Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-vanishing normal density in cold holographic superfluids

The low energy and finite temperature excitations of a $d+1$-dimensional system exhibiting superfluidity are well described by a hydrodynamic model with two fluid flows: a normal flow and a superfluid flow. In the vicinity of a quantum critical point, thermodynamics and transport in the system are expected to be controlled by the critical exponents and by the spectrum of irrelevant deformations away from the quantum critical point. Here, using gauge-gravity duality, we present the low temperature dependence of thermodynamic and charge transport coefficients at first order in the hydrodynamic derivative expansion in terms of the critical exponents. Special attention will be paid to the behavior of the charge density of the normal flow in systems with emergent infrared conformal and Lifshitz symmetries, parameterized by a Lifshitz dynamical exponent $z>1$. When $1\leq z 1$) previous results obtained by relativistic effective field theory techniques. Instead, when $z>d+2$, we show that the normal charge density becomes non-vanishing at zero temperature. An extended appendix generalizes these results to systems that violate hyperscaling as well as systems with generalized photon masses. Our results clarify previous work in the holographic literature and have relevance to recent experimental measurements of the superfluid density on cuprate superconductors.
On passivity, feedback passivity, and feedback passivity over erasure network: a piecewise affine approximation approach

In this paper, we deal with the problem of local passivity and local feedback passivation of smooth discrete-time nonlinear systems by considering their piecewise affine approximations. Sufficient conditions are derived for local passivity and local feedback passivity. These results are then extended to systems that operate over Gilbert-Elliott type communication channels. One of the main features of the approach presented in this paper is that it allows us to design the controller by solving a few linear matrix inequalities (LMIs) that are subject to certain constraints. As a special case, results for feedback passivity of piecewise affine systems over a lossy channel are also derived.
LYSTROSAURUS MURRAYI (THERAPSIDA, DICYNODONTIA): BONE HISTOLOGY, GROWTH AND LIFESTYLE ADAPTATIONS

Abstract: Examination of the bone microstructure of Lystrosaurus murrayi from India and South Africa reveals a predominance of fibrolamellar bone tissue, which suggests rapid periosteal osteogenesis and an overall fast growth. Four distinct ontogenetic stages have been identified based on tissue type, organization of the primary osteons, incidence of growth rings, secondary reconstruction and endosteal bone deposition. An indeterminate growth strategy is proposed for Lystrosaurus. Inter‐elemental histovariability suggests differential growth rate of the skeletal elements within the same individual, and among different individuals. The high cortical thickness of the dorsal ribs, an extensive secondary reconstruction in the cortical region of different skeletal elements that resulted in erosionally enlarged channels from the perimedullary to the midcortical region, and trabecular infilling of the medullary region even in the diaphyseal sections of the limb bones suggest at least a semi‐aquatic lifestyle for L. murrayi.
The normalized Laplacian and related indexes of graphs with edges blew up by cliques

In this paper, we introduce the clique-blew up graph $CL(G)$ of a given graph $G$, which is obtained from $G$ by replacing each edge of $G$ with a complete graph $K_n$. We characterize all the normalized Laplacian spectrum of the grpah $CL(G)$ in term of the given graph $G$. Based on the spectrum obtained, the formulae to calculate the multiplicative degree-Kirchhoff index, the Kemeny's constant and the number of spanning trees of $CL(G)$ are derived well. Finally, the spectrum and indexes of the clique-blew up iterative graphs are present.
Plane wave medical ultrasound imaging using adaptive beamforming

In this paper, the adaptive, minimum variance (MV) beam-former is applied to medical ultrasound imaging. The significant resolution and contrast gain provided by the adaptive, minimum variance (MV) beamformer, introduces the possibility of plane wave (PW) ultrasound imaging. Data is obtained using Field II and a 7 MHz, 128-elements, linear array transducer with lambda/2-spacing. MV is compared to the conventional delay-and-sum (DS) beamformer with Boxcar and Hanning weights. Furthermore, the PW images are compared to the a conventional ultrasound image, obtained from a linear scan sequence. The four approaches, {Linear Scan, DS Boxcar, DS Hanning, MV}, have full width at half maximum of {0.82, 0.71, 1.28, 0.12} mm and peak side-lobe levels of {-40.1,-16.8,-34.4,-57.0} dB.
Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework

We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy. In such a case, different modalities of the same video are treated as positives and video clips from a different video are treated as negatives. Because the spatio-temporal information is important for video representation, we extend the negative samples by introducing intra-negative samples, which are transformed from the same anchor video by breaking temporal relations in video clips. With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn video representations. There are many flexible options in our IIC framework and we conduct experiments by using several different configurations. Evaluations are conducted on video retrieval and video recognition tasks using the learned video representation. Our proposed IIC outperforms current state-of-the-art results by a large margin, such as 16.7% and 9.5% points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively. For video recognition, improvements can also be obtained on these two benchmark datasets.
Speculative Dereferencing of Registers: Reviving Foreshadow

Since 2016, multiple microarchitectural attacks have exploited an effect that is attributed to prefetching. These works observe that certain user-space operations can fetch kernel addresses into the cache. Fetching user-inaccessible data into the cache enables KASLR breaks and assists various Meltdown-type attacks, especially Foreshadow. In this paper, we provide a systematic analysis of the root cause of this prefetching effect. While we confirm the empirical results of previous papers, we show that the attribution to a prefetching mechanism is fundamentally incorrect in all previous papers describing or exploiting this effect. In particular, neither the prefetch instruction nor other user-space instructions actually prefetch kernel addresses into the cache, leading to incorrect conclusions and ineffectiveness of proposed defenses. The effect exploited in all of these papers is, in fact, caused by speculative dereferencing of user-space registers in the kernel. Hence, mitigation techniques such as KAISER do not eliminate this leakage as previously believed. Beyond our thorough analysis of these previous works, we also demonstrate new attacks enabled by understanding the root cause, namely an address-translation attack in more restricted contexts, direct leakage of register values in certain scenarios, and the first end-to-end Foreshadow (L1TF) exploit targeting non-L1 data. The latter is effective even with the recommended Foreshadow mitigations enabled and thus revives the Foreshadow attack. We demonstrate that these dereferencing effects exist even on the most recent Intel CPUs with the latest hardware mitigations, and on CPUs previously believed to be unaffected, i.e., ARM, IBM, and AMD CPUs.
Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks

The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'. International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such entries, auditors regularly conduct a sample-based assessment referred to as 'audit sampling'. However, the task of audit sampling is often conducted early in the overall audit process. Often at a stage, in which an auditor might be unaware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the application of Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.
Solving imperfect-information games via exponential counterfactual regret minimization

Two agents' decision-making problems can be modeled as the game with two players, and a Nash equilibrium is the basic solution conception representing good play in games. Counterfactual regret minimization (CFR) is a popular method to solve Nash equilibrium strategy in two-player zero-sum games with imperfect information. The CFR and its variants have successfully addressed many problems in this field. However, the convergence of the CFR methods is not fast, since they solve the strategy by iterative computing. To some extent, this further affects the solution performance. In this paper, we propose a novel CFR based method, exponential counterfactual regret minimization, which also can be called as ECFR. Firstly, we present an exponential reduction technique for regret in the process of the iteration. Secondly, we prove that our method ECFR has a good theoretical guarantee of convergence. Finally, we show that, ECFR can converge faster compared with the prior state-of-the-art CFR based methods in the experiment.
Concentration Bounds for Co-occurrence Matrices of Markov Chains

Co-occurrence statistics for sequential data are common and important data signals in machine learning, which provide rich correlation and clustering information about the underlying object space. We give the first bound on the convergence rate of estimating the co-occurrence matrix of a regular (aperiodic and irreducible) finite Markov chain from a single random trajectory. Our work is motivated by the analysis of a well-known graph learning algorithm DeepWalk by [Qiu et al. WSDM '18], who study the convergence (in probability) of co-occurrence matrix from random walk on undirected graphs in the limit, but left the convergence rate an open problem. We prove a Chernoff-type bound for sums of matrix-valued random variables sampled via an ergodic Markov chain, generalizing the regular undirected graph case studied by [Garg et al. STOC '18]. Using the Chernoff-type bound, we show that given a regular Markov chain with $n$ states and mixing time $\tau$, we need a trajectory of length $O(\tau (\log{(n)}+\log{(\tau)})/\epsilon^2)$ to achieve an estimator of the co-occurrence matrix with error bound $\epsilon$. We conduct several experiments and the experimental results are consistent with the exponentially fast convergence rate from theoretical analysis. Our result gives the first sample complexity analysis in graph representation learning.
Towards Accurate Pixel-wise Object Tracking by Attention Retrieval

The encoding of the target in object tracking moves from the coarse bounding-box to fine-grained segmentation map recently. Revisiting de facto real-time approaches that are capable of predicting mask during tracking, we observed that they usually fork a light branch from the backbone network for segmentation. Although efficient, directly fusing backbone features without considering the negative influence of background clutter tends to introduce false-negative predictions, lagging the segmentation accuracy. To mitigate this problem, we propose an attention retrieval network (ARN) to perform soft spatial constraints on backbone features. We first build a look-up-table (LUT) with the ground-truth mask in the starting frame, and then retrieves the LUT to obtain an attention map for spatial constraints. Moreover, we introduce a multi-resolution multi-stage segmentation network (MMS) to further weaken the influence of background clutter by reusing the predicted mask to filter backbone features. Our approach set a new state-of-the-art on recent pixel-wise object tracking benchmark VOT2020 while running at 40 fps. Notably, the proposed model surpasses SiamMask by 11.7/4.2/5.5 points on VOT2020, DAVIS2016, and DAVIS2017, respectively. We will release our code at this https URL.
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is an enhanced version of Grad-CAM in terms of conservation and sensitivity. It is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM. The code is available at this https URL.
Nephalai: towards LPWAN C-RAN with physical layer compression

We propose Nephelai, a Compressive Sensing-based Cloud Radio Access Network (C-RAN), to reduce the uplink bit rate of the physical layer (PHY) between the gateways and the cloud server for multi-channel LPWANs. Recent research shows that single-channel LPWANs suffer from scalability issues. While multiple channels improve these issues, data transmission is expensive. Furthermore, recent research has shown that jointly decoding raw physical layers that are offloaded by LPWAN gateways in the cloud can improve the signal-to-noise ratio (SNR) of week radio signals. However, when it comes to multiple channels, this approach requires high bandwidth of network infrastructure to transport a large amount of PHY samples from gateways to the cloud server, which results in network congestion and high cost due to Internet data usage. In order to reduce the operation's bandwidth, we propose a novel LPWAN packet acquisition mechanism based on Compressive Sensing with a custom design dictionary that exploits the structure of LPWAN packets, reduces the bit rate of samples on each gateway, and demodulates PHY in the cloud with (joint) sparse approximation. Moreover, we propose an adaptive compression method that takes the Spreading Factor (SF) and SNR into account. Our empirical evaluation shows that up to 93.7% PHY samples can be reduced by Nephelai when SF = 9 and SNR is high without degradation in the packet reception rate (PRR). With four gateways, 1.7x PRR can be achieved with 87.5% PHY samples compressed, which can extend the battery lifetime of embedded IoT devices to 1.7.
Design of a Checklist for Evaluating Language Learning Websites

This paper focuses on the creation of an evaluation tool which provides a thorough assessment of EFL learning websites. After analysing different frameworks for assessing EFL sites, we will define a clear model based on a set of criteria committed to assess EFL learning websites. Finally, we will propose our own evaluation checklist for surveying the nature of such sites comprising some key items for analysis such as multimedia, interactivity, educational content and more especially those related communicative aspects included such as computer mediated communication (CMM).
Effects of culling on badger meles meles spatial organization: implications for the control of bovine tuberculosis

1. The incidence of bovine tuberculosis (TB) in British cattle has risen markedly over the last two decades. Failure to control the disease in cattle has been linked to the persistence of a reservoir of infection in European badgers Meles meles, a nationally protected species. Although badger culling has formed a component of British TB control policy for many years, a recent large-scale randomized field experiment found that TB incidence in cattle was no lower in areas subject to localized badger culling than in nearby areas where no experimental culls occurred. Indeed, analyses indicated that cattle incidence was higher in culled areas. 2. One hypothesis advanced to explain this pattern is that localized culling disrupted badgers' territorial behaviour, potentially increasing the rate of contact between cattle and infected badgers. This study evaluated this hypothesis by investigating badger activity and spatial organization in 13 study areas subjected to different levels of culling. Badger home ranges were mapped by feeding colour-marked baits at badger dens and measuring the geographical area in which colour-marked faeces were retrieved. 3. Badger home ranges were consistently larger in culling areas. Moreover, in areas not subjected to culling, home range sizes increased with proximity to the culling area boundary. Patterns of overlap between home ranges were also influenced by culling. 4. Synthesis and applications. This study demonstrates that culling badgers profoundly alters their spatial organization as well as their population density. These changes have the potential to influence contact rates between cattle and badgers, both where culls occur and on adjoining land. These results may help to explain why localized badger culling appears to have failed to control cattle TB, and should be taken into account in determining what role, if any, badger culling should play in future control strategies.
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions

The performance of speaker verification systems degrades when vocal effort conditions between enrollment and test (e.g., shouted vs. normal speech) are different. This is a potential situation in non-cooperative speaker verification tasks. In this paper, we present a study on different methods for linear compensation of embeddings making use of Gaussian mixture models to cluster shouted and normal speech domains. These compensation techniques are borrowed from the area of robustness for automatic speech recognition and, in this work, we apply them to compensate the mismatch between shouted and normal conditions in speaker verification. Before compensation, shouted condition is automatically detected by means of logistic regression. The process is computationally light and it is performed in the back-end of an x-vector system. Experimental results show that applying the proposed approach in the presence of vocal effort mismatch yields up to 13.8% equal error rate relative improvement with respect to a system that applies neither shouted speech detection nor compensation.
Discussing with a computer to practice a foreign language: research synthesis and conceptual framework of dialogue-based CALL

Abstract This article presents the results of a systematic review of the literature on dialogue-based CALL, resulting in a conceptual framework for research on the matter. Applications allowing a learner to have a conversation in a foreign language with a computer have been studied from various perspectives and under different names (dialogue systems, conversational agents, chatbots…). Considering the fragmentation of what we identify under the term dialogue-based CALL, we attempt to offer a structured overview of these efforts into a conceptual framework. Through a methodical search strategy, we collected a corpus of 343 publications. From this corpus, we formalized an operational definition of dialogue-based CALL, which allowed us to identify 96 relevant systems. Analyzing the type of dialogue they offer, on a continuum of constraints on form and meaning, we propose to classify those systems into four groups. We have called these branching, form-focused, goal-oriented and reactive systems, and we describe their corresponding interactional, instructional and technological traits. We summarize the main results from empirical studies on such systems, distinguishing observational, survey and experimental studies, and discuss the impact of dialogue-based CALL on motivation and L2 development, identifying positive evidence on both outcomes. Finally, we propose two main avenues for future research: relative effectiveness of dialogue-based CALL approaches, and dialogue systems as an environment for testing SLA hypotheses.
Boris computational spintronics—High performance multi-mesh magnetic and spin transport modeling software

This work discusses the design and testing of a new computational spintronics research software. Boris is a comprehensive multi-physics open-source software, combining micromagnetics modelling capabilities with drift-diffusion spin transport modelling and heat flow solver in multi-material structures. A multi-mesh paradigm is employed, allowing modelling of complex multi-layered structures with independent discretization and arbitrary relative positioning between different computational meshes. Implemented micromagnetics models include not only ferromagnetic materials modelling, but also two-sublattice models, allowing simulations of antiferromagnetic and ferrimagnetic materials, fully integrated in the multi-mesh and multi-material design approach. High computational performance is an important design consideration in Boris, and all computational routines can be executed on GPUs, in addition to CPUs. In particular a modified 3D convolution algorithm is used to compute the demagnetizing field on the GPU, termed pipelined convolution, and benchmark comparisons with existing GPU-accelerated software Mumax3 have shown performance improvements up to twice faster.
Evaluation of Pre-Trained Convolutional Neural Network Models for Object Recognition

This paper aims to evaluate the accuracy performance of pre-trained Convolutional Neural Network (CNN) models, namely AlexNet and GoogLeNet accompanied by one custom CNN. AlexNet and GoogLeNet have been proven for their good capabilities as these network models had entered ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and produce relatively good results. The evaluation results in this research are based on the accuracy, loss and time taken of the training and validation processes. The dataset used is Caltech101 by California Institute of Technology (Caltech) that contains 101 object categories. The result reveals that custom CNN architecture produces 91.05% accuracy whereas AlexNet and GoogLeNet achieve similar accuracy which is 99.65%. GoogLeNet consistency arrives at an early training stage and provides minimum error function compared to the other two models.