact might be. To analyze this heterogeneous effect of own-gender bias, I incorporated the first difference approach into the causal forest approach. I will discuss the performance of the proposed causal forest approach and how to apply it to my empirical example. The causal forest approach is consistent and asymptotically Gaussian and provides an estimator for their asymptotic variance that allows us to construct valid confidence intervals (Athey and Wager, 2019). Causal forest utilizes a forest-based approach to calculate a similarity weight and then applies the local generalized method of moments to estimate based on a weighted set of neighbors. The authors also show that using the centered instead of the original outcome and treatment allows a forest to focus its splits on the features that affect the treatment effect instead of the primary outcome. Intuitively, a causal forest consists of regression trees (Athey and Imbens, 2016; Breiman et al., 1984), which estimate conditional average treatment effects (CATE) at the leaves of the trees instead of predicting the outcome variables. I utilize a two-step approach by incorporating the first-difference approach into the causal forest approach, the similar two-step approach in the standard differencein-difference method. In the first step, I estimate the first difference, which is the change in the selection of individual contestants from a male coach to a female coach. This step teases out the systematic differences between coaches in female and male coach groups. In the second step, I use the outcome variables obtained from the first step and apply the causal forest approach for heterogeneous treatment-effect analysis. This step teases out the effect of contestant gender trends that exist in both women and men. My methods rely heavily on the causal forest approach, a machine learning method that can estimate heterogeneous causal effect functions under the above assumptions. The main strength associated with the causal forest is that it provides a strategy for learning patterns of heterogeneity from the data. This method requires little researcher discretion compared to traditional subgroup analyses. For instance, one does not have to decide which cutoffs to use to analyze heterogeneity in effects along continuous covariates, nor do we require to define the functional condition of the heterogeneity; the causal forest algorithm is designed to automate this search and find the most appropriate model that best characterizes the heterogeneous effect in the data (Bonander and Svensson, 2021). This paper is available on arxiv under CC 4.0 license. Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Author: Author: (1) Anuar Assamidanov, Department of Economics, Claremont Graduate University, 150 E 10th St, Claremont, CA 91711. (Email: anuar.assamidanov@cgu.edu). Table of Links Abstract Abstract Background Background Data Data Methodology Methodology Results Results Discussion and Conclusion, and References Discussion and Conclusion, and References A Appendix Tables and Figures A Appendix Tables and Figures 3 Methodology The identification strategy used in this study is Difference in Differences with Dummy Outcomes (Price and Wolfers, 2010), which focuses on coach and artist gender interaction. Specifically, we estimate the following linear probability model: The regression model in the difference-in-differences framework does not capture potential heterogeneous effects across covariates. There could be a positive effect on some groups in the covariates but a negative or no effect on other groups. For example, the coaches in the initial stage of the blind audition could elicit own-gender bias, but in the later stage, opposite-gender bias. The standard regression model will provide a weighted average of effect size, even if an effect varies across a population represented by different groups. One plausible solution is to estimate heterogeneous treatment effects. Instead of just estimating one effect, I estimate a distribution of effect size for a given unit with a given set of attributes and what their impact might be. To analyze this heterogeneous effect of own-gender bias, I incorporated the first difference approach into the causal forest approach. I will discuss the performance of the proposed causal forest approach and how to apply it to my empirical example. The causal forest approach is consistent and asymptotically Gaussian and provides an estimator for their asymptotic variance that allows us to construct valid confidence intervals (Athey and Wager, 2019). Causal forest utilizes a forest-based approach to calculate a similarity weight and then applies the local generalized method of moments to estimate based on a weighted set of neighbors. The authors also show that using the centered instead of the original outcome and treatment allows a forest to focus its splits on the features that affect the treatment effect instead of the primary outcome. Intuitively, a causal forest consists of regression trees (Athey and Imbens, 2016; Breiman et al., 1984), which estimate conditional average treatment effects (CATE) at the leaves of the trees instead of predicting the outcome variables. I utilize a two-step approach by incorporating the first-difference approach into the causal forest approach, the similar two-step approach in the standard differencein-difference method. In the first step, I estimate the first difference, which is the change in the selection of individual contestants from a male coach to a female coach. This step teases out the systematic differences between coaches in female and male coach groups. In the second step, I use the outcome variables obtained from the first step and apply the causal forest approach for heterogeneous treatment-effect analysis. This step teases out the effect of contestant gender trends that exist in both women and men. My methods rely heavily on the causal forest approach, a machine learning method that can estimate heterogeneous causal effect functions under the above assumptions. The main strength associated with the causal forest is that it provides a strategy for learning patterns of heterogeneity from the data. This method requires little researcher discretion compared to traditional subgroup analyses. For instance, one does not have to decide which cutoffs to use to analyze heterogeneity in effects along continuous covariates, nor do we require to define the functional condition of the heterogeneity; the causal forest algorithm is designed to automate this search and find the most appropriate model that best characterizes the heterogeneous effect in the data (Bonander and Svensson, 2021). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Using Causal Forests to Unveil Gender Bias in Hiring

Meet ForestVPN, Startups of the Year Winner in Dubai

Scientific American Supplement, No. 711, August 17, 1889, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . SUCCESSION OF FOREST GROWTHS. here SUCCESSION OF FOREST GROWTHS. The following is from an address delivered by Mr. Robert Douglas before the Association of American Nurserymen at the meeting in Chicago recently. It is the prevailing and almost universal belief that when native forests are destroyed they will be replaced by other kinds, for the simple reason that the soil has been impoverished of the constituents required for the growth of that particular tree or trees. This I believe to be one of the fallacies handed down from past ages, taken for granted, and never questioned. Nowhere does the English oak grow better than where it grew when William the Conqueror found it at the time he invaded Britain. Where do you find white pines growing better than in parts of New England where this tree has grown from time immemorial? Where can you find young redwoods growing more thriftily than among their giant ancestors, nearly or quite as old as the Christian era? The question why the original growth is not reproduced can best be answered by some illustrations. When a pine forest is burned over, both trees and seeds are destroyed, and as the burned trees cannot sprout from the stump like oaks and many other trees, the land is left in a condition well suited for the germination of tree seeds, but there are no seeds to germinate. It is an open field for pioneers to enter, and the seeds which arrive there first have the right of possession. The aspen poplar ( ) has the advantage over all other trees. It is a native of all our northern forests, from the Atlantic to the Pacific. Even fires cannot eradicate it, as it grows in moist as well as dry places, and sprouts from any part of the root. It is a short-lived tree, consequently it seeds when quite young and seeds abundantly; the seeds are light, almost infinitesimal, and are carried on wings of down. Its seeds ripen in spring, and are carried to great distances at the very time when the ground is in the best condition for them. Even on the dry mountain sides in Colorado, the snows are just melting and the ground is moist where they fall. Populus tremuloides To grow this tree from seed would require the greatest skill of the nurseryman, but the burnt land is its paradise. Wherever you see it on high, dry land you may rest assured that a fire has been there. On land slides you will not find its seeds germinating, although they have been deposited there as abundantly as on the burned land. Next to the aspen and poplars comes the canoe birch, and further north the yellow birch, and such other trees as have provision for scattering their seeds. I have seen acorns and nuts germinating in clusters on burned lands in a few instances. They had evidently been buried there by animals and had escaped the fires. I have seen the red cherry ( ) coming up in great quantities where they might never have germinated had not the fires destroyed the debris which covered the seed too deeply. Prunus Pennsylvanica A careful examination around the margin of a burned forest will show the trees of surrounding kinds working in again. Thus by the time the short-lived aspens (and they are very short-lived on high land) have made a covering on the burned land, the surrounding kinds will be found re-established in the new forest, the seeds of the conifers, carried in by the winds, the berries by the birds, the nuts and acorns by the squirrels, the mixture varying more or less from the kinds which grew there before the fire. It is wonderful how far the seeds of berries are carried by birds. The waxwings and cedar birds carry seeds of our tartarean honeysuckles, purple barberries and many other kinds four miles distant, where we see them spring up on the lake shore, where these birds fly in flocks to feed on the juniper berries. It seems to be the same everywhere. I found European mountain ash trees last summer in a forest in New Hampshire; the seed must have been carried over two miles as the crow flies. While this alternation is going on in the East, and may have been going on for thousands of years, the Rocky Mountain district is not so fortunate. When a forest is burned down in that dry region, it is doubtful if coniferous trees will ever grow again, except in some localities specially favored. I have seen localities where short-lived trees were dying out and no others taking their places. Such spots will hereafter take their places above the timber line, which seems to me to be a line governed by circumstances more than by altitude or quality of soil. There are a few exceptions where pines will succeed pines in a burned-down forest. grows up near the timber line in the Rocky Mountains. This tree has persistent cones which adhere to the trees for many years. I have counted the cones of sixteen years on one of these trees, and examined burned forests of this species, where many of the cones had apparently been bedded in the earth as the trees fell. The heat had opened the cones and the seedlings were growing up in myriads; but not a conifer of any other kind could be seen as far as the fire had reached. Pinus Murrayana In the Michigan Peninsula, northern Wisconsin and Minnesota, , a comparatively worthless tree, is replacing the valuable red pine ( ), and in the Sierras and are replacing the more valuable species by the same process. P. Banksiana P. resinosa P. Murrayana P. tuberculata In this case, also, the worthless trees are the shortest lived. So we see that nature is doing all that she can to remedy the evil. Man only is reckless, and especially the American man. The Mexican will cut large limbs off his trees for fuel, but will spare the tree. Even the poor Indian, when at the starvation point, stripping the bark from the yellow pine ( ), for the mucilaginous matter being formed into sap wood, will never take a strip wider than one third the circumference of the tree, so that its growth may not be injured. P. ponderosa We often read that oaks are springing up in destroyed forests where oaks had never grown before. The writers are no doubt sincere, but they are careless. The only pine forests where oaks are not intermixed are either in land so sandy that oaks cannot be made to grow on them at all, or so far north that they are beyond their northern limit. In the Green Mountains and in the New England forests, in the pine forests in Pennsylvania, in the Adirondacks, in Wisconsin and Michigan—except in sand—I have found oaks mixed with the pines and spruces. In northwestern Minnesota and in northern Dakota the oaks are near their northern limit, but even there the burr oak drags on a bare existence among the pines and spruces. In the Black Hills, in Dakota, poor, forlorn, scrubby burr oaks are scattered through the hills among the yellow pines. In Colorado we find them as shrubs among the pines and Douglas spruces. In New Mexico we find them scattered among the piñons. In Arizona they grow like hazel bushes among the yellow pines. On the Sierra Nevada the oak region crosses the pine region, and scattering oaks reach far up into the mountains. Yet oaks will not flourish between the one hundredth meridian and the eastern base of the Sierras, owing to the aridity of the climate. I recently found oaks scattered among the redwoods on both sides of the Coast Range Mountains. Darwin has truly said, "The oaks are driving the pines to the sands." Wherever the oak is established—and we have seen that it is already established whereever it can endure the soil and climate—there it will remain and keep on advancing. The oak produces comparatively few seeds. Where it produces a hundred, the ash and maple will yield a thousand, the elm ten thousand, and many other trees a hundred thousand. The acorn has no provision for protection and transportation like many tree seeds. Many kinds are furnished with wings to float them on the water and carry them in the air. Nearly every tree seed, except the acorn, has a case to protect it while growing, either opening and casting the seeds off to a distance when ripe or falling with them to protect them till they begin to germinate. Even the equally large seeds of other kinds are protected in some way. The hickory nut has a hard shell, which shell itself is protected by a strong covering until ripe. The black walnut has both a hard shell and a fleshy covering. The acorn is the only seed I can think of which is left by nature to take care of itself. It matures without protection, falls heavily and helplessly to the ground, to be eaten and trodden on by animals, yet the few which escape and those which are trodden under are well able to compete in the race for life. While the elm and maple seeds are drying up on the surface, the hickories and the walnuts waiting to be cracked, the acorn is at work with its coat off. It drives its tap root into the earth in spite of grass, and brush, and litter. No matter if it is shaded by forest trees so that the sun cannot penetrate, it will manage to make a short stem and a few leaves the first season, enough to keep life in the root, which will drill in deeper and deeper. When age or accident removes the tree which has overshadowed it, then it will assert itself. Fires may run over the land, destroying almost everything else, the oak will be killed to the ground, but it will throw up a new shoot the next spring, the root will keep enlarging, and when the opportunity arrives it will make a vigorous growth, in proportion to the strength of the root, and throw out strong side roots, and after that care no more for its tap root, which has been its only support, than the frog cares for the tail of the tadpole after it has got on its own legs. There is no mystery about the succession of forest growths, nothing in nature is more plain and simple. We cannot but admire her wisdom, economy, and justness, compensating in another direction for any disadvantage a species may have to labor under. Every kind of tree has an interesting history in itself. Seeds with a hard shell, or with a pulpy or resinous covering which retards their germination, are often saved from becoming extinct by these means. The red cedar ( ) reaches from Florida to and beyond Cape Cod; it is among the hills of Tennessee, through the Middle States and New England. It is scattered through the Western States and Territories, at long distances apart, creeping up the Platte River, in Nebraska. (I found only three in the Black Hills, in Dakota, in an extended search for the different trees which grow there. Found only one in a long ramble in the hills at Las Vegas, New Mexico.) Yet this tree has crept across the continent, and is found here and there in a northwesterly direction between the Platte and the Pacific Coast. It is owing to the resinous coating which protects its seeds that this tree is found to-day scattered over that immense region. Juniperus Virginiana About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2004). Scientific American Supplement, No. 711, August 17, 1889. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/16972/pg16972-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Stories, News & Mentions about FOREST

Categories

Trending Topics

Stories, News & Mentions about FOREST

Forest’s Stories on HackerNoon

Forest's News & Mentions

Categories

Trending Topics