Miracle-dz/UniFAR-FTUs huggingface dataset:
Viscoelastic response of impact process on dense suspensions

We numerically study impact processes on dense suspensions using the lattice Boltzmann method to elucidate the connection between the elastic rebound of an impactor and relations among the impact speed $u_0$, maximum force acting on the impactor $F_{\rm max}$, and elapsed time $t_{\rm max}$ to reach $F_{\rm max}$. We find that $t_{\rm max}$ emerges in the early stage of the impact, while the rebound process takes place in the late stage. We find a crossover of $F_{\rm max}$ from $u_0$ independent regime for low $u_0$ to a power law regime satisfying $F_{\rm max}\propto u_0^\alpha$ with $\alpha\approx 1.5$ for high $u_0$. Similarly, $t_{\rm max}$ satisfies $t_{\rm max}\propto u_0^{\beta}$ with $\beta\approx -0.5$ for high $u_0$. Both power-law relations for $F_{\rm max}$ and $t_{\rm max}$ versus $u_0$ for high $u_0$ are independent of the system size, but the rebound phenomenon strongly depends on the depth of the container for suspensions. Thus, we indicate that the rebound phenomenon is not directly related to the relations among $u_0$, $F_{\rm max}$ and $t_{\rm max}$. We propose a floating + force chain model, where the rebound process is caused by an elastic term that is proportional to the number of the connected force chains from the impactor to the bottom plate. On the other hand, there are no elastic contributions in the relations for $F_{\rm max}$ and $t_{\rm max}$ against $u_0$ because of the absence of percolated force chains in the early stage. This phenomenology predicts $F_{\rm max}\propto u_0^{3/2}$ and $t_{\rm max}\propto u_0^{-1/2}$ for high $u_0$ and also recovers the behavior of the impactor quantitatively even if there is the rebound process.
Bayesian Neural Networks: Essentials

Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural networks to support probabilistic deep learning. However, it is nontrivial to understand, design and train Bayesian neural networks due to their complexities. We discuss the essentials of Bayesian neural networks including duality (deep neural networks, probabilistic models), approximate Bayesian inference, Bayesian priors, Bayesian posteriors, and deep variational learning. We use TensorFlow Probability APIs and code examples for illustration. The main problem with Bayesian neural networks is that the architecture of deep neural networks makes it quite redundant, and costly, to account for uncertainty for a large number of successive layers. Hybrid Bayesian neural networks, which use few probabilistic layers judicially positioned in the networks, provide a practical solution.
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models

Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval models, we introduce and formalize the paradigm of deep generative retrieval models defined via the cumulative probabilities of generating query terms. This paradigm offers a grounded probabilistic view on relevance estimation while still enabling the use of modern neural architectures. In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty. We adopt several current neural generative models in our framework and introduce a novel generative ranker T-PGN, which combines the encoding capacity of Transformers with the Pointer Generator Network model. We conduct an extensive set of evaluation experiments on passage retrieval, leveraging the MS MARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking collections. Our results show the significantly higher performance of the T-PGN model when compared with other generative models. Lastly, we demonstrate that exploiting the uncertainty information of deep generative rankers opens new perspectives to query/collection understanding, and significantly improves the cut-off prediction task.
Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD learns only from the HIF signals eliminating the need for presence of diverse non-HIF scenarios in the CAE training. CAE distinguishes HIFs from non-HIF operating conditions by employing cross-correlation. To discriminate HIFs from transient disturbances such as capacitor or load switching, CAE-HIFD uses kurtosis, a statistical measure of the probability distribution shape. The performance evaluation studies conducted using the IEEE 13-node test feeder indicate that the CAE-HIFD reliably detects HIFs, outperforms the state-of-the-art HIF detection techniques, and is robust against noise.
Stochastic modeling of scientific impact

Recent research has found that select scientists have a disproportional share of highly cited papers. Researchers reasoned that this could not have happened if success in science was random and introduced a hidden parameter Q, or talent, to explain this finding. So, the talented high-Q scientists have many high-impact papers. Here I show that an upgrade of an old random citation copying model could also explain this finding. In the new model the probability of citation copying is not the same for all papers but is proportional to the logarithm of the total number of citations to all papers of its author. Numerical simulations of the model give results similar to the empirical findings of the Q-factor article.
Multipliers and unicentral Leibniz algebras

In this paper, we prove Leibniz analogues of results found in Peggy Batten’s 1993 dissertation. We first construct a Hochschild–Serre-type spectral sequence of low dimension, which is used to characterize the multiplier in terms of the second cohomology group with coefficients in the field. The sequence is then extended by a term and a Ganea sequence is constructed for Leibniz algebras. The maps involved with these exact sequences, as well as a characterization of the multiplier, are used to establish criteria for when a central ideal is contained in a certain set seen in the definition of unicentral Leibniz algebras. These criteria are then specialized, and we obtain conditions for when the center of the cover maps onto the center of the algebra.
Assessing Generalization of SGD via Disagreement

We empirically show that the test error of deep networks can be estimated by simply training the same architecture on the same training set but with a different run of Stochastic Gradient Descent (SGD), and measuring the disagreement rate between the two networks on unlabeled test data. This builds on -- and is a stronger version of -- the observation in Nakkiran&Bansal '20, which requires the second run to be on an altogether fresh training set. We further theoretically show that this peculiar phenomenon arises from the \emph{well-calibrated} nature of \emph{ensembles} of SGD-trained models. This finding not only provides a simple empirical measure to directly predict the test error using unlabeled test data, but also establishes a new conceptual connection between generalization and calibration.
Effects of Basic Fertilizer Ratio and Nitrogen Top-Dressing at Jointing Stage on Pasting Properties of Waxy Maize Flour

【Objective】The objective of the experiment is to understand the effects of basic fertilizer ratio and N topdressing at jointing stage on pasting properties of waxy maize flours. 【Method】 The effects of different basic fertilizer ratio(N,P,K was single or mix applied,75 kg.hm-2,respectively) and N topdressing(0,150,and 300 kg.hm-2) application at jointing stage on pasting properties of waxy maize flours were studied using the control varieties in national southern waxy maize regional test,Suyunuo5 and Yunuo7 as materials under split block design. 【Result】The single and interactions of basic fertilizer and N topdressing all affected the pasting characteristics,and the influence of basic fertilizer on all pasting characteristics of waxy maize flours except breakdown(BD) was larger than N topdressing on it.Among different basic fertilizer treatments,the flours presented higher viscosity characteristics except moderate pasting temperature(Ptemp) when N,P and K was balanced applied(each was 75 kg.hm-2). With the increment of N topdressing,peak viscosity(PV),trough viscosity(TV),final viscosity(FV),BD increased first and fall later,setback(SB) gradually increased,while Ptemp was similar among three treatments.Compared with the flours from spring-sown,it from autumn-sown plants presented higher PV,TV,and FV,in addition of lower SB,BD,and Ptemp.The flours from Suyunuo5 had lower PV,BD,and higher TV,FV,SB and Ptempthan from Yunuo7.PV,TV,FV,and BD were observed positively correlated with each other,and SB was observed positively correlated with TV and FV. 【Conclusion】In waxy maize production,plants from autumn sown treatments and balanced application of N,P,and K(each was about 75 kg.hm-2) with moderate N topdressing(about 150 kg.hm-2) at jointing stage should be adopted,which could improve waxy maize viscosity quality.
Language Models are Good Translators

Recent years have witnessed the rapid advance in neural machine translation (NMT), the core of which lies in the encoder-decoder architecture. Inspired by the recent progress of large-scale pre-trained language models on machine translation in a limited scenario, we firstly demonstrate that a single language model (LM4MT) can achieve comparable performance with strong encoder-decoder NMT models on standard machine translation benchmarks, using the same training data and similar amount of model parameters. LM4MT can also easily utilize source-side texts as additional supervision. Though modeling the source- and target-language texts with the same mechanism, LM4MT can provide unified representations for both source and target sentences, which can better transfer knowledge across languages. Extensive experiments on pivot-based and zero-shot translation tasks show that LM4MT can outperform the encoder-decoder NMT model by a large margin.
On the (Un-)Avoidability of Adversarial Examples

The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability. While many deep neural networks have shown impressive performance in terms of predictive accuracy, it has been shown that in many instances an imperceptible perturbation can falsely flip the network's prediction. Most research has then focused on developing defenses against adversarial attacks or learning under a worst-case adversarial loss. In this work, we take a step back and aim to provide a framework for determining whether a model's label change under small perturbation is justified (and when it is not). We carefully argue that adversarial robustness should be defined as a locally adaptive measure complying with the underlying distribution. We then suggest a definition for an adaptive robust loss, derive an empirical version of it, and develop a resulting data-augmentation framework. We prove that our adaptive data-augmentation maintains consistency of 1-nearest neighbor classification under deterministic labels and provide illustrative empirical evaluations.
Perturbed primal-dual dynamics with damping and time scaling coefficients for affine constrained convex optimization problems

We propose a perturbed inertial primal-dual dynamic with damping and scaling coefficients, which involves inertial terms both for primal and dual variables, for a linearly constrained convex optimization problem in a Hilbert setting. With different choices of damping and scaling coefficients, by a Lyapunov analysis approach we discuss the asymptotic properties of the dynamic and prove its fast convergence properties. Our results can be viewed extensions of the existing ones on inertial dynamical systems for the unconstrained convex optimization problem to the linearly constrained convex optimization problem.
Learning to Sample Replacements for ELECTRA Pre-Training

ELECTRA pretrains a discriminator to detect replaced tokens, where the replacements are sampled from a generator trained with masked language modeling. Despite the compelling performance, ELECTRA suffers from the following two issues. First, there is no direct feedback loop from discriminator to generator, which renders replacement sampling inefficient. Second, the generator's prediction tends to be over-confident along with training, making replacements biased to correct tokens. In this paper, we propose two methods to improve replacement sampling for ELECTRA pre-training. Specifically, we augment sampling with a hardness prediction mechanism, so that the generator can encourage the discriminator to learn what it has not acquired. We also prove that efficient sampling reduces the training variance of the discriminator. Moreover, we propose to use a focal loss for the generator in order to relieve oversampling of correct tokens as replacements. Experimental results show that our method improves ELECTRA pre-training on various downstream tasks.
Decomposed Mutual Information Estimation for Contrastive Representation Learning

Recent contrastive representation learning methods rely on estimating mutual information (MI) between multiple views of an underlying context. E.g., we can derive multiple views of a given image by applying data augmentation, or we can split a sequence into views comprising the past and future of some step in the sequence. Contrastive lower bounds on MI are easy to optimize, but have a strong underestimation bias when estimating large amounts of MI. We propose decomposing the full MI estimation problem into a sum of smaller estimation problems by splitting one of the views into progressively more informed subviews and by applying the chain rule on MI between the decomposed views. This expression contains a sum of unconditional and conditional MI terms, each measuring modest chunks of the total MI, which facilitates approximation via contrastive bounds. To maximize the sum, we formulate a contrastive lower bound on the conditional MI which can be approximated efficiently. We refer to our general approach as Decomposed Estimation of Mutual Information (DEMI). We show that DEMI can capture a larger amount of MI than standard non-decomposed contrastive bounds in a synthetic setting, and learns better representations in a vision domain and for dialogue generation.
Improving Human Decisions by Adjusting the Alerting Thresholds for Computer Alerting Tools According to User and Task Characteristics

Objective: To investigate whether performance (number of correct decisions) of humans supported by a computer alerting tool can be improved by tailoring the tool's alerting threshold (sensitivity/specificity combination) according to user ability and task difficulty. Background: Many researchers implicitly assume that for each tool there exists a single ideal threshold. But research shows the effects of alerting tools on decision errors to vary depending on variables such as user ability and task difficulty. These findings motivated our investigation. Method: Forty-seven participants edited text passages, aided by a simulated spell-checker tool. We experimentally manipulated passage difficulty and tool alerting threshold, measured participants' editing and dictation ability, and counted participants' decision errors (false positives + false negatives). We investigated whether alerting threshold, user ability, task difficulty and their interactions affected error count. Results: Which alerting threshold better helped a user depended on an interaction between user ability and passage difficulty. Some effects were large: for higher ability users, a more sensitive threshold reduced errors by 30%, on the easier passages. Participants were not significantly more likely to prefer the alerting threshold with which they performed better. Conclusion: Adjusting alerting thresholds for individual users' ability and task difficulty could substantially improve effects of automated alerts on user decisions. This potential deserves further investigation. Improvement size and rules for adjustment will be application-specific. Application: Computer alerting tools have critical roles in many domains. Designers should assess potential benefits of adjustable alerting thresholds for their specific CAT application. Guidance for choosing thresholds will be essential for realizing these benefits in practice.
CEAZ: Accelerating Parallel I/O via Hardware-Algorithm Co-Design of Efficient and Adaptive Lossy Compression

As supercomputers continue to grow to exa-scale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also helps us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ’s dual-quantization algorithm to our hardware use case. (4) We evaluate CEAZ on five real-world datasets with both a single FPGA board and 128 nodes from Bridges-2 supercomputer. Experiments show that CEAZ outperforms the second-best
The Metal Content of the Hot Atmospheres of Galaxy Groups

Galaxy groups host the majority of matter and more than half of all the galaxies in the Universe. Their hot (107 K), X-ray emitting intra-group medium (IGrM) reveals emission lines typical of many elements synthesized by stars and supernovae. Because their gravitational potentials are shallower than those of rich galaxy clusters, groups are ideal targets for studying, through X-ray observations , feedback effects, which leave important marks on their gas and metal contents. Here, we review the history and present status of the chemical abundances in the IGrM probed by X-ray spectroscopy. We discuss the limitations of our current knowledge, in particular due to uncertainties in the modeling of the Fe-L shell by plasma codes, and coverage of the volume beyond the central region. We further summarize the constraints on the abundance pattern at the group mass scale and the insight it provides to the history of chemical enrichment. Parallel to the observational efforts, we review the progress made by both cosmological hydrodynamical simulations and controlled high-resolution 3D simulations to reproduce the radial distribution of metals in the IGrM, the dependence on system mass from group to cluster scales, and the role of AGN and SN feedback in producing the observed phenomenology. Finally, we highlight future prospects in this field, where progress will be driven both by a much richer sample of X-ray emitting groups identified with eROSITA, and by a revolution in the study of X-ray spectra expected from micro-calorimeters onboard XRISM and ATHENA.
Current and Emerging Ophthalmic Diagnostic Imaging: A Review

A review of current and emerging ophthalmic diagnostic imaging techniques has been presented. Fundamental physical principles behind the imaging techniques are discussed along with their application areas and limitations. The potential for high resolution 3D ophthalmic anatomical and functional diagnostic imaging using ultrawideband (UWB) radar techniques for more accurate diagnosis and monitoring of ophthalmic medical conditions has been explored. Finally, a Micro-Electro-Mechanical Systems (MEMS) based novel UWB radar for non-contact noninvasive non-ionizing 3D real-time diagnostic imaging of eyes has been presented.
Using Echo State Networks to Approximate Value Functions for Control

An Echo State Network (ESN) is a type of single-layer recurrent neural network with randomly-chosen internal weights and a trainable output layer. We prove under mild conditions that a sufficiently large Echo State Network can approximate the value function of a broad class of stochastic and deterministic control problems. Such control problems are generally non-Markovian. We describe how the ESN can form the basis for novel and computationally efficient reinforcement learning algorithms in a non-Markovian framework. We demonstrate this theory with two examples. In the first, we use an ESN to solve a deterministic, partially observed, control problem which is a simple game we call `Bee World'. In the second example, we consider a stochastic control problem inspired by a market making problem in mathematical finance. In both cases we can compare the dynamics of the algorithms with analytic solutions to show that even after only a single reinforcement policy iteration the algorithms arrive at a good policy.
Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems

Adversarial examples are designed to fool the speaker recognition (SR) system by adding a carefully crafted human-imperceptible noise to the speech signals. Posing a severe security threat to state-of-the-art SR systems, it becomes vital to deep-dive and study their vulnerabilities. Moreover, it is of greater importance to propose countermeasures that can protect the systems against these attacks. Addressing these concerns, we first investigated how state-of-the-art x-vector based SR systems are affected by white-box adversarial attacks, i.e., when the adversary has full knowledge of the system. x-Vector based SR systems are evaluated against white-box adversarial attacks common in the literature like fast gradient sign method (FGSM), basic iterative method (BIM)–a.k. a. iterative-FGSM–, projected gradient descent (PGD), and Carlini-Wagner (CW) attack. To mitigate against these attacks, we investigated four pre-processing defenses which do not need adversarial examples during training. The four pre-processing defenses–viz. randomized smoothing, DefenseGAN, variational autoencoder (VAE), and Parallel WaveGAN vocoder (PWG) are compared against the baseline defense of adversarial training. Performing powerful adaptive white-box adversarial attack (i.e., when the adversary has full knowledge of the system, including the defense), our conclusions indicate that SR systems were extremely vulnerable under BIM, PGD, and CW attacks. Among the proposed pre-processing defenses, PWG combined with randomized smoothing offers the most protection against the attacks, with accuracy averaging 93% compared to 52% in the undefended system and an absolute improvement >90% for BIM attacks with $L_\infty >0.001$ and CW attack.