Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Intelligent Surface-Empowered MIMO Systems

Reconfigurable intelligent surface (RIS)-assisted communications appear as a promising candidate for future wireless systems due to its attractive advantages in terms of implementation cost and end-to-end system performance. In this article, two new multiple-input multiple-output (MIMO) system designs using RISs are presented to enhance the performance and boost the spectral efficiency of state-of-the-art MIMO communication systems. Vertical Bell Labs layered space-time (VBLAST) and Alamouti's schemes have been considered in this article and RIS-based simple transceiver architectures are proposed. For the VBLAST-based new system, an RIS is used to enhance the performance of the nulling and canceling-based suboptimal detection procedure as well as to noticeably boost the spectral efficiency by conveying extra bits through the adjustment of the phases of the RIS elements. In addition, RIS elements have been utilized in order to redesign Alamouti's scheme with a single radio frequency signal generator at the transmitter side and to enhance its bit error rate (BER) performance. Monte Carlo simulations are provided to show the effectiveness of our system designs and it has been shown that they outperform the reference schemes in terms of BER performance and spectral efficiency.
Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning

Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD.
Understanding (Non-)Robust Feature Disentanglement and the Relationship Between Low- and High-Dimensional Adversarial Attacks

Recent work has put forth the hypothesis that adversarial vulnerabilities in neural networks are due to them overusing "non-robust features" inherent in the training data. We show empirically that for PGD-attacks, there is a training stage where neural networks start heavily relying on non-robust features to boost natural accuracy. We also propose a mechanism reducing vulnerability to PGD-style attacks consisting of mixing in a certain amount of images contain-ing mostly "robust features" into each training batch, and then show that robust accuracy is improved, while natural accuracy is not substantially hurt. We show that training on "robust features" provides boosts in robust accuracy across various architectures and for different attacks. Finally, we demonstrate empirically that these "robust features" do not induce spatial invariance.
Lightweight Multi-View 3D Pose Estimation Through Camera-Disentangled Representation

We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. Building upon recent advances in interpretable representation learning, we exploit 3D geometry to fuse input images into a unified latent representation of pose, which is disentangled from camera view-points. This allows us to reason effectively about 3D pose across different views without using compute-intensive volumetric grids. Our architecture then conditions the learned representation on camera projection operators to produce accurate per-view 2d detections, that can be simply lifted to 3D via a differentiable Direct Linear Transform (DLT) layer. In order to do it efficiently, we propose a novel implementation of DLT that is orders of magnitude faster on GPU architectures than standard SVD-based triangulation methods. We evaluate our approach on two large-scale human pose datasets (H36M and Total Capture): our method outperforms or performs comparably to the state-of-the-art volumetric methods, while, unlike them, yielding real-time performance.
Arabic Offensive Language on Twitter: Analysis and Experiments

Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization. In this paper, we focus on building a large Arabic offensive tweet dataset. We introduce a method for building a dataset that is not biased by topic, dialect, or target. We produce the largest Arabic dataset to date with special tags for vulgarity and hate speech. We thoroughly analyze the dataset to determine which topics, dialects, and gender are most associated with offensive tweets and how Arabic speakers useoffensive language. Lastly, we conduct many experiments to produce strong results (F1 =83.2) on the dataset using SOTA techniques.
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks

We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved response quality, their complexity also hinders the application of the models in real systems. In this work, we pursue a model that has a simple structure yet can effectively leverage conversation contexts for response generation. To this end, we propose four auxiliary tasks including word order recovery, utterance order recovery, masked word recovery, and masked utterance recovery, and optimize the objectives of these tasks together with maximizing the likelihood of generation. By this means, the auxiliary tasks that relate to context understanding can guide the learning of the generation model to achieve a better local optimum. Empirical studies with three benchmarks indicate that our model can significantly outperform state-of-the-art generation models in terms of response quality on both automatic evaluation and human judgment, and at the same time enjoys a much faster decoding process.
Catalan and Schröder permutations sortable by two restricted stacks

We investigate the permutation sorting problem with two restricted stacks in series by applying a right greedy process. Instead of $\sigma$-machine introduced recently by Cerbai {\it et al.}, we focus our study on $(\sigma,\tau)$-machine where the first stack avoids two patterns of length three, $\sigma$ and $\tau$, and the second stack avoids the pattern $21$. For $(\sigma,\tau)=(123,132$), we prove that sortable permutations are those avoiding some generalized patterns, which provides a new set enumerated by the Catalan numbers. When $(\sigma,\tau) =(132,231)$, we prove that sortable permutations are counted by the Schroder numbers. Finally, we suspect that other pairs of length three patterns lead to sets enumerated by the Catalan numbers and leave them as open problems.
New Insights for Hormone Therapy in Perimenopausal Women Neuroprotection

Perimenopause is a mandatory period in women ’ s life, when the medical staff may initiate hormone therapy with sex steroids for the delay of brain aging and neurodegenerative diseases, during the so-called “ window of opportunity. ” Animals ’ models are helpful to sustain the still controversial results of human clinical observational and/or randomized controlled studies. Estrogens, progesterone, and androgens, with their nuclear and membrane receptors, genes, and epigenetics, with their connections to cholinergic, GABAergic, serotoninergic, and glutamatergic systems are involved in women ’ s normal brain or in brain ’ s pathology. The sex steroids are active through direct and/or indirect mechanisms to modulate and/or to protect brain plasticity, and vessels network, fuel metabolism — glucose, ketones, ATP, to reduce insulin resistance, and inflammation of the aging brain through blood-brain barrier disruption, microglial aberrant activation, and neural cell survival/loss.
Machine Translation Pre-training for Data-to-Text Generation - A Case Study in Czech

While there is a large body of research studying deep learning methods for text generation from structured data, almost all of it focuses purely on English. In this paper, we study the effectiveness of machine translation based pre-training for data-to-text generation in non-English languages. Since the structured data is generally expressed in English, text generation into other languages involves elements of translation, transliteration and copying - elements already encoded in neural machine translation systems. Moreover, since data-to-text corpora are typically small, this task can benefit greatly from pre-training. We conduct experiments on Czech, a morphologically complex language. Results show that machine translation pre-training lets us train endto-end models that significantly improve upon unsupervised pre-training and linguistically informed pipelined neural systems, as judged by automatic metrics and human evaluation. We also show that this approach enjoys several desirable properties, including improved performance in low data scenarios and applicability to low resource languages.
Global well-posedness and blow-up for the damped perturbation of Camassa-Holm type equations

In this paper, we prove global well-posedness of strong solutions to a class of perturbed Camassa-Holm type equations in Besov spaces. It is shown that the existence of global solutions depends only on the $L^1$-integrability of the time-dependent parameters, but not on the shape or smoothness conditions on initial data. As a by-product, we obtain a new global-in-time result for the weakly dissipative Camassa-Holm type equations in Besov spaces, which considerably improves the results in \cite{wu2009global,wuyin2008blow,guo2009some}. Moreover, we derive two kinds of precise blow-up criteria for a certain perturbed Camassa-Holm type equations in Sobolev space, which in some sense tell us how the time-dependent parameters affect the singularity formation of strong solutions.
T-count and Qubit Optimized Quantum Circuit Designs of Carry Lookahead Adder

Quantum circuits of arithmetic operations such as addition are needed to implement quantum algorithms in hardware. Quantum circuits based on Clifford+T gates are used as they can be made tolerant to noise. The tradeoff of gaining fault tolerance from using Clifford+T gates and error correcting codes is the high implementation overhead of the T gate. As a result, the T-count performance measure has become important in quantum circuit design. Due to noise, the risk for errors in a quantum circuit computation increases as the number of gate layers (or depth) in the circuit increases. As a result, low depth circuits such as quantum carry lookahead adders (QCLA)s have caught the attention of researchers. This work presents two QCLA designs each optimized with emphasis on T-count or qubit cost respectively. In-place and out-of-place versions of each design are shown. The proposed QCLAs are compared against the existing works in terms of T-count. The proposed QCLAs for out-of-place addition achieve average T gate savings of $54.34 \%$ and $37.21 \%$, respectively. The proposed QCLAs for in-place addition achieve average T gate savings of $72.11 \%$ and $35.87 \%$
Learning-based Safe Symbolic Abstractions for Nonlinear Control Systems

This paper investigates a novel learning-based approach towards symbolic abstractions for nonlinear control systems. In particular, the symbolic model is constructed based on learning the un-modeled part of the dynamics from training data based on a state-space exploration. Moreover, we aim at achieving safe exploration, meaning that the trajectory of the system is guaranteed to be in a safe region for all times while collecting the training data. In addition, we provide some techniques to reduce the computational load of constructing the symbolic models and the safety controller synthesis, so as to make our approach practical. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.
Star Formation Enhancement in Barred Disk Galaxies in Interacting Galaxy Clusters

A recent study shows that bars can be induced via interaction of galaxy clusters, but it has been unclear if the bar formation by the interaction between clusters is related to the enhancement of star formation. We study galaxies in 105 galaxy clusters at 0.015 < z < 0.060 detected from Sloan Digital Sky Survey data, in order to examine whether the fraction of star-forming galaxies (fsf) in 16 interacting clusters is enhanced compared with that of the other non-interacting clusters and to investigate the possible connection between the fsf enhancement and the bar formation in interacting clusters. We find that fsf is moderately higher (∼20%) in interacting clusters than in non-interacting clusters and that the enhancement of star formation in interacting clusters occurs only in moderate-mass disk-dominated galaxies ( and the bulge-to-total light ratio is ≤0.5). We also find that the enhancement of fsf in moderate-mass disk-dominated galaxies in interacting clusters is mostly due to the increase of the number of barred galaxies. Our result suggests that the cluster–cluster interaction can simultaneously induce bars and star formation in disk galaxies.
Inferring a network from dynamical signals at its nodes

We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.
Improved Pretraining for Domain-specific Contextual Embedding Models

We investigate methods to mitigate catastrophic forgetting during domain-specific pretraining of contextual embedding models such as BERT, DistilBERT, and RoBERTa. Recently proposed domain-specific models such as BioBERT, SciBERT and ClinicalBERT are constructed by continuing the pretraining phase on a domain-specific text corpus. Such pretraining is susceptible to catastrophic forgetting, where the model forgets some of the information learned in the general domain. We propose the use of two continual learning techniques (rehearsal and elastic weight consolidation) to improve domain-specific training. Our results show that models trained by our proposed approaches can better maintain their performance on the general domain tasks, and at the same time, outperform domain-specific baseline models on downstream domain tasks.
Extending the Choquet theory: Trace convexity

We introduce the notion of trace convexity for functions and respectively, for subsets of a compact topological space. This notion generalizes both classical convexity of vector spaces, as well as Choquet convexity for compact metric spaces. We provide new notions of trace-convexification for sets and functions as well as a general version of Krein-Milman theorem. We show that the class of upper semicontinuous convex-trace functions attaining their maximum at exactly one Choquet-boundary point is residual and we obtain several enhanced versions of the maximum principle which generalize both the classical Bauer's theorem as well as its abstract version in the Choquet theory. We illustrate our notions and results with concrete examples of three different types.
Unsupervised Domain Clusters in Pretrained Language Models

The notion of “in-domain data” in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality. In addition, domain labels are many times unavailable, making it challenging to build domain-specific systems. We show that massive pre-trained language models implicitly learn sentence representations that cluster by domains without supervision – suggesting a simple data-driven definition of domains in textual data. We harness this property and propose domain data selection methods based on such models, which require only a small set of in-domain monolingual data. We evaluate our data selection methods for neural machine translation across five diverse domains, where they outperform an established approach as measured by both BLEU and precision and recall with respect to an oracle selection.
Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework

In this paper we cast neural networks defined on graphs as message-passing neural networks (MPNNs) in order to study the distinguishing power of different classes of such models. We are interested in whether certain architectures are able to tell vertices apart based on the feature labels given as input with the graph. We consider two variants of MPNNS: anonymous MPNNs whose message functions depend only on the labels of vertices involved; and degree-aware MPNNs in which message functions can additionally use information regarding the degree of vertices. The former class covers a popular formalisms for computing functions on graphs: graph neural networks (GNN). The latter covers the so-called graph convolutional networks (GCNs), a recently introduced variant of GNNs by Kipf and Welling. We obtain lower and upper bounds on the distinguishing power of MPNNs in terms of the distinguishing power of the Weisfeiler-Lehman (WL) algorithm. Our results imply that (i) the distinguishing power of GCNs is bounded by the WL algorithm, but that they are one step ahead; (ii) the WL algorithm cannot be simulated by "plain vanilla" GCNs but the addition of a trade-off parameter between features of the vertex and those of its neighbours (as proposed by Kipf and Welling themselves) resolves this problem.
A Learning Framework for n-Bit Quantized Neural Networks Toward FPGAs

The quantized neural network (QNN) is an efficient approach for network compression and can be widely used in the implementation of field-programmable gate arrays (FPGAs). This article proposes a novel learning framework for <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-bit QNNs, whose weights are constrained to the power of two. To solve the gradient vanishing problem, we propose a reconstructed gradient function for QNNs in the back-propagation algorithm that can directly get the real gradient rather than estimating an approximate gradient of the expected loss. We also propose a novel QNN structure named <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-BQ-NN, which uses shift operation to replace the multiply operation and is more suitable for the inference on FPGAs. Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs. We also carry out comparable experiments to evaluate our framework. The experimental results show that the quantized models of ResNet, DenseNet, and AlexNet through our learning framework can achieve almost the same accuracies with the original full-precision models. Moreover, when using our learning framework to train our <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-BQ-NN from scratch, it can achieve state-of-the-art results compared with typical low-precision QNNs. Experiments on Xilinx ZCU102 platform show that our <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-BQ-NN with our SVPE can execute 2.9 times faster than that with the vector processing element (VPE) in inference. As the SHIFT operation in our SVPE array will not consume digital signal processing (DSP) resources on FPGAs, the experiments have shown that the use of SVPE array also reduces average energy consumption to 68.7% of the VPE array with 16 bit.
Binary-state dynamics on complex networks: Stochastic pair approximation and beyond

Theoretical approaches to binary-state models on complex networks are generally restricted to infinite size systems, where a set of non-linear deterministic equations is assumed to characterize its dynamics and stationary properties. We develop in this work the stochastic formalism of the different compartmental approaches, these are: approximate master equation (AME), pair approximation (PA) and heterogeneous mean field (HMF), in descending order of accuracy. Using different system-size expansions of a general master equation, we are able to obtain approximate solutions of the fluctuations and finite-size corrections of the global state. On the one hand, far from criticality, the deviations from the deterministic solution are well captured by a Gaussian distribution whose properties we derive, including its correlation matrix and corrections to the average values. On the other hand, close to a critical point there are non-Gaussian statistical features that can be described by the finite-size scaling functions of the models. We show how to obtain the scaling functions departing only from the theory of the different approximations. We apply the techniques for a wide variety of binary-state models in different contexts, such as epidemic, opinion and ferromagnetic models.
Fractional $BV$ solutions for $2\times 2$ systems of conservation laws with a linearly degenerate field.

The class of $2\times 2$ nonlinear hyperbolic systems with one genuinely nonlinear field and one linearly degenerate field are considered. Existence of global weak solutions for small initial data in fractional BV spaces $BV^s$ is proved. The exponent $s$ is related to the usual fractional Sobolev derivative. Riemann invariants $w$ and $z$ corresponding respectively to the genuinely nonlinear component and to the linearly degenerate component play different key roles in this work. We obtain the existence of a global weak solution provided that the initial data written in Riemann coordinates $ (w_0,z_0)$ are small in $ BV^s \times L^\infty $, $1/3 \leq s<1$. The restriction on the exponent $s$ is due to a fundamental result of P.D. Lax, the variation of the Riemann invariant $z$ on the Lax shock curve depends in a cubic way of the variation of the other Riemann invariant $w$.
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy

Stylistic response generation is crucial for building an engaging dialogue system for industrial use. While it has attracted much research interest, existing methods often generate stylistic responses at the cost of the content quality (relevance and fluency). To enable better balance between the content quality and the style, we introduce a new training strategy, know as Information-Guided Reinforcement Learning (IG-RL). In IG-RL, a training model is encouraged to explore stylistic expressions while being constrained to maintain its content quality. This is achieved by adopting reinforcement learning strategy with statistical style information guidance for quality-preserving explorations. Experiments on two datasets show that the proposed approach outperforms several strong baselines in terms of the overall response performance.
Dilworth's Theorem for Borel Posets

A famous theorem of Dilworth asserts that any finite poset of width $k$ can be decomposed into $k$ chains. We study the following problem: given a Borel poset $P$ of finite width $k$, is it true that it can be decomposed into $k$ Borel chains? We give a positive answer in a special case of Borel posets embeddable into the real line. We also prove a dual theorem for posets whose comparability graphs are locally countable.