Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Over the past years, great progress has been made in improving the computing power of general-purpose graphics processing units (GPGPUs), which facilitates the prosperity of deep neural networks (DNNs) in multiple fields like computer vision and natural language processing. A typical DNN training process repeatedly updates tens of millions of parameters, which not only requires huge computing resources but also consumes significant energy. In order to train DNNs in a more energy-efficient way, we empirically investigate the impact of GPU Dynamic Voltage and Frequency Scaling (DVFS) on the energy consumption and performance of deep learning. Our experiments cover a wide range of GPU architectures, DVFS settings, and DNN configurations. We observe that, compared to the default core frequency settings of three tested GPUs, the optimal core frequency can help conserve 8.7%~23.1% energy consumption for different DNN training cases. Regarding the inference, the benefits vary from 19.6%~26.4%. Our findings suggest that GPU DVFS has great potentials to help develop energy efficient DNN training/inference schemes.
Accessing the bath information in open quantum systems with the stochastic 
c
-number Langevin equation method

In traditional open quantum systems, the baths are usually traced out so that only the system information is left in the equations of motion. However, recent studies reveal that using only the system degrees of freedom can be insufficient. In this work, we develop a stochastic c-number Langevin equation method which can conveniently access the bath information. In our method, the studied quantities are the expectation values of operators which can contain both system operators and bath operators. The dynamics of the operators of interest is formally divided into separate system and bath parts, with auxiliary stochastic fields. After solving the independent stochastic dynamics of the system part and the bath part, we can recombine them by taking the average over these stochastic fields to obtain the desired quantities. Several applications of the theory are highlighted, including the pure dephasing model, the spin-boson model, and an optically excited quantum dot coupled to a bath of phonons.
Radial Prediction Layer

For a broad variety of critical applications, it is essential to know how confident a classification prediction is. In this paper, we discuss the drawbacks of softmax to calculate class probabilities and to handle uncertainty in Bayesian neural networks. We introduce a new kind of prediction layer called radial prediction layer (RPL) to overcome these issues. In contrast to the softmax classification, RPL is based on the open-world assumption. Therefore, the class prediction probabilities are much more meaningful to assess the uncertainty concerning the novelty of the input. We show that neural networks with RPLs can be learned in the same way as neural networks using softmax. On a 2D toy data set (spiral data), we demonstrate the fundamental principles and advantages. On the real-world ImageNet data set, we show that the open-world properties are beneficially fulfilled. Additionally, we show that RPLs are less sensible to adversarial attacks on the MNIST data set. Due to its features, we expect RPL to be beneficial in a broad variety of applications, especially in critical environments, such as medicine or autonomous driving.
Constraining the Fraction of Binary Black Holes Formed in Isolation and Young Star Clusters with Gravitational-wave Data

Ten binary black hole mergers have already been detected during the first two observing runs of advanced LIGO and Virgo, and many more are expected to be observed in the near future. This opens the possibility for gravitational-wave (GW) astronomy to better constrain the properties of black hole binaries—not only as single sources, but as a whole astrophysical population. In this paper, we address the problem of using GW measurements to estimate the proportion of merging black holes produced either via isolated binaries or binaries evolving in young star clusters. To this end, we use a Bayesian hierarchical modeling approach applied to catalogs of merging binary black holes generated using state-of-the-art population synthesis and N-body codes. In particular, we show that, although current advanced LIGO/Virgo observations only mildly constrain the mixing fraction between the two formation channels, we expect to narrow down the fractional errors on to 10%–20% after a few hundreds of detections.
The Star Formation History in the Solar Neighborhood as Told by Massive White Dwarfs

White dwarfs (WDs) are the remnants of low- and intermediate-mass stars. Because of electron degeneracy, their evolution is just a simple gravothermal process of cooling. Recently, thanks to Gaia data, it has been possible to construct the luminosity function of massive ( ) WDs in the solar neighborhood ( pc). Because the lifetime of their progenitors is very short, the birth times of both parents and daughters are very close and facilitate the reconstruction of an (effective) star formation rate. This rate started growing from zero during the early Galaxy and reached a maximum 6–7 Gyr ago. It declined and ∼5 Gyr ago started to climb once more, reaching a maximum 2–3 Gyr ago; it has decreased since then. There are some traces of a recent star formation burst, but the method used here is not appropriate for recently born WDs.
Counting Homomorphisms Modulo a Prime Number

Counting problems in general and counting graph homomorphisms in particular have numerous applications in combinatorics, computer science, statistical physics, and elsewhere. One of the most well studied problems in this area is #GraphHom(H) --- the problem of finding the number of homomorphisms from a given graph G to the graph H. Not only the complexity of this basic problem is known, but also of its many variants for digraphs, more general relational structures, graphs with weights, and others. In this paper we consider a modification of #GraphHom(H), the #_p GraphHom(H) problem, p a prime number: Given a graph G, find the number of homomorphisms from G to H modulo p. In a series of papers Faben and Jerrum, and Goebel et al. determined the complexity of #_2 GraphHom(H) in the case H (or, in fact, a certain graph derived from H) is square-free, that is, does not contain a 4-cycle. Also, Goebel et al. found the complexity of #_p GraphHom(H) for an arbitrary prime p when H is a tree. Here we extend the above result to show that the #_p GraphHom(H) problem is #_p P-hard whenever the derived graph associated with H is square-free and is not a star, which completely classifies the complexity of #_p GraphHom(H) for square-free graphs H.
A layered architecture for flexible Web service invocation

Web service composition is emerging as an interesting approach to integrate business applications and create intra‐organizational business processes. Single Web services are combined to create a complex Web service that will realize the process business logic. Once the process is created, it is executed by an orchestration engine that invokes individual Web services in the correct order. However, Web services composing the workflow sometimes become unavailable during the run‐time phase, blocking process execution. This paper describes an architecture that allows the flexible orchestration of business processes. With this approach, Web services composing the process can be automatically substituted with other compatible Web services during process execution. A methodology is defined to evaluate Web service compatibility based on interface matching, in order to select substitutable Web services. Copyright © 2005 John Wiley & Sons, Ltd.
On the Error Rate of the LoRa Modulation With Interference

LoRa is a chirp spread-spectrum modulation developed for the Internet of Things (IoT). In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model, which assumes perfect alignment of the signal of interest and the interference, to the more realistic case where the interfering user is neither chip- nor phase-aligned with the signal of interest and we derive an expression for the error rate. We show that the existing aligned interference model overestimates the effect of interference on the error rate. Moreover, we prove two symmetries in the interfering signal and we derive low-complexity approximate formulas that can significantly reduce the complexity of computing the symbol and frame error rates compared to the complete expression. Finally, we provide numerical simulations to corroborate the theoretical analysis and to verify the accuracy of our proposed approximations.
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference

In this paper, we introduce Hierarchical Invertible Neural Transport (HINT), an algorithm that merges Invertible Neural Networks and optimal transport to sample from a posterior distribution in a Bayesian framework. This method exploits a hierarchical architecture to construct a Knothe-Rosenblatt transport map between an arbitrary density and the joint density of hidden variables and observations. After training the map, samples from the posterior can be immediately recovered for any contingent observation. Any underlying model evaluation can be performed fully offline from training without the need of a model-gradient. Furthermore, no analytical evaluation of the prior is necessary, which makes HINT an ideal candidate for sequential Bayesian inference. We demonstrate the efficacy of HINT on two numerical experiments.
Velocity Saturation Effect on Low Frequency Noise in Short Channel Single Layer Graphene Field Effect Transistors

Graphene devices for analog and radio frequency (RF) applications are prone to low frequency noise (LFN) due to its up conversion to undesired phase noise at higher frequencies. Such applications demand the use of short channel graphene transistors (GFETs) that operate at high electric fields in order to ensure a high speed. Electric field is inversely proportional to device length and proportional to channel potential, so it gets maximized as the drain voltage increases and the transistor’s length shrinks. Under these conditions though, short channel effects like velocity saturation (VS) should be considered. The reduction of LFN data due to the VS effect at short channel GFETs operating at high drain potential is for the first time shown in the present work. Carrier number and mobility fluctuations have been proven to be the main sources that generate LFN in GFETs. While their contribution to the bias dependence of LFN in long channels has been thoroughly investigated, the way in which VS phenomenon aff.
A test for differential ascertainment in case‐control studies with application to child maltreatment

We propose a method to test for the presence of differential ascertainment in case‐control studies, when data are collected by multiple sources. We show that, when differential ascertainment is present, the use of only the observed cases leads to severe bias in the computation of the odds ratio. We can alleviate the effect of such bias using the estimates that our method of testing for differential ascertainment naturally provides. We apply it to a dataset obtained from the National Violent Death Reporting System, with the goal of checking for the presence of differential ascertainment by race in the count of deaths caused by child maltreatment.
On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective

Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing graph-structured data. However, it is known that they do not improve (or sometimes worsen) their predictive performance as we pile up many layers and add non-lineality. To tackle this problem, we investigate the expressive power of graph NNs via their asymptotic behaviors as the layer size tends to infinity. Our strategy is to generalize the forward propagation of a Graph Convolutional Network (GCN), which is a popular graph NN variant, as a specific dynamical system. In the case of a GCN, we show that when its weights satisfy the conditions determined by the spectra of the (augmented) normalized Laplacian, its output exponentially approaches the set of signals that carry information of the connected components and node degrees only for distinguishing nodes. Our theory enables us to relate the expressive power of GCNs with the topological information of the underlying graphs inherent in the graph spectra. To demonstrate this, we characterize the asymptotic behavior of GCNs on the Erdős -- Renyi graph. We show that when the Erdős -- Renyi graph is sufficiently dense and large, a broad range of GCNs on it suffers from the "information loss" in the limit of infinite layers with high probability. Based on the theory, we provide a principled guideline for weight normalization of graph NNs. We experimentally confirm that the proposed weight scaling enhances the predictive performance of GCNs in real data. Code is available at this https URL.
Provably Efficient Imitation Learning from Observation Alone

We study Imitation Learning (IL) from Observations alone (ILFO) in large-scale MDPs. While most IL algorithms rely on an expert to directly provide actions to the learner, in this setting the expert only supplies sequences of observations. We design a new model-free algorithm for ILFO, Forward Adversarial Imitation Learning (FAIL), which learns a sequence of time-dependent policies by minimizing an Integral Probability Metric between the observation distributions of the expert policy and the learner. FAIL is the first provably efficient algorithm in ILFO setting, which learns a near-optimal policy with a number of samples that is polynomial in all relevant parameters but independent of the number of unique observations. The resulting theory extends the domain of provably sample efficient learning algorithms beyond existing results, which typically only consider tabular reinforcement learning settings or settings that require access to a near-optimal reset distribution. We also investigate the extension of FAIL in a model-based setting. Finally we demonstrate the efficacy of FAIL on multiple OpenAI Gym control tasks.
Effect of air velocity on kinetics of thin layer carrot pomace drying

Carrot pomace is a by-product obtained during carrot juice processing. Thin layer carrot pomace drying was performed in a laboratory scale hot air forced convective dryer. The drying experiments were carried out at the air velocity of 0.5, 0.7 and 1.0 m/s at air temperatures from 60 to 75 °C. It was observed that whole drying process of carrot pomace took place in a falling rate period except a very short accelerating period at the beginning. Mathematical models were tested to fit drying data of carrot pomace. The best fit model was observed on the basis of R2, Chi-square and RMSE values. R2 values for all the selected models were above 0.9783. The average values of effective diffusivity ranged from 2.61 × 10−9 to 3.64 × 10−9 m2/s.
Binary Neutron Star (BNS) Merger: What We Learned from Relativistic Ejecta of GW/GRB 170817A

Gravitational Waves (GW) from coalescence of a Binary Neutron Star (BNS) and its accompanying short Gamma-Ray Burst (GRB) GW/GRB 170817A confirmed the presumed origin of these puzzling transients and opened up the way for relating properties of short GRBs to those of their progenitor stars and their surroundings. Here we review an extensive analysis of the prompt gamma-ray and late afterglows of this event. We show that a fraction of polar ejecta from the merger had been accelerated to ultra-relativistic speeds. This structured jet had an initial Lorentz factor of about 260 in our direction, which was O ( 10 ∘ ) from the jet’s axis, and was a few orders of magnitude less dense than in typical short GRBs. At the time of arrival to circum-burst material the ultra-relativistic jet had a close to Gaussian profile and a Lorentz factor ≳ 130 in its core. It had retained in some extent its internal collimation and coherence, but had extended laterally to create mildly relativistic lobes—a cocoon. Its external shocks on the far from center inhomogeneous circum-burst material and low density of colliding shells generated slowly rising afterglows, which peaked more than 100 days after the prompt gamma-ray. The circum-burst material was somehow correlated with the merger. As non-relativistic outflows or tidally ejected material during BNS merger could not have been arrived to the location of the external shocks before the relativistic jet, circum-burst material might have contained recently ejected materials from resumption of internal activities, faulting and mass loss due to deformation and breaking of stars crusts by tidal forces during latest stages of their inspiral but well before their merger. By comparing these findings with the results of relativistic Magneto-Hydro-Dynamics (MHD) simulations and observed gravitational waves we conclude that progenitor neutron stars were most probably old, had close masses and highly reduced magnetic fields.
Time-domain finite element method based on Smoothed Cosine temporal basis function for three-dimensional EM radiation problem

A novel time-domain finite element method (TDFEM) based on smoothed cosine temporal basis function is presented in this paper. This method is motivated by goals of achieving higher computational efficiency. Unlike the reported TDFEM scheme[Z. Lou and J. Jin, 2005], which is based on Galerkin's method with a piecewise linear temporal expansion of electric field, the electric field in our approach is expanded in terms of smoothed cosine basis function. Smoothed cosine function is chosen as temporal basis function for not only a smoother interpolation but also a more robust approximation [A. Taflove, 1995]. Moreover, this scheme allows for a consistent time discretization of the electric field vector wave equation and its augmented PML regions. The novel scheme is first verified on a cavity problem, whose results agree with the analytical solution very well. Finally the scheme is applied to a dipole antenna and the results have good agreement with those from TDFEM based on linear temporal basis function.
Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks

We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay. In our experiments on neural networks for image classification, speech recognition, machine translation, and language modeling, it performs on par or better than well tuned SGD with momentum and Adam or AdamW. Additionally, NovoGrad (1) is robust to the choice of learning rate and weight initialization, (2) works well in a large batch setting, and (3) has two times smaller memory footprint than Adam.
Analysis of environmental air samples by solid-phase microextraction and gas chromatography/ion trap mass spectrometry.

A simple and efficient method for extraction and concentration of volatile organic compounds (VOCs) in air using solid-phase microextraction (SPME) has been developed. The analytes are extracted directly from the air and desorbed into a gas chromatograph for separation and quantification. This method requires minimal sample preparation time and, therefore, is suitable for on-site air monitoring. Analytes can be detected at the part per trillion (ppt) to sub part per billion (ppb) concentration levels with an ion trap mass spectrometer. The precision of the method was determined to be 1.5-6% relative standard deviation (RSD). Temperature and humidity effects were studied experimentally, and the method was compared with an EPA-validated method for selected VOCs. The SPME technique may be suitable for both spot sampling and integral exposure overtime sampling. Integrated sampling has been successfully applied to the identification and quantification of toxic volatile organic compounds in environmental air samples.
Enhanced Virulence of the Escherichia coli O157:H7 Spinach-Associated Outbreak Strain in Two Animal Models Is Associated with Higher Levels of Stx2 Production after Induction with Ciprofloxacin

ABSTRACT Shiga toxin (Stx)-producing Escherichia coli (STEC) causes hemorrhagic colitis and the hemolytic-uremic syndrome (HUS). STEC strains may produce Stx1a and/or Stx2a or variants of either toxin. A 2006 spinach-associated outbreak of STEC O157:H7 resulted in higher hospitalization and HUS rates than previous STEC outbreaks. The spinach isolate, strain K3995, contains both stx 2a and stx 2c. We hypothesized that the enhanced virulence of K3995 reflects the combination of stx 2 alleles (carried on lysogenic phages) and/or the amount of Stx2 made by that strain. We compared the virulence of K3995 to those of other O157:H7 isolates and an isogenic Stx2 mutant in rabbits and mice. We also measured the relative levels of Stx2 produced from those strains with or without induction of the stx-carrying phage. Some rabbits infected with K3995 exhibited intestinal pathology and succumbed to infection, while none of those infected with O157:H7 strain 2812 (Stx1a+ Stx2a+) died or showed pathological signs. Rabbits infected with the isogenic Stx2a mutant K3995 stx 2a::cat were not colonized as well as those infected with K3995 and exhibited no signs of disease. In the streptomycin-treated mouse model, more animals infected with K3995 died than did those infected with O157:H7 strain 86-24 (Stx2a+). Additionally, K3995 produced higher levels of total Stx2 and toxin phage DNA in cultures after phage induction than did 86-24. Our results demonstrate the greater virulence of K3995 compared to other O157:H7 strains in rabbits and mice. We conclude that this enhanced virulence is linked to higher levels of Stx2 expression as a consequence of increased phage induction.
Kernel Conditional Density Operators

We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows performance that is competitive with recent neural conditional density models and Gaussian processes. The proposed model is based on a novel approach to the reconstruction of probability densities from their kernel mean embeddings by drawing connections to estimation of Radon-Nikodym derivatives in the reproducing kernel Hilbert space (RKHS). We prove finite sample bounds for the estimation error in a standard density reconstruction scenario, independent of problem dimensionality. Interestingly, when a kernel is used that is also a probability density, the CDO allows us to both evaluate and sample the output density efficiently. We demonstrate the versatility and performance of the proposed model on both synthetic and real-world data.