Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrastive Learning for Cold-Start Recommendation

Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information of items (e.g. content features) into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance on cold-start recommendation. In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet efficient Contrastive Learning-based Cold-start Recommendation framework (CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules. It allows us to preserve collaborative signals in the content representations for both warm and cold-start items. Through extensive experiments on four publicly accessible datasets, we observe that CLCRec achieves significant improvements over state-of-the-art approaches in both warm- and cold-start scenarios.
Theory of time-domain Brillouin scattering for probe light and acoustic beams propagating at an arbitrary relative angle: Application to acousto-optic interaction near material interfaces

A simple theory is developed for an interpretation of the time-domain Brillouin scattering experiments where the coherent acoustic pulse and the probe light pulse beams are propagating at an angle to each other. The directivity pattern of their acousto-optic interaction in case of heterodyne detection of the acoustically scattered probe light (in nearly backward direction to the probe light) is predicted. The theory reveals the dependences of carrier frequency and duration of acoustically induced “wave packets” in the transient reflectivity signals, on the widths of light and sound beams, and on the angle of their relative propagation (interaction angle). It also describes the transient dynamics of these wave packets when the probe light and the coherent acoustic pulses are incident on material interfaces (inter-grain boundaries) and Brillouin scattering by incident acoustic field is transformed into Brillouin scattering by the reflected and transmitted (refracted) acoustic fields. In general, these transformations are accompanied by the modifications of the interaction angles between the coherent acoustic pulses and probe light beams. The sensitivities of the carrier frequencies and wave packet amplitudes in the reflected/transmitted beams to the angle of the beams incidence on the interface are evaluated and compared. The theory confirms the expected possibility of strong and dominant reduction in the time-domain Brillouin scattering amplitude following the reflection/transmission processes for large interaction angles.
Spatial and Temporal Networks for Facial Expression Recognition in the Wild Videos

The paper describes our proposed methodology for the seven basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2021. In this task, facial expression recognition (FER) methods aim to classify the correct expression category from a diverse background, but there are several challenges. First, to adapt the model to in-the-wild scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose an ensemble model with a convolution neural network (CNN), a CNN-recurrent neural network (CNN-RNN), and a CNN-Transformer (CNN-Transformer), to incorporate both spatial and temporal information. Our ensemble model achieved F1 as 0.4133, accuracy as 0.6216 and final metric as 0.4821 on the validation set.
Feature-based Event Stereo Visual Odometry

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by feature reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on both MVSEC and DSEC sequences. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization.
Data from: Improving SABRE hyperpolarization with highly non-intuitive pulse sequences: moving beyond avoided crossings to describe dynamics

Signal Amplification By Reversible Exchange (SABRE) creates “hyperpolarization” (large spin magnetization) using a transition metal catalyst and parahydrogen, addressing the sensitivity limitations of magnetic resonance. SABRE and its heteronuclear variant X-SABRE are simple, fast and general, but to date have not produced polarization levels as large as more established methods. We show here that inaccuracies in the theoretical framework for these applications, which focuses on avoided crossings (also called level anticrossings or LACs) steer current SABRE and X-SABRE experiments away from optimal solutions. Accurate simulations show astonishingly rich and unexpected dynamics in SABRE/X-SABRE, which we explain with a combination of perturbation theory and average Hamiltonian approaches. This theoretical picture predicts simple pulse sequences with field values far from LACs (both instantaneously and on average), using different terms in the effective Hamiltonian to strategically control evolution and improve polarization transfer. Significant signal enhancements under such highly non-intuitive conditions are verified experimentally. Introduction Nuclear magnetic resonance techniques have wide applications in chemistry, physics, materials science, and medicine. Unfortunately, as the interaction of nuclei with an external magnetic field only induces small energy differences between spin states, the sensitivity of these techniques is fundamentally limited. Hyperpolarization methods artificially alter spin populations to induce polarizations far beyond thermal equilibrium by transferring polarization from a source of spin order to the target nucleus. Techniques such as dissolution Dynamic Nuclear Polarization (DNP)(1) and Spin Exchange Optical Pumping (SEOP)(2) have been in use for decades and have matured to the point of clinical applicability, but are limited by expense and need for specialized personnel to operate equipment. An alternative method, originally introduced as Signal Amplification By Reversible Exchange (SABRE)(3, 4), enables large signal enhancements in many different molecules at a small fraction of the cost of other techniques. SABRE derives spin order from parahydrogen, the singlet state (1 √2 ⁄ (|αααα⟩ − |αααα⟩)) of the hydrogen molecule, using reversible interactions between a polarization transfer catalyst (PTC), parahydrogen, and a target ligand (Figure 1). The parahydrogen creates a singlet hydride pair bound reversibly to the Ir coordination center (green), typically exchanging on and off with rates kkHH ≈ 1 − 5 ss−1. Target ligands, such as pyridine in this example system, also Figure 1. Exchange interactions in SABRE with an Ir(IMes) catalyst. Parahydrogen and target ligand exchange at the equatorial sites to form transient spin coupling networks for polarization transfer. The example system shown here is Ir(H)2(IMes)(N-pyr)3
Computer Vision-assisted Decimeter-level Single-antenna RSSI Localization Harnessing Dynamic Blockage Events

This paper demonstrates the feasibility of received power strength indicator (RSSI)-based single-antenna localization (R-SAL) with decimeter-level localization accuracy. To achieve decimeter-level accuracy, either fine-grained radio frequency (RF) information (e.g., channel state information) or coarse-grained RF information (e.g., RSSI) from more than multiple antennas is required. Meanwhile, owing to deficiency of single-antenna RSSI which only indicates a distance between a receiver and a transmitter, realizing fine-grained localization accuracy with single coarse-grained RF information is challenging. Our key idea to address this challenge is to leverage computer vision (CV) and to estimate the most likely Fresnel zone between the receiver and transmitter, where the role of RSSI is to detect blockage timings. Specifically, historical positions of an obstacle that dynamically blocks the Fresnel zone are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the Fresnel zone boundaries, enabling the estimation of the Fresnel zone and localization of the transmitter. The experimental evaluation revealed that the proposed R-SAL achieved decimeter-level localization in an indoor environment, which is comparable to that of a simple previous RSSI-based localization with three receivers.
Incremental Vulnerability Detection via Back-Propagating Symbolic Execution of Insecurity Separation Logic

We present the first compositional, incremental static analysis for detecting memory-safety and information leakage vulnerabilities in C-like programs. To do so, we develop the first under-approximate relational program logics, including Insecurity Separation Logic (InsecSL). We show how InsecSL can be automated via backpropagating symbolic execution (BPSE) to build a bottom-up, inter-procedural and incremental analysis for detecting vulnerabilities. We prove our approach sound in Isabelle/HOL and implement it in a proof-of-concept tool, Underflow, for analysing C programs, which we apply to various case studies.
Raychadhuri equations and gravitational collapse in Einstein–Cartan theory

Sudipta Hensh ∗ and Stefano Liberati 3, 4, † Research Centre for Theoretical Physics and Astrophysics, Institute of Physics, Silesian University in Opava, Bezručovo náměst́ı 13, CZ-74601 Opava, Czech Republic SISSA International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy IFPU Institute for Fundamental Physics of the Universe, Via Beirut 2, 34014 Trieste, Italy INFN Sezione di Trieste, Via Valerio 2, 34127 Trieste, Italy (Dated: July 13, 2021)
Algorithmic Causal Effect Identification with causaleffect

Our evolution as a species made a huge step forward when we understood the relationships between causes and effects. These associations may be trivial for some events, but they are not in complex scenarios. To rigorously prove that some occurrences are caused by others, causal theory and causal inference were formalized, introducing the $do$-operator and its associated rules. The main goal of this report is to review and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data. To this end, we first present some basic background knowledge on probability and graph theory, before introducing important results on causal theory, used in the construction of the algorithms. We then thoroughly study the identification algorithms presented by Shpitser and Pearl in 2006, explaining our implementation in Python alongside. The main identification algorithm can be seen as a repeated application of the rules of $do$-calculus, and it eventually either returns an expression for the causal query from experimental probabilities or fails to identify the causal effect, in which case the effect is non-identifiable. We introduce our newly developed Python library and give some usage examples.
A strongly universal cellular automaton with four states

In this paper, we prove that there is a strongly universal cellular automaton in the dodecagrid, the tesselllation {5,3,4} of the hyperbolic 3D space, with four states but, it is not rotation invariant as the automaton of arXiv:2104.01561 is with five states. The present paper is not an improvement of arXiv:2104.01561. The reduction to four states of the automaton of the present paper results from a relaxation of the condition of rotation invariance. However, the relaxation is not complete: the rules are invariant with respect to rotations of the dodecahedron which leave it globally invariant which also leave invariant a couple of opposite faces of the dodecahedron. As there are twenty five such rotations, it can be considered that the reduction to four states is significant although the relaxation it implied.
A Projector-Camera System Using Hybrid Pixels with Projection and Capturing Capabilities

We propose a novel projector-camera system (ProCams) in which each pixel has both projection and capturing capabilities. Our proposed ProCams solves the difficulty of obtaining precise pixel correspondence between the projector and the camera. We implemented a proof-of-concept ProCams prototype and demonstrated its applicability to a dynamic projection mapping.
Multiplayer Homicidal Chauffeur Reach-Avoid Games via Guaranteed Winning Strategies

This paper studies a planar multiplayer Homicidal Chauffeur reach-avoid differential game, where each pursuer is a Dubins car and each evader has simple motion. The pursuers aim to protect a goal region cooperatively from the evaders. Due to the high-dimensional strategy space among pursuers, we decompose the whole game into multiple one-pursuer-one-evader subgames, each of which is solved in an analytical approach instead of solving Hamilton-Jacobi-Isaacs equations. For each subgame, an evasion region (ER) is introduced, based on which a pursuit strategy guaranteeing the winning of a simple-motion pursuer under specific conditions is proposed. Motivated by the simple-motion pursuer, a strategy for a Dubins-car pursuer is proposed when the pursuer-evader configuration satisfies separation condition (SC) and interception orientation (IO). The necessary and sufficient condition on capture radius, minimum turning radius and speed ratio to guarantee the pursuit winning is derived. When the IO is not satisfied (Non-IO), a heading adjustment pursuit strategy is proposed, and the condition to achieve IO within a finite time, is given. Then, a two-step pursuit strategy is proposed for the SC and Non-IO case. A non-convex optimization problem is introduced to give a condition guaranteeing the winning of the pursuer. A polynomial equation gives a lower bound of the non-convex problem, providing a sufficient and efficient pursuit winning condition. Finally, these pairwise outcomes are collected for the pursuer-evader matching. Simulations are provided to illustrate the theoretical results.
Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules

The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be improved either by increasing the number of training points or by improving the models. In order to build accurate models based on expensive ab initio calculations, much of recent work has focused on the latter. In particular, it has been shown that gradient domain machine learning (GDML) models produce accurate results for high-dimensional molecular systems with a small number of ab initio calculations. The present work extends GDML to models with composite kernels built to maximize inference from a small number of molecular geometries. We illustrate that GDML models can be improved by increasing the complexity of underlying kernels through a greedy search algorithm using Bayesian information criterion as the model selection metric. We show that this requires including anisotropy into kernel functions and produces models with significantly smaller generalization errors. The results are presented for ethanol, uracil, malonaldehyde and aspirin. For aspirin, the model with composite kernels trained by forces at 1000 randomly sampled molecular geometries produces a global 57-dimensional PES with the mean absolute accuracy 0.177 kcal mol−1 (61.9 cm−1) and FFs with the mean absolute error 0.457 kcal mol−1 Å−1.
Stateful Detection of Model Extraction Attacks

Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers. Recent work has shown that attackers can exploit these APIs to extract good approximations of such ML models, by querying them with samples of their choosing. We propose VarDetect, a stateful monitor that tracks the distribution of queries made by users of such a service, to detect model extraction attacks. Harnessing the latent distributions learned by a modified variational autoencoder, VarDetect robustly separates three types of attacker samples from benign samples, and successfully raises an alarm for each. Further, with VarDetect deployed as an automated defense mechanism, the extracted substitute models are found to exhibit poor performance and transferability, as intended. Finally, we demonstrate that even adaptive attackers with prior knowledge of the deployment of VarDetect, are detected by it.
Secure Dual-Functional Radar-Communication Transmission: Exploiting Interference for Resilience Against Target Eavesdropping

We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously. Uniquely for such scenarios, the radar target is regarded as a potential eavesdropper which might surveil the information sent from the base station (BS) to communication users (CUs), that is carried by the radar probing signal. Transmit waveform and receive beamforming are jointly designed to maximize the signal-to-interference-plus-noise ratio (SINR) of the radar under the security and power budget constraints. We apply a Directional Modulation (DM) approach to exploit constructive interference (CI), where the known multiuser interference (MUI) can be exploited as a source of useful signal. Moreover, to further deteriorate the eavesdropping signal at the radar target, we utilize destructive interference (DI) by pushing the received symbols at the target towards the destructive region of the signal constellation. Our numerical results verify the effectiveness of the proposed design showing a secure transmission with enhanced performance against benchmark DFRC techniques.
Neural Waveshaping Synthesis

We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal approach to neural audio synthesis which operates directly in the waveform domain, with an accompanying optimisation (FastNEWT) for efficient CPU inference. The NEWT uses time-distributed multilayer perceptrons with periodic activations to implicitly learn nonlinear transfer functions that encode the characteristics of a target timbre. Once trained, a NEWT can produce complex timbral evolutions by simple affine transformations of its input and output signals. We paired the NEWT with a differentiable noise synthesiser and reverb and found it capable of generating realistic musical instrument performances with only 260k total model parameters, conditioned on F0 and loudness features. We compared our method to state-of-the-art benchmarks with a multi-stimulus listening test and the Fr\'echet Audio Distance and found it performed competitively across the tested timbral domains. Our method significantly outperformed the benchmarks in terms of generation speed, and achieved real-time performance on a consumer CPU, both with and without FastNEWT, suggesting it is a viable basis for future creative sound design tools.
Toward Decentralized Interior Point Methods for Control

Distributed and decentralized optimization are key for the control of network systems—for example in distributed model predictive control, and in distributed sensing or estimation. Non-linear systems, however, lead to problems with non-convex constraints for which classical decentralized optimization algorithms lack convergence guarantees. Moreover, classical decentralized algorithms usually exhibit only linear convergence. This paper presents a decentralized optimization algorithm based on primal-dual interior point methods, which is based on neighbor-to-neighbor communication. We prove local convergence for non-convex problems at a superlinear rate. We show that the method works reliably on a medium-scale numerical example from power systems. Our results indicate that the proposed method outperforms ADMM in terms of computational complexity.
Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications

The seven basic facial expression classifications are a basic way to express complex human emotions and are an important part of artificial intelligence research. Based on the traditional Bayesian neural network framework, the ResNet18_BNN network constructed in this paper has been improved in the following three aspects: (1) A new objective function is proposed, which is composed of the KL loss of uncertain parameters and the intersection of specific parameters. Entropy loss composition. (2) Aiming at a special objective function, a training scheme for alternately updating these two parameters is proposed. (3) Only model the parameters of the last convolution group. Through testing on the FER2013 test set, we achieved 71.5% and 73.1% accuracy in PublicTestSet and PrivateTestSet, respectively. Compared with traditional Bayesian neural networks, our method brings the highest classification accuracy gain.
Identifying Layers Susceptible to Adversarial Attacks

In this paper, we investigate the use of pretraining with adversarial networks, with the objective of discovering the relationship between network depth and robustness. For this purpose, we selectively retrain different portions of VGG and ResNet architectures on CIFAR-10, Imagenette, and ImageNet using non-adversarial and adversarial data. Experimental results show that susceptibility to adversarial samples is associated with low-level feature extraction layers. Therefore, retraining of high-level layers is insufficient for achieving robustness. Furthermore, adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers. This supports common hypotheses regarding the association of robustness with the feature extractor, insufficiency of deeper layers in providing robustness, and large differences in adversarial and non-adversarial feature vectors.