Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytic Learning of Convolutional Neural Network For Pattern Recognition

Training convolutional neural networks (CNNs) with back-propagation (BP) is time-consuming and resource-intensive particularly in view of the need to visit the dataset multiple times. In contrast, analytic learning attempts to obtain the weights in one epoch. However, existing attempts to analytic learning considered only the multilayer perceptron (MLP). In this article, we propose an analytic convolutional neural network learning (ACnnL). Theoretically we show that ACnnL builds a closed-form solution similar to its MLP counterpart, but differs in their regularization constraints. Consequently, we are able to answer to a certain extent why CNNs usually generalize better than MLPs from the implicit regularization point of view. The ACnnL is validated by conducting classification tasks on several benchmark datasets. It is encouraging that the ACnnL trains CNNs in a significantly fast manner with reasonably close prediction accuracies to those using BP. Moreover, our experiments disclose a unique advantage of ACnnL under the small-sample scenario when training data are scarce or expensive.
On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems

Speaker verification (SV) systems have been widely used in smartphones and Internet of Things devices to identify legitimate users. In recent work, it has been shown that adversarial attacks, such as FAKEBOB, can work effectively against SV systems. The goal of this article is to design a detector that can distinguish an original audio from an audio contaminated by adversarial attacks, if the original audio and the audio before contaminated by the attack have a similar signal-to-noise ratio. Specifically, our designed detector, called MEH-FEST, calculates the minimum energy in an audio signal’s high-frequency band through the short-time Fourier transform and uses it as a detection metric. Through both analysis and experiments, we show that our proposed detector is easy to implement, fast to process an input audio, and effective in determining whether an audio is corrupted by FAKEBOB attacks. The experimental results indicate that the detector is extremely effective: with near zero false-positive and false-negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM) and i-vector SV systems. Moreover, adaptive adversarial attacks against our proposed detector and their countermeasures are discussed and studied, showing the game between attackers and defenders.
Oxidative, antioxidant, selenium status, and consumers acceptability of poultry meat enriched with selenium by dietary supplementation

The effect of selenium supplementation with an organic form (SeMet) and an inorganic form (SeNa) in the poultry finishing diet, on the oxidative and antioxidative status of Pectoralis and Gastrocnemius muscles, refrigerated at 2 °C during 0, 3 and 7 days, was studied. Also, selenium enrichment and sensory evaluation of each muscle was studied in fresh (0 days) meat. At 35 days old, chicks were distributed into three groups (30 birds each), and were fed with a corn-soybean basal diet without Se supplementation (Control) or with 0.30 ppm of selenium as selenomethionine (SeMet) or as sodium selenite (SeNa) until day 56 d-old. In Pectoralis muscle, significantly lower TBARS values were found at day 0 in meat from birds supplemented with both sources of Se (P<0.05). A higher GPx activity and selenium content was observed in meat from birds supplemented with SeMet compared with the Control (P<0.05). In SeMet raw meat from both muscles, selenium is increased and conserved after cooking in Gastrocnemius. The organic source enhanced the antioxidant enzyme activity in fresh Gastrocnemius muscle, and the inorganic source enhanced it in the fresh and in the refrigerated stored Pectoralis muscle (P<0.05). Inorganic selenium in poultry diet increase textural attributes as cohesiveness and chewed need to form a bolus. Perception of an aromatic aftertaste in Gastrocnemius (P<0.05) and Pectoralis (P<0.06) muscle felt as a metallic taste was perceived with SeMet diet. Swallowing ability was increased by SeNa and SeMet sources only in Gastrocnemius. In conclusion, selenium in the poultry diet improved antioxidant status, nutritional value by Se-enriching and textural and taste sensory perception in meat but differences are found depending on the muscle type.
Risk minimisation in construction of upstream tailings storage facilities based on in-situ testing

Tailings storage facilities (TSFs) in Chile are now built using the downstream method of construction, an approach that was triggered by the failure of a number of upstream constructed facilities during or immediately after large seismic events. In Australia, the upstream method continues to be used, because of the significantly lower cost and the perceived lack of a credible seismic risk. The design of TSFs in Australia is moving towards the adoption of maximum credible earthquake (MCE) considerations, particularly for closure, where the design life is increasingly expected to be ‘in perpetuity’. Recent research in Chile has shown the viability of using a lightweight penetrometer, the PANDA penetrometer, as a tool for rapid, inexpensive and regular in-situ determination of the state of deposited tailings. The PANDA has been calibrated against density measurements and is frequently used to estimate the relative density, which is a useful indication of liquefaction susceptibility. This paper describes an approach for managing upstream TSFs in Australia using the PANDA penetrometer for regular in-situ testing which, when coupled with the results of laboratory compressibility measurements, can be used to predict the future state of tailings once buried to a significant depth. RÉSUMÉ : Aujourd’hui les digues de stockage de résidus miniers (DSR) au Chili sont construites par la méthode aval, une approche qui a été déclenchée par la rupture de plusieurs ouvrages contruits par la méthode amont, pendant ou immédiatement après d’importants événements sismiques. En Australie, la méthode amont continue d’être utilisée, du fait de son moindre coût et de la perception de l’absence de risque sismique crédible. La conception des DSR en Australie avance vers l’adoption de considérations d’un tremblement de terre maximum crédible, en particulier dans le cas de fermeture de site minier, pour laquelle la durée de vie de l’ouvrage est considérée être à perpétuité. Des recherches menées au Chili ont montré qu’un pénétromètre léger, le pénétromètre PANDA, peut être utilisé comme outil pour déterminer l’état des résidus déposés dans les digues, de manière rapide, peu coûteuse et régulière. Le PANDA a été étalonné vis-à-vis de mesures de densité et est utilisé fréquemment pour estimer la densité relative, donnant ainsi une indication de la tendance à la liquéfaction. Ce papier décrit une approche de la gestion des DSR en Australie basée sur des essais in situ réguliers à l’aide du pénétromètre PANDA qui, combinés aux résultats de mesures de compressibilité en laboratoire, permettent de prédire l’état atteint par les résidus une fois enfouis à une certaine profondeur.
Necessary and sufficient conditions for identifiability in the admixture model

We consider M SNP data from N individuals who are an admixture of K unknown ancient populations. Let Π si be the frequency of the reference allele of individual i at SNP s . So the number of reference alleles at SNP s for a diploid individual is distributed as binomial(2 , Π si ). We suppose Π si = P Kk =1 F sk Q ki , where F sk is the allele frequency of SNP s in population k and Q ki is the proportion of population k in the ancestry of individual i . I am interested in the identiﬁability of F and Q , up to a relabelling of the ancient populations. Under what conditions, when Π = F 1 Q 1 = F 2 Q 2 are F 1 and F 2 and Q 1 and Q 2 equal? I show that the anchor condition (Cabreros and Storey, 2019) on one matrix together with an independence condition on the other matrix is suﬃcient for identiﬁability. I will argue that the proof of the necessary condition in Cabreros and Storey, 2019 is incorrect, and I will provide a correct proof, which in addition does not require knowledge of the number of ancestral populations. I will also provide abstract necessary and suﬃcient conditions for identiﬁability. I will show that one cannot deviate substantially from the anchor condition without losing identiﬁability. Finally, I show necessary and suﬃcient conditions for identiﬁability for the non-admixed case.
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts

Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture distribution shifts. While valuable, the existing benchmarks are limited in that many of them only contain a small number of shifts and they lack systematic annotation about what is different across different shifts. We present MetaShift--a collection of 12,868 sets of natural images across 410 classes--to address this challenge. We leverage the natural heterogeneity of Visual Genome and its annotations to construct MetaShift. The key construction idea is to cluster images using its metadata, which provides context for each image (e.g."cats with cars"or"cats in bathroom") that represent distinct data distributions. MetaShift has two important benefits: first, it contains orders of magnitude more natural data shifts than previously available. Second, it provides explicit explanations of what is unique about each of its data sets and a distance score that measures the amount of distribution shift between any two of its data sets. We demonstrate the utility of MetaShift in benchmarking several recent proposals for training models to be robust to data shifts. We find that the simple empirical risk minimization performs the best when shifts are moderate and no method had a systematic advantage for large shifts. We also show how MetaShift can help to visualize conflicts between data subsets during model training.
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits

We consider the problem of combining and learning over a set of adversarial bandit algorithms with the goal of adaptively tracking the best one on the fly. The CORRAL algorithm of Agarwal et al. (2017) and its variants (Foster et al., 2020a) achieve this goal with a regret overhead of order $\widetilde{O}(\sqrt{MT})$ where $M$ is the number of base algorithms and $T$ is the time horizon. The polynomial dependence on $M$, however, prevents one from applying these algorithms to many applications where $M$ is poly$(T)$ or even larger. Motivated by this issue, we propose a new recipe to corral a larger band of bandit algorithms whose regret overhead has only \emph{logarithmic} dependence on $M$ as long as some conditions are satisfied. As the main example, we apply our recipe to the problem of adversarial linear bandits over a $d$-dimensional $\ell_p$ unit-ball for $p \in (1,2]$. By corralling a large set of $T$ base algorithms, each starting at a different time step, our final algorithm achieves the first optimal switching regret $\widetilde{O}(\sqrt{d S T})$ when competing against a sequence of comparators with $S$ switches (for some known $S$). We further extend our results to linear bandits over a smooth and strongly convex domain as well as unconstrained linear bandits.
Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event

Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention.
A pointed Prym-Petri Theorem

We construct pointed Prym-Brill-Noether varieties parametrizing line bundles assigned to an irreducible \'etale double covering of a curve with a prescribed minimal vanishing at a fixed point. We realize them as degeneracy loci in type D and deduce their classes in case of expected dimension. Thus, we determine a pointed Prym-Petri map and prove a pointed version of the Prym-Petri theorem implying that the expected dimension holds in the general case. These results build on work of Welters and De Concini-Pragacz on the unpointed case. Finally, we show that Prym varieties are Prym-Tyurin varieties for Prym-Brill-Noether curves of exponent enumerating standard shifted tableaux times a factor of $2$, extending to the Prym setting work of Ortega.
Fold-Transversal Clifford Gates for Quantum Codes

We generalize the concept of folding from surface codes to CSS codes by considering certain dualities within them. In particular, this gives a general method to implement logical operations in suitable LDPC quantum codes using transversal gates and qubit permutations only. To demonstrate our approach, we specifically consider a [[30, 8, 3]] hyperbolic quantum code called Bring's code. Further, we show that by restricting the logical subspace of Bring's code to four qubits, we can obtain the full Clifford group on that subspace.
Exact values for unbalanced Zarankiewicz numbers

For positive integers s, t, m and n, the Zarankiewicz number Zs,t(m,n) is defined to be the maximum number of edges in a bipartite graph with parts of sizes m and n that has no complete biparitite subgraph containing s vertices in the part of size m and t vertices in the part of size n. A simple argument shows that, for each t > 2, Z2,t(m,n) = (t− 1) ( m 2 ) + n when n > (t − 1) ( m 2 ) . Here, for large m, we determine the exact value of Z2,t(m,n) in almost all of the remaining cases where n = Θ(tm2). We establish a new family of upper bounds on Z2,t(m,n) which complement a family already obtained by Roman. We then prove that the floor of the best of these bounds is almost always achieved. We also show that there are cases in which this floor cannot be achieved and others in which determining whether it is achieved is likely a very hard problem. Our results are proved by viewing the problem through the lens of linear hypergraphs and our constructions make use of existing results on edge decompositions of dense graphs.
Detection of a Dense Group of Hypercompact Radio Sources in the Central Parsec of the Galaxy

Using the JVLA, we explored the Galactic center (GC) with a resolution of 0. ″05 at 33.0 and 44.6 GHz. We detected 64 hypercompact radio sources (HCRs) in the central parsec. The dense group of HCRs can be divided into three spectral types: 38 steep-spectrum (α ≤ −0.5) sources, 10 flat-spectrum (−0.5 < α ≤ 0.2) sources, and 17 inverted-spectrum sources having α > 0.2, assuming S ∝ ν α . The steep-spectrum HCRs are likely to represent a population of massive stellar remnants associated with nonthermal compact radio sources powered by neutron stars and stellar black holes. The surface-density distribution of the HCRs as a function of radial distance (R) from Sgr A* can be described as a steep power law Σ(R) ∝ R −Γ, with Γ = 1.6 ± 0.2, along with the presence of a localized order-of-magnitude enhancement in the range 0.1–0.3 pc. The steeper profile of the HCRs relative to that of the central cluster might result from the concentration of massive stellar remnants by mass segregation at the GC. The GC magnetar SGR J1745−2900 belongs to the inverted-spectrum subsample. We find two spectral components present in the averaged radio spectrum of SGR J1745−2900, separated at ν ∼ 30 GHz. The centimeter component is fitted to a power law with α cm = −1.5 ± 0.6. The enhanced millimeter component shows a rising spectrum α mm = 1.1 ± 0.2. Based on the ALMA observations at 225 GHz, we find that the GC magnetar is highly variable on a day-to-day timescale, showing variations up to a factor of 6. Further JVLA and ALMA observations of the variability, spectrum, and polarization of the HCRs are critical for determining whether they are associated with stellar remnants.
Parameterizing the AGN radius -- luminosity relation from the Eigenvector 1 viewpoint

The study of the broad-line region (BLR) using reverberation mapping has allowed us to establish an empirical relation between the size of this line emitting region and the continuum luminosity that drives the line emission (i.e. the RHβ − L5100 relation). To realize its full potential, the intrinsic scatter in the RHβ − L5100 relation needs to be understood better. The mass accretion rate (or equivalently the Eddington ratio) plays a key role in addressing this problem. On the other hand, the Eigenvector 1 schema has helped to reveal an almost clear connection between the Eddington ratio and the strength of the optical Fe II emission that originates from the BLR. This paper aims to reveal the connection between theoretical entities, like, the ionization parameter (U) and cloud mean density (nH) of the BLR, with physical observables obtained directly from the spectra, such as optical Fe II strength (RFeII) that has shown immense potential to trace the accretion rate. We utilize the photoionization code CLOUDY and perform a suite of models to reveal the physical conditions in the low-ionization, dust-free, line emitting BLR. The key here is the focus on the recovery of the equivalent widths (EWs) for the two low-ionization emission lines Hβ and the optical Fe II, in addition to the ratio of their EWs, i.e., RFeII. We compare the spectral energy distributions of prototypical Population A and Population B sources, I ZW 1 and NGC 5548, respectively, in this study. The results from the photoionization modelling are then combined with existing reverberation mapped sources with observed RFeII estimates taken from the literature, allowing us to assess our analytical formulation to tie together the aforementioned quantities. The recovery of the correct physical conditions in the BLR then suggests that the BLR “sees” only a very small fraction (∼1-10%) of the original ionizing continuum.
How Likely A Coalition of Voters Can Influence A Large Election?

For centuries, it has been widely believed that the influence of a small coalition of voters is negligible in a large election. Consequently, there is a large body of literature on characterizing the asymptotic likelihood for an election to be influenced, especially by the manipulation of a single voter, establishing an O( 1 √ n ) upper bound and an Ω( 1 n67 ) lower bound for many commonly studied voting rules under the i.i. d. uniform distribution, known as Impartial Culture (IC) in social choice, where n is the number is voters. In this paper, we extend previous studies in three aspects: (1) we consider a more general and realistic semi-random model, where a distribution adversary chooses a worst-case distribution and then a data adversary modifies up to ψ portion of the data, (2) we consider many coalitional influence problems, including coalitional manipulation, margin of victory, and various vote controls and bribery, and (3) we consider arbitrary and variable coalition size B. Our main theorem provides asymptotically tight bounds on the semi-random likelihood of the existence of a size-B coalition that can successfully influence the election under a wide range of voting rules. Applications of the main theorem and its proof techniques resolve long-standing open questions about the likelihood of coalitional manipulability under IC, by showing that the likelihood is Θ ( min { B √ n , 1 }) for many commonly studied voting rules. The main technical contribution is a characterization of the semi-random likelihood for a Poisson multinomial variable (PMV) to be unstable, which we believe to be a general and useful technique with independent interest. 0 ar X iv :2 20 2. 06 41 1v 2 [ ec on . T H ] 9 N ov 2 02 2
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning

In recent centralized nonconvex distributed learning and federated learning, local methods are one of the promising approaches to reduce communication time. However, existing work has mainly focused on studying first-order optimality guarantees. On the other side, second-order optimality guaranteed algorithms, i.e., algorithms escaping saddle points, have been extensively studied in the non-distributed optimization literature. In this paper, we study a new local algorithm called Bias-Variance Reduced Local Perturbed SGD (BVR-L-PSGD), that combines the existing bias-variance reduced gradient estimator with parameter perturbation to find second-order optimal points in centralized nonconvex distributed optimization. BVR-L-PSGD enjoys second-order optimality with nearly the same communication complexity as the best known one of BVR-L-SGD to find first-order optimality. Particularly, the communication complexity is better than non-local methods when the local datasets heterogeneity is smaller than the smoothness of the local loss. In an extreme case, the communication complexity approaches to $\widetilde \Theta(1)$ when the local datasets heterogeneity goes to zero. Numerical results validate our theoretical findings.
The Impact of Batch Learning in Stochastic Linear Bandits

We consider a special case of bandit problems, named batched bandits, in which an agent observes batches of responses over a certain time period. Unlike previous work, we consider a more practically relevant batch-centric scenario of batch learning. That is to say, we provide a policy-agnostic regret analysis and demonstrate upper and lower bounds for the regret of a candidate policy. Our main theoretical results show that the impact of batch learning is a multiplicative factor of batch size relative to the regret of online behavior. Primarily, we study two settings of the stochastic linear bandits: bandits with finitely and infinitely many arms. While the regret bounds are the same for both settings, the former setting results hold under milder assumptions. Also, we provide a more robust result for the 2-armed bandit problem as an important insight. Finally, we demonstrate the consistency of theoretical results by conducting empirical experiments and reflect on optimal batch size choice.