Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory II: Landscape of the Empirical Risk in Deep Learning

Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least in the case of the most successful Deep Convolutional Neural Networks (DCNNs), practitioners can always increase the network size to fit the training data (an extreme example would be [1]). The most successful DCNNs such as VGG and ResNets are best used with a degree of"overparametrization". In this work, we characterize with a mix of theory and experiments, the landscape of the empirical risk of overparametrized DCNNs. We first prove in the regression framework the existence of a large number of degenerate global minimizers with zero empirical error (modulo inconsistent equations). The argument that relies on the use of Bezout theorem is rigorous when the RELUs are replaced by a polynomial nonlinearity (which empirically works as well). As described in our Theory III [2] paper, the same minimizers are degenerate and thus very likely to be found by SGD that will furthermore select with higher probability the most robust zero-minimizer. We further experimentally explored and visualized the landscape of empirical risk of a DCNN on CIFAR-10 during the entire training process and especially the global minima. Finally, based on our theoretical and experimental results, we propose an intuitive model of the landscape of DCNN's empirical loss surface, which might not be as complicated as people commonly believe.
Exact rate of convergence of the expected W_2 distance between the empirical and true Gaussian distribution

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Exact rate of convergence of the expected W_2 distance between the empirical and true Gaussian distribution Philippe Berthet, Jean Fort
ZEROS IN THE CHARACTER TABLES OF SYMMETRIC GROUPS WITH AN l-CORE INDEX

Let Cn = [χλ(μ)]λ,μ be the character table for Sn, where the indices λ and μ run over the p(n) many integer partitions of n. In this note we study Zl(n), the number of zero entries χλ(μ) in Cn, where λ is an l-core partition of n. For every prime l ≥ 5, we prove an asymptotic formula of the form Zl(n) ∼ αl · σl(n+ δl)p(n) ≫l n l−5 2 e √ , where σl(n) is a twisted Legendre symbol divisor function, δl := (l 2 − 1)/24, and 1/αl > 0 is a normalization of the Dirichlet L-value L (( · l ) , l−1 2 ) . For primes l and n > l/24, we show that χλ(μ) = 0 whenever λ and μ are both l-cores. Furthermore, if Z ∗ l (n) is the number of zero entries indexed by two l-cores, then for l ≥ 5 we obtain the asymptotic Z∗ l (n) ∼ α2l · σl(n+ δl)2 ≫l n.
Statistical dynamics of on-line independent component analysis

The learning dynamics of on-line independent component analysis is analysed in the limit of large data dimension. We study a simple Hebbian learning algorithm that can be used to separate out a small number of non-Gaussian components from a high-dimensional data set. The de-mixing matrix parameters are confined to a Stiefel manifold of tall, orthogonal matrices and we introduce a natural gradient variant of the algorithm which is appropriate to learning on this manifold. For large input dimension the parameter trajectory of both algorithms passes through a sequence of unstable fixed points, each described by a diffusion process in a polynomial potential. Choosing the learning rate too large increases the escape time from each of these fixed points, effectively trapping the learning in a sub-optimal state. In order to avoid these trapping states a very low learning rate must be chosen during the learning transient, resulting in learning time-scales of O(N2) or O(N3) iterations where N is the data dimension. Escape from each sub-optimal state results in a sequence of symmetry breaking events as the algorithm learns each source in turn. This is in marked contrast to the learning dynamics displayed by related on-line learning algorithms for multilayer neural networks and principal component analysis. Although the natural gradient variant of the algorithm has nice asymptotic convergence properties, it has an equivalent transient dynamics to the standard Hebbian algorithm.
The CERT Approach to Cybersecurity Workforce Development

Abstract : For most established organizations, developing and maintaining a competent cybersecurity workforce needs little justification and is, in fact, a central requirement for ensuring resilient operations. As a result, these organizations invest significant resources in attempts to fulfill this requirement. However, most organizations find that the rapid changes and dynamic nature of cybersecurity make keeping their workforce up to date a very challenging problem. This report describes a traditional model commonly used for addressing this challenge, explains some operational limitations associated with that model, and presents a new, continuous approach to cybersecurity workforce development.
Energy Efficient Cooperative Caching in Mobile Ad Hoc Networks

MANETs or mobile ad hoc networks are a form of wireless networks which do not require a base station for providing network connectivity. Mobile ad­hoc networks have many characteristics which distinguish them from other wireless networks. Frequent network disconnection is one among various characteristics, due to which data availability is lower than traditional wired networks. Cooperative caching helps MANETs in alleviating from the situation of non data availability. In this paper we have proposed a cache cooperation strategy named global cluster cooperation (GCC) which is based on clusters. This approach fully exploits the pull mechanism to facilitate cache sharing in a MANET. For implementation we have evaluated the performance of our strategy based on global cooperation scheme with different cooperative caching schemes.
The Complexity of Obstruction-Free Implementations

Obstruction-free implementations of concurrent objects are optimized for the common case where there is no step contention, and were recently advocated as a solution to the costs associated with synchronization without locks. In this paper, we study this claim and this goes through precisely defining the notions of obstruction-freedom and step contention. We consider several classes of obstruction-free implementations, present corresponding generic object implementations, and prove lower bounds on their complexity. Viewed collectively, our results establish that the worstcase operation time complexity of obstruction-free implementations is high, even in the absence of step contention. We also show that lock-based implementations are not subject to some of the time-complexity lower bounds we present.
User Investigative Query Step 1 : Relatedness Classifier ( Gradient Tree Based Model ) ... ... Investigative Query + Entity Information Step 2 : Agreement Classifier ( RNN + Attention Model ) Documents with relatedness scores A B Agree

Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreementaware search framework for investigating rumor news. Given an investigative question,Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement ofMaester over the original FNC winning solution, for agreement-aware search.
Interference of Herbal Medicine with Axitinib in Metastatic Renal Cell Cancer Treatment: A Case Study

Introduction: The awareness and the clinical relevance of the potential interactions between standard and complementary medicine are increasing in medical oncology. Nonetheless, the research and experience of the efficacy, safety, and toxicity of herbal substances are poorly documented. Case Presentation: Here, we report the case of a 68-year-old female patient who had been diagnosed with advanced renal cell cancer with metastasis in the liver and pancreas and had undergone surgical resection with hemi-hepatectomy and resection of metastasis in the pancreas in November 2021. Thereafter, chemotherapy was immediately initiated with three-weekly infusions of pembrolizumab and daily intake of the tyrosine kinase inhibitor axitinib. Surprisingly, 3 months after initiation of systemic treatment, the patient developed early progression and metastasis in the liver, which was then treated with selective internal radiotherapy. Despite continued axitinib and pembrolizumab treatment, a short-term follow-up in November 2022 revealed another metastatic lesion in her pancreas. Due to the presumed lack of response to treatment, the plasma concentration of axitinib was measured and found to demonstrate subtherapeutic levels of exposure. Upon extended anamnesis, the patient reported regular intake of herbal substances prescribed by her oncology acupuncturist for gastrointestinal complaints associated with the primary operation. Conclusion: Further clinical-pharmacological workup strikingly demonstrated a reduction of the therapeutic concentration of axitinib of about 90%, likely caused by herbal drugs such as Dang gui and Bai zhu.
Behaviour of sand–clay mixtures for road pavement subgrade

Abstract Materials forming sand grains and colluvial soil deposits have a distinct structure, consisting of a composite matrix of coarse and fine soil grains. The influence of sand grains content on the behaviour of sand–clay mixtures was investigated by a series of intensive laboratory experiments. The California bearing ratio (CBR), unconfined compression strength (UCS) and compaction tests were carried out on various contents of sand and clay mixtures. The sand–clay mixtures were prepared with sand contents of 0, 10, 20, 30, 40, and 50% by weight. The laboratory tests on these mixtures have indicated that their behaviour will depend on the relative concentration of the sand and clay samples. The results of the tests showed a decrease in the UCS, and an increase the CBR values with an increase in the amount of sand. An increase in dry unit weight and a decrease in respective moisture content by an increase in the amount of sand were observed in the compaction tests.
The biological functions of sphingolipids in plant pathogenic fungi

Sphingolipids are critically significant in a range of biological processes in animals, plants, and fungi. In mammalian cells, they serve as vital components of the plasma membrane (PM) in maintaining its structure, tension, and fluidity. They also play a key role in a wide variety of biological processes, such as intracellular signal transduction, cell polarization, differentiation, and migration. In plants, sphingolipids are important for cell development and for cell response to environmental stresses. In pathogenic fungi, sphingolipids are crucial for the initiation and the development of infection processes afflicting humans. However, our knowledge on the metabolism and function of the sphingolipid metabolic pathway of pathogenic fungi affecting plants is still very limited. In this review, we discuss recent developments on sphingolipid pathways of plant pathogenic fungi, highlighting their uniqueness and similarity with plants and animals. In addition, we discuss recent advances in the research and development of fungal-targeted inhibitors of the sphingolipid pathway, to gain insights on how we can better control the infection process occurring in plants to prevent or/and to treat fungal infections in crops.
Molecular Adsorbent Recirculating System for Acute Liver Failure in a New Pediatric-Based Extracorporeal Liver Support Program

IMPORTANCE: Acute liver failure (ALF) carries significant morbidity and mortality, for both pediatric and adult patients. Albumin dialysis via the molecular adsorbent recirculating system (MARS) is a form of extracorporeal liver support (ELS) that can reduce hepatic encephalopathy (HE), a main driver of mortality in ALF. However, data on MARS and its benefit on mortality have been inconsistent. OBJECTIVES: We sought to report our experiences and patient outcomes from the first 2 years of operation of a new ELS program, within an established pediatric liver transplantation center. DESIGN, SETTING, AND PARTICIPANTS: Retrospective review of outcomes in pediatric and adult patients treated with MARS therapy for ALF, from 2021 to 2022. MAIN OUTCOMES AND MEASURES: Outcomes included reduction in HE and biochemical markers of ALF after MARS therapy, survival, and transplant-free survival. Comparisons were made via Wilcoxon signed-rank test. RESULTS: Five pediatric and two adult patients underwent MARS for ALF. Ages ranged from 2 to 29 years. Overall, 21 MARS runs were performed (median 3 runs per patient, 12.4 hr per run [interquartile range, IQR 10.1–17]). Overall survival was 85.7%, and transplant-free survival was 71.4%. There was a statistically significant reduction in HE score with MARS therapy (median 3 [IQR 3–4] to 1 [IQR 0–1], p = 0.03), and in ALF biomarkers including ammonia (256 µL/dL [195–265] to 75 µL/dL [58–101], p = 0.02), aspartate aminotransferase (6,362 U/L [920–8,305] to 212 U/L [72–431], p = 0.02), alanine aminotransferase (8,362 U/L [3,866–9,189] to 953 U/L [437–1,351], p = 0.02), and international normalized ratio (4.5 [3.3–6.7] to 1.3 [1.2–1.4], p = 0.02). CONCLUSIONS AND RELEVANCE: MARS therapy for ALF was well tolerated by both pediatric and adult patients, and resulted in significant improvement in clinical and biochemical parameters. We demonstrated encouraging overall and transplant-free survival, suggesting that early initiation of MARS with relatively long and frequent cycle times may be of significant benefit to ALF patients, and is worthy of additional study in larger cohorts.
UNSCENTED KALMAN FILTER FOR SPACECRAFT ATTITUDE ESTIMATION USING QUATERNIONS AND EULER ANGLES

A spacecraft attitude estimation approach based on the Unscented Kalman Filter is derived. For nonlinear systems the Unscented Kalman Filter uses a carefully selected set of sample points to map more accurately the probability distribution than the linearization of the standard Extended Kalman Filter, leading to faster convergence from inaccurate initial conditions in attitude estimation problems. The filter formulation is based on standard attitude- vector measurements using a gyro-based model for attitude propagation. This paper compares the performance of a new technique, the Unscented Kalman Filter, when two different mathematical constructs are used to represent the attitude: the Euler angles and quaternions. In this study, the attitude of satellite is estimated with real time algorithms using real data supplied by gyros, Earth sensors and Sun sensors that are on board of the CBERS-2 (China Brazil Earth Resources Satellite).
Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning

Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.
Vascular persistence following precision micropuncture

The success of engineered tissues continues to be limited by time to vascularization and perfusion. Recently, we described a simple microsurgical approach, termed micropuncture (MP), which could be used to rapidly vascularize an adjacently placed scaffold from the recipient macrovasculature. Here we studied the long‐term persistence of the MP‐induced microvasculature.
Metodología de extensión para crear emprendimientos productivos: caso de Isla Venado, Golfo de Nicoya, Costa Rica

Enterprises are one of the four essential components to increase production and generate income in an economy. Through research and outreach projects, universities contribute to the development of social organizations, chambers, and producers’ associations, which results in employment, production, and investment. Outreach, understood as a link between the university and the most vulnerable social sectors, implies creating economic opportunities. In this context, the purpose of this article is to offer a methodology to generate productive projects that may become future micro o small businesses. This methodology is prepared as part of the analysis of the chain production of artisanal fisheries in Isla Venado, Gulf of Nicoya, Costa Rica.
Recent Trends in Foliar Nanofertilizers: A Review

It is estimated that 40–70%, 80–90% and 50–90% of the conventional macronutrients N, P and K applied to the soil are lost, respectively, resulting in considerable loss of resources. Compared to conventional fertilizers, nanofertilizers have the advantages of controlled release, high nutrient utilization, low cost and relatively low environmental pollution due to their small size (1–100 nm) and high specific surface area. The application of nanofertilizers is an up-and-coming field of agricultural research and is an attractive and economical substitute for common fertilizers which can boost global food productivity sustainably. Foliar fertilization is a popular way to satisfy the needs of higher plants. Because of its small application dose, faster nutrient uptake than soil application and relatively less environmental pollution, foliar fertilization is more popular among plants. It can be seen that nanofertilizers and foliar fertilization are the hotspots of attention at present and that current research on the foliar application of nanofertilizers is not as extensive as that on soil application. Based on this background, this paper provides an overview of various applications of foliar spraying of nanofertilizers in agriculture, including applications in improving crop yield and quality as well as mitigating heavy metal stress, salt stress and drought stress.
Instabilities in Stress Corrosion and the Transition to Brittle Failure

Studies on the dissolution of stressed crystals have shown that stress corrosion can lead to roughening of interfaces. The corrosion pattern develops due to gradients in elastic energy along a free crystal surface with an initial roughness. Dissolution will cause further increase in elastic energy, which in turn will speed up dissolution, the so-called Asaro-Tiller-Grinfeld instability. We present a numerical model in order to study the effect of stress corrosion that is observed in physical experiments of interface roughening of brittle salt crystals. In the simulations a salt crystal that is immersed in saturated brine is stressed and dissolution patterns develop. Stress corrosion can lead to an Asaro-Tiller-Grinfeld instability that develops into cusp instabilities and crack-like structures or anti-cracks. The system breaks its symmetry and produces a secondary instability where the number of growing anti-cracks is reduced. This coarsening of the surface roughness is due to stress-shielding effects of growing anti-cracks. Finally, one single anti-crack remains on each side of an initial hole as a "superstructure". The large anti-crack grows at an almost constant speed and eventually leads to brittle failure of the crystal. This mechanism may be important not only for dissolution-precipitation creep but also other phase-transitions and may lead to large scale brittle failure that can be associated with earthquakes.
Pharmacodynamic effects of filgotinib treatment driving clinical improvement in patients with active psoriatic arthritis enrolled in the EQUATOR trial

Objectives The goal of this study was to identify protein and transcriptional biomarkers and pathways associated with baseline disease state, the effect of filgotinib (FIL) treatment on these biomarkers, and to investigate the mechanism of action of FIL on clinical improvement in patients with active psoriatic arthritis (PsA). Methods The phase II EQUATOR (NCT03101670) trial evaluated the efficacy of FIL, a Janus kinase 1-preferential inhibitor, in patients with PsA. Peripheral protein and gene expression levels in association with clinical state at baseline and post-treatment were assessed in 121 patients using linear mixed effects models for repeated measures analyses. Mediation analysis and structural equation modelling (SEM) were performed to investigate the mechanism of action of FIL at week 4 on downstream clinical improvement at week 16. Results Baseline analyses showed that markers of inflammation were significantly associated with multiple PsA clinical metrics, except for Psoriasis Area and Severity Index (PASI), which corresponded to Th17 markers. FIL treatment resulted in sustained transcriptional inhibition of immune genes and pathways, a sustained increase in B-cell fraction and mature B-cells in circulation, and a transient effect on other cell fractions. Mediation analysis revealed that changes in B cells, systemic inflammatory cytokines and neutrophils at week 4 were associated with changes in clinical metrics at week 16. SEM suggested that FIL improved PASI through reduction of IL-23 p19 and IL-12 p40 proteins. Conclusions Our results revealed that FIL treatment rapidly downregulates inflammatory and immune pathways associated with PsA disease activity corresponding to clinical improvement in PsA. Trial registration number NCT03101670.