Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dynamical Analogue of the Criterion of N\'eron-Ogg-Shafarevich

We introduce an anabelian approach to the study of arboreal Galois representations and apply Tamagawa's anabelian version of the N\'eron-Ogg-Shafarevich criterion to produce a dynamical analogue of this criterion: unramified representations correspond to rational maps satisfying a strong form of good reduction in terms of their critical locus. Subsequently, we pursue a dynamical anlaogue of the N\'eron-Ogg-Shafarevich criterion in terms of the more (dynamically) traditional arboreal representations, which relates unramified arboreal representations to a certain separability condition on the dynamical system. Finally, we relate the our criteria: the anabelian criterion corresponds to the dynamical criterion as one varies the base point around the critical locus. Along the way we develop effective criteria to determine which primes are infinitely ramified in arboreal representations over number fields, as well as the asymptotic growth of that ramification; we conclude with examples and applications, especially to dynamical systems over number fields.
Solving the vehicle routing problem with deep reinforcement learning

Recently, the applications of the methodologies of Reinforcement Learning (RL) to NP-Hard Combinatorial optimization problems have become a popular topic. This is essentially due to the nature of the traditional combinatorial algorithms, often based on a trial-and-error process. RL aims at automating this process. At this regard, this paper focuses on the application of RL for the Vehicle Routing Problem (VRP), a famous combinatorial problem that belongs to the class of NP-Hard problems. In this work, first, the problem is modeled as a Markov Decision Process (MDP) and then the PPO method (which belongs to the Actor-Critic class of Reinforcement learning methods) is applied. In a second phase, the neural architecture behind the Actor and Critic has been established, choosing to adopt a neural architecture based on the Convolutional neural networks, both for the Actor and the Critic. This choice resulted in effectively addressing problems of different sizes. Experiments performed on a wide range of instances show that the algorithm has good generalization capabilities and can reach good solutions in a short time. Comparisons between the algorithm proposed and the state-of-the-art solver OR-TOOLS show that the latter still outperforms the Reinforcement learning algorithm. However, there are future research perspectives, that aim to upgrade the current performance of the algorithm proposed.
Dominant Eigenvalue-Eigenvector Pair Estimation via Graph Infection

We present a novel method to estimate the dominant eigenvalue and eigenvector pair of any non-negative real matrix via graph infection. The key idea in our technique lies in approximating the solution to the first-order matrix ordinary differential equation (ODE) with the Euler method. Graphs, which can be weighted, directed, and with loops, are first converted to its adjacency matrix A. Then by a naive infection model for graphs, we establish the corresponding first-order matrix ODE, through which A's dominant eigenvalue is revealed by the fastest growing term. When there are multiple dominant eigenvalues of the same magnitude, the classical power iteration method can fail. In contrast, our method can converge to the dominant eigenvalue even when same-magnitude counterparts exist, be it complex or opposite in sign. We conduct several experiments comparing the convergence between our method and power iteration. Our results show clear advantages over power iteration for tree graphs, bipartite graphs, directed graphs with periods, and Markov chains with spider-traps. To our knowledge, this is the first work that estimates dominant eigenvalue and eigenvector pair from the perspective of a dynamical system and matrix ODE. We believe our method can be adopted as an alternative to power iteration, especially for graphs.
The Many Facets of Trust in AI: Formalizing the Relation Between Trust and Fairness, Accountability, and Transparency

Efforts to promote fairness, accountability, and transparency are assumed to be critical in fostering Trust in AI (TAI), but extant literature is frustratingly vague regarding this 'trust'. The lack of exposition on trust itself suggests that trust is commonly understood, uncomplicated, or even uninteresting. But is it? Our analysis of TAI publications reveals numerous orientations which differ in terms of who is doing the trusting (agent), in what (object), on the basis of what (basis), in order to what (objective), and why (impact). We develop an ontology that encapsulates these key axes of difference to a) illuminate seeming inconsistencies across the literature and b) more effectively manage a dizzying number of TAI considerations. We then reflect this ontology through a corpus of publications exploring fairness, accountability, and transparency to examine the variety of ways that TAI is considered within and between these approaches to promoting trust.
A 23-μW Keyword Spotting IC With Ring-Oscillator-Based Time-Domain Feature Extraction

This article presents the first keyword spotting (KWS) IC that uses a ring-oscillator-based time-domain processing technique for its analog feature extractor (FEx). Its extensive usage of time-encoding schemes allows the analog audio signal to be processed in a fully time-domain manner except for the voltage-to-time conversion stage of the analog front end. Benefiting from fundamental building blocks based on digital logic gates, it offers better technology scalability compared to conventional voltage-domain designs. Fabricated in a 65-nm CMOS process, the prototyped KWS IC occupies 2.03 mm 2 and dissipates 23-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption, including analog FEx and digital neural network classifier. The 16-channel time-domain FEx achieves a 54.89-dB dynamic range for 16-ms frame shift size while consuming 9.3 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. The measurement result verifies that the proposed IC performs a 12-class KWS task on the Google Speech Command dataset (GSCD) with >86% accuracy and 12.4-ms latency.
1D Hyperbolic Systems with Nonlinear Boundary Conditions II: Criteria for Finite Time Stability

We investigate the finite time stability property of one-dimensional nonautonomous initial boundary value problems for linear decoupled hyperbolic systems with nonlinear boundary conditions. We establish sufficient and necessary conditions under which continuous or $L^2$-generalized solutions stabilize to zero in a finite time. Our criteria are expressed in terms of a propagation operator along characteristic curves.
Gradient-descent quantum process tomography by learning Kraus operators

We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems.
Genetic algorithms with Nelder-Mead optimization for the finite elements methods applied on non-linear problems in fluid mechanics

The p-Laplacian operator, or the p-Laplace operator, is a quasilinear elliptic partial differential operator of 2nd order. The p-Laplacian equation is a generalization of the PDE of Laplace Equation and in this paper, we present a way of its solution using Finite Elements. Our method of Finite Elements leads to an Optimization Problem that can be solved by an appropriate combination of Genetic Algorithms and Nelder- Mead. Our method is illustrated by a numerical example. Other methods for the solution of other equations that contain the p-Laplacian operator are also discussed.
Moduli of Representations of Skewed-Gentle Algebras

We prove irreducible components of moduli spaces of semistable representations of skewed-gentle algebras, and more generally, clannish algebras, are isomorphic to products of projective spaces. This is achieved by showing irreducible components of varieties of representations of clannish algebras can be viewed as irreducible components of skewed-gentle algebras, which we show are always normal. The main theorem generalizes an analogous result for moduli of representations of special biserial algebras proven by Carroll-Chindris-Kinser-Weyman.
Studying writer-suggestion interaction: A qualitative study to understand writer interaction with aligned/misaligned next-phrase suggestion

We present an exploratory qualitative study to understand how writers interact with next-phrase suggestions. While there has been some quantitative research on the effects of suggestion systems on writing, there has been little qualitative work to understand how writers interact with suggestion systems and how it affects their writing process—specifically for a non-native but English writer. We conducted a study where amateur writers were asked to write two movie reviews each, one without suggestions and one with. We found writers interact with next-phrase suggestions in various complex ways – writers are able to abstract multiple parts of the suggestions and incorporate them within their writing—even when they disagree with the suggestion as a whole. The suggestion system also had various effects on the writing processes – contributing to different aspects of the writing process in unique ways. We propose a model of writer-suggestion interaction for writing with GPT-2 for a movie review writing task, followed by ways in which the model can be used for future research, along with outlining opportunities for research and design.
PUSH: a primal heuristic based on Feasibility PUmp and SHifting

This work describes PUSH, a primal heuristic combining Feasibility Pump and Shifting. The main idea is to replace the rounding phase of the Feasibility Pump with a suitable adaptation of the Shifting and other rounding heuristics. The algorithm presents different strategies, depending on the nature of the partial rounding obtained. In particular, we distinguish when the partial solution is feasible, infeasible with potential candidates, and infeasible without candidates. We used a threshold to indicate the percentage of variables to round with our algorithm and which other to round to the nearest integer. Most importantly, our algorithm tackles directly equality constraints without duplicating rows. We select the parameters of our algorithm on the 19 instances provided for the Mip Competition 2022. Finally, we compared our approach to other start heuristics, like Simple Rounding, Rounding, Shifting, and Feasibility Pump on the first 800 MIPLIB2017 instances ordered by the number of non-zeros.
Comparison Between Extreme Learning Machine and Fast Learning Network Based on Intrusion Detection System

: Currently, Intrusion Detection System (IDS) design based on machine learning algorithms achieved better results in compare with traditional intrusion detection system. Moreover, Extreme Learning Machine (ELM) and Fast Learning Network (FLN) are represents a popular machine learning Algorithms that had been gives promising results when applied in several fields. This work proposed a comparison between ELM and FLN based on intrusion detection system with different numbers of Neurons to analysis the impact of algorithms architecture based IDS accuracy.
Proteins and lipids deﬁne the diffusional ﬁeld of nitric oxide

Proteins and lipids deﬁne the diffusional ﬁeld of nitric oxide. Am J Physiol Lung Cell Mol Physiol 281: L904–L912, 2001. —Nitric oxide (NO) ﬂuxes released from the surface of individual activated macrophages or cells localized in small aggregates were measured with a novel polarographic self-referencing microsensor. NO ﬂuxes could be detected at distances from the cells of 100–500 m m. The initial ﬂux and the distance from the cells at which NO could be detected were directly related to the number of cells in the immediate vicinity of the probe releas-ing NO. Thus, whereas NO ﬂuxes of ; 1 pmol z cm 2 2 z s 2 1 were measured from individual macrophages, aggregates com-posed of groups of cells varying in number from 18 to 48 cells produced NO ﬂuxes of between ; 4 and 10 pmol z cm 2 2 z s 2 1 . NO ﬂuxes required the presence of L -arginine. Signals were signiﬁcantly reduced by the addition of hemoglobin and by N- nitro- L -arginine methyl ester. NO ﬂuxes were greatest when the sensor was placed immediately adjacent to cell membranes and declined as the distance from the cell increased. The NO signal was markedly reduced in the presence of the protein albumin but not by either oxidized or reduced glutathione. A reduction in the NO signal was also noted after the addition of lipid micelles to the culture medium. These results demonstrate that NO can be detected at signiﬁcant distances from the cell of origin. In addition, both proteins and lipids strongly inﬂuence the net movement of free NO from macrophages. This suggests that these tissue components play an important role in regulating the biological activity of NO. In experiments examining the arginine dependence of the NO signal, cells were cultured for 1–4 h in a modiﬁed Hanks’ balanced salt solution that consisted of (in mM) 118 NaCl, 5.33 KCl, 1.26 CaCl 2 , 0.5 MgCl 2 , 0.4 MgSO 4 , 5.6 glucose, and 25 Na-HEPES; the pH of the solution was adjusted to 7.3 with NaOH.
Metabolically Healthy Obesity Is Not Associated with Food Intake in White or Black Men 1–4

Background: Healthy obese individuals may be protected against adverse health outcomes. Diet and race might inﬂuence healthy obesity, but data on their roles and interactions on the phenotype are limited. Objective: We compared the food intake of metabolically healthy obese men to those of other weight status–metabolic health phenotypes. Methods: Men ( n = 4855) aged $ 45 y with BMI $ 18.5 kg/m 2 and free of cardiovascular diseases, diabetes, and cancer were evaluatedinacross-sectionalstudyoftheREGARDS(REasonsforGeographicAndRacialDifferencesinStroke)studycohort.Food intakewas assessedwiththeuseofa foodfrequency questionnaire. Weightstatus–metabolic healthphenotypesweredeﬁnedby using metabolic syndrome (MetS) and homeostasis model assessment of insulin resistance (HOMA-IR) criteria. Mean differences in food intake among weight status–metabolic health phenotypes were compared with the use of linear regression. Results: MetS-deﬁned healthy obesity was present in 44% of white obese men and 58% of black obese men; the healthy obese phenotype, based on HOMA-IR, was equally prevalent in both white (20%) and black (21%) obese men. Among white men, MetS-deﬁned healthy and unhealthy obesity were associated with lower wholegrain bread intake and higher consumption of red meat ( P < 0.001), whereas HOMA-IR–deﬁned healthy and unhealthy obesity were associated with lower red meat intake ( P < 0.0001) compared with healthy normal weight in multivariable-adjusted analyses that adjusted for sociodemographic, lifestyle, and clinical confounders. However, results were attenuated and became nonsigniﬁcant after further adjustment for BMI. Healthy and unhealthy overweight, deﬁned by both criteria, were associated with lower whole grain bread intake ( P < 0.001) in all models. Among black men, weight status–metabolic health phenotypes were not associated with food intake in all models. Conclusion: Healthy obesity in men is not associated with a healthier diet. Future studies need to consider dietary patterns, which may better inform the holistic effect of diet on healthy obesity, in prospective analyses. J Nutr 2015;145:2551–61. dairy, high-fat dairy, 100% fruit juice, and SSBs) and to identify pairwise mean differences in the weight status–metabolic health phe- notypes (healthy normal weight, unhealthy normal weight, healthy overweight, unhealthy overweight, healthy obese, or unhealthy obese). The SAS procedure PROC GLM was used to ﬁt models (29). Five hierarchical models were ﬁtted: model 1 adjusted for age; model 2 ad- ditionally adjusted for marital status, residential region, education level, income, alcohol intake, multivitamin use, cigarette smoking status, physical activity, television viewing, hs-CRP, and food intake; model 3 further adjusted for BMI; model 4 additionally adjusted for energy intake; and model 5 adjusted for variables in model 3 excluding lifestyle factors (alcohol intake, multivitamin use, cigarette smoking status, physical activity, and television viewing). Data are presented as means 6 SEMs.
Making Logistic Regression A Core Data Mining Tool A Practical Investigation of Accuracy, Speed, and Simplicity.

Binary classiﬁcation is a core data mining task. For large datasets or real-time applications, desirable classiﬁers are accurate, fast, and automatic (i.e. no parameter tuning). Naive Bayes and decision trees are fast and parameter-free, but their accuracy is often below state-of-the-art. Linear support vector machines (SVM) are fast and have good accuracy, but current implementations are sensitive to the capacity parameter. SVMs with radial basis function kernels are accurate but slow, and have multiple parameters that require tuning. In this paper we demonstrate that a very simple parameter-free implementation of logistic regression (LR) is sufﬁciently accurate and fast to compete with state-of-the-art binary classiﬁers on large real-world datasets. The accuracy is comparable to per-dataset tuned linear SVMs and, in higher dimensions, to tuned RBF SVMs. A combination of regularization, truncated-Newton methods, and iteratively re-weighted least squares make this implementation faster than SVMs and relatively insensitive to parameters. Our ﬁtting procedure, TR-IRLS, appears to outperform several common LR ﬁtting procedures in our experiments. TR-IRLS is robust to linear dependencies and scaling problems in the data, and no data preprocessing is necessary. TR-IRLS is easy to implement and can be used anywhere that IRLS is used. Convergence guarantees can be stated for generalized linear models with canonical links.
Mechanism and Effect of the Dilution Gas Flow Rate on Various Fe–Si/SiO 2 Soft Magnetic Composites during Fluidised Bed Chemical Vapour Deposition

: The effect of the dilution gas ﬂow rate on inorganic oxide insulating layers can improve ﬂuidised bed chemical vapour deposition (FBCVD) in Fe–Si/inorganic-oxide soft magnetic composites and obtain excellent magnetic properties. Herein, Fe–Si/SiO 2 composite particles coated via FBCVD and deposited at a 125–350 mL/min Ar-dilution gas ﬂow rate were prepared and sintered into soft magnetic composites. Results demonstrate that SiO 2 deposited on the Fe–Si substrate particle surface changed from submicron SiO 2 clusters (125 mL/min) to an incomplete SiO 2 ﬁlm, then to a complete SiO 2 ﬁlm, and ﬁnally to a porous SiO 2 ﬁlm as the Ar-dilution gas ﬂow rate increased. SiO 2 layers began to transform from the amorphous to the beta-cristobalite state with a hexagonal crystal structure between 1149.45 K and 1280.75 K. However, the SiO 2 amorphous layers’ crystallisation did not affect the Fe–Si substrate particles’ crystal structure. With the increasing Ar-dilution gas ﬂow rate, the saturation magnetisation of Fe–Si/SiO 2 soft magnetic composites initially decreased and then increased. The electrical resistivity increased before 150 mL/min, followed by an increase between 150 and 250 mL/min and then decreased, whereas the total core loss exhibited the opposite trend. These results show that magnetic performance can be promoted by selecting a suitable dilution ﬂow rate.
Recombinant Diabody-Based Immunocapture Enzyme-Linked Immunosorbent Assay for Quantiﬁcation of Rabies Virus Glycoprotein (cid:1)

The potency of rabies vaccines, determined using the NIH mouse protection test, can be directly correlated to the amount of rabies virus glycoprotein (RV GP) present in the vaccine. In an effort to develop a simple and sensitive enzyme-linked immunosorbent assay (ELISA) using recombinant diabody for quantiﬁcation of RV GP, the variable heavy (V H ) and light chain (V L ) domains of an RV GP-speciﬁc human monoclonal antibody (MAb) secreted by a human (cid:1) mouse heterohybridoma (human MAb R16E5) was ampliﬁed, linked using splicing by overlap extension PCR (SOE PCR), and expressed as a recombinant diabody (D06) in the pET28a bacterial expression system. The diabody D06 was puriﬁed by immobilized metal afﬁnity chromatography on a nickel-nitrilotriacetic acid (NTA) agarose column and characterized. The puriﬁed diabody was used in combination with a well-characterized RV GP-speciﬁc mouse MAb, M5B4, to develop an immunocapture ELISA (IC-ELISA) for the quantiﬁcation of RV GP in human rabies vaccine preparations. The maximum detection limit of the IC-ELISA using the M5B4-D06 combination was up to 31.25 ng/ml of RV GP. The speciﬁcity of the diabody was established by its nonreactivity toward other human viral antigens as determined by ELISA and toward RV GP as determined by immunoblot transfer assay and competitive ELISA with the parent human MAb R16E5 and MAb M5B4. The adjusted r 2 value obtained by the regression through the origin model was 0.902, and the equation for predicted potency values for M5B4-D06-based IC-ELISA and MAb M5B4 IC-ELISA were 0.5651 x and 0.8044 x , respectively, where x is the estimate of RV GP from the IC-ELISA in micrograms. Analysis of variance (ANOVA) results showed the estimates of the two methods differed signiﬁcantly ( P < 0.001), while the predicted potencies by the two tests did not differ signiﬁcantly ( P > 0.05). The IC-ELISA can be readily adapted to measure the RV GP content in puriﬁed antigen, and a vaccine can be formulated based on the estimated GP. of rabies glycoprotein human MAb, positive rabies virus nucleoprotein (NP)-speciﬁc a