Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-user Beam Alignment in Presence of Multi-path

To overcome the high pathloss and the intense shadowing in millimeterwave (mmWave) communications, effective beamforming schemes are required which incorporate narrow beams with high beamforming gains. The mm Wave channel consists of a few spatial clusters each associated with an angle of departure (AoD). The narrow beams must be aligned with the channel AoDs to increase the beamforming gain. This is achieved through a procedure called beam alignment (BA). Most of the BA schemes in the literature consider channels with a single dominant path while in practice the channel has a few resolvable paths with different AoDs, hence, such BA schemes may not work correctly in the presence of multi-path or at the least do not exploit such multi path to achieve diversity or increase robustness. In this paper, we propose an efficient BA schemes in presence of multi-path. The proposed BA scheme transmits probing packets using a set of scanning beams and receives the feedback for all the scanning beams at the end of probing phase from each user. We formulate the BA scheme as minimizing the expected value of the average transmission beamwidth under different policies. The policy is defined as a function from the set of received feedback to the set of transmission beams (TB). In order to maximize the number of possible feedback sequences, we prove that the set of scanning beams (SB) has an special form, namely, Tulip Design. Consequently, we rewrite the minimization problem with a set of linear constraints and reduced number of variables which is solved by using an efficient greedy algorithm.
Modal Loss Analysis of -a nd -Plane Filtering Structures

An efficient method for evaluating the scattering ma- trix of lossy -a nd -plane filters, diplexers, and triplexers is introduced. Starting from the electromagnetic computation of the powerlossinsidethestructure,thelossynetworkS-parametersare retrieved. The favorable comparison between the computed and measured results for several examples demonstrates the high effi- ciency and accuracy of the proposed approach. Index Terms—Loss analysis, modal expansion, waveguide com- ponents.
SAUTE RL: Almost Surely Safe Reinforcement Learning Using State Augmentation

Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plane landing and take-off should ideally occur with probability one. We address the problem by introducing Safety Augmented (Saute) Markov Decision Processes (MDPs), where the safety constraints are eliminated by augmenting them into the state-space and reshaping the objective. We show that Saute MDP satisfies the Bellman equation and moves us closer to solving Safe RL with constraints satisfied almost surely. We argue that Saute MDP allows viewing the Safe RL problem from a different perspective enabling new features. For instance, our approach has a plug-and-play nature, i.e., any RL algorithm can be"Sauteed". Additionally, state augmentation allows for policy generalization across safety constraints. We finally show that Saute RL algorithms can outperform their state-of-the-art counterparts when constraint satisfaction is of high importance.
Hard X-ray luminosity functions of cataclysmic variables: Joint Swift/BAT and Gaia data

Cataclysmic variables (CVs) are the most numerous population among the Galactic objects emitting in hard X-rays. Most probably, they are responsible for the extended hard X-ray emission of the Galactic ridge and the central Galactic regions. Here we consider the sample of CVs detected in the all-sky hard X-ray Swift/BAT survey which were also detected by Gaia and thus have reliable distance estimates. Using these data, we derive accurate estimates for local number density per solar mass ($\rho _{\rm M} = 1.37^{+0.3}_{-0.16}. \times 10^{-5}\, {\rm M}_\odot ^{-1}$) and luminosity density per solar mass ($\rho _{\rm L} = 8.95^{+0.15}_{-0.1}\times 10^{26}$ erg s−1 M$_\odot ^{-1}$) for objects in the sample. These values appear to be in good agreement with the integrated Galactic ridge X-ray emission and Nuclear Stellar Cluster luminosities. Analysis of the differential luminosity functions dρM/d(log10Lx) and dρL/d(log10Lx) confirms that there are two populations of hard X-ray emitting CVs. Intermediate polars dominate at luminosities L > 1033 erg s−1, whereas non-magnetic CVs and polars are much more numerous but have lower luminosities on average. As a consequence, the contribution of these populations to the observed hard X-ray luminosity is almost equivalent.
Dual Task Framework for Improving Persona-grounded Dialogue Dataset

This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly depend on the raw crowdsourced benchmark datasets such as Persona-Chat. In contrast, we aim to fix annotation artifacts in benchmarking, which is orthogonally applicable to any dialogue model. Specifically, we augment relevant personas to improve dialogue dataset/agent, by leveraging the primal-dual structure of the two tasks, predicting dialogue responses and personas based on each other. Experiments on Persona-Chat show that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of accuracy.
Splitting numerical integration for matrix completion

Low rank matrix approximation is a popular topic in machine learning. In this paper, we propose a new algorithm for this topic by minimizing the least-squares estimation over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical gradient descent within the framework of optimization on manifolds. In particular, we reformulate an unconstrained optimization problem on a low-rank manifold into a differential dynamic system. We develop a splitting numerical integration method by applying a splitting integration scheme to the dynamic system. We conduct the convergence analysis of our splitting numerical integration algorithm. It can be guaranteed that the error between the recovered matrix and true result is monotonically decreasing in the Frobenius norm. Moreover, our splitting numerical integration can be adapted into matrix completion scenarios. Experimental results show that our approach has good scalability for large-scale problems with satisfactory accuracy
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency, among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies for them according to the evaluation perspectives and evaluation methods. Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them. The contents of this survey are based on more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP, and IUI, and their complete summarization are presented at https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MODOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation.
Expected yields of $^{181}$Ta (e,e') $^{181}$Ta$^*$ in the multi-keV range with a plasma-cathode electron beam

Calculations of the cross sections of inelastic electron scattering (e, e) on a nucleus in the multikeV energy range strongly depend on the description of the screening of the nuclear Coulomb potential as well as on the deformation of the wave functions of the incoming and outgoing electron in the vicinity of the nucleus. These cross sections are evaluated at values lower than 10 cm which vary by several orders of magnitude according to the models. Experimental measurements would be required to constrain the models but it is a real challenge to measure such low cross sections. In this study, we demonstrate that inelastic electron scattering is the main nuclear excitation mechanism in a Ta target irradiated with a new intense 10 30 keV electron beam produced with a biased laser-plasma. Calculations show that through the detection of conversion electrons, it should be possible to measure the nuclear excitation yields. The effect of electron beam heating and of plasma deposition on the tantalum target are quantified, thus allowing the dimensioning of a possible experimental configuration to study (e, e) processes in this range of energy for the first time. ∗ gobet@cenbg.in2p3. fr
Temperature Control of Diffusive Memristor Hysteresis and Artificial Neuron Spiking

Memristive devices are promising elements for energy-efficient neuromorphic computing and future artificial intelligence systems. For diffusive memristors, the device state switching occurs because of the sequential formation and disappearance of conduction pillars between device terminals due to the drift and diffusion of Ag nanoparticles in the dielectric matrix. This process is governed by the application of the voltage to the device contacts. Here, both in experiment and in theory we demonstrate that varying temperature offers an efficient control of memristor states and charges transport in the device. We found out that by raising and lowering the device temperature, one can reset the memristor state as well as change the residual time the memristor stays in high and low resistive states when the current spiking is generated in the memristive circuit at a constant applied voltage. Our theoretical model demonstrates a good qualitative agreement with the experiments and helps to explain the effects reported.
Rate-Matching the Regret Lower-Bound in the Linear Quadratic Regulator with Unknown Dynamics

The theory of adaptive learning-based control currently suffers from a mismatch between its empirical performance and the theoretical characterization of its performance, with consequences for, e.g., the understanding of sample efficiency, safety, and robustness. The linear quadratic regulator with unknown dynamics is a fundamental adaptive control setting with significant structure in its dynamics and cost function, yet even in this setting the ratio between the best-known regret or estimation error upper bounds and their corresponding best-known lower bounds is unbounded due to polylogarithmic factors in $T$. This gap has not been closed in any of the many papers theoretically studying the linear quadratic regulator with unknown dynamics, and indeed similar gaps have plagued other areas of theoretical online learning such as reinforcement learning. The contribution of this paper is to close that gap by establishing a novel regret upper-bound of $O_{p}(\sqrt{T})$, and simultaneously establishes an estimation error bound on the dynamics of $O_{p}(T^{-1/4})$. The two keys to our improved proof technique are (1) a more precise upper- and lower-bound on the system Gram matrix by establishing exact rates of eigenvalues from different sub-spaces and (2) a self-bounding argument for the expected estimation error of the optimal controller. Our technique may shed light on removing polylogarithmic factors in other adaptive learning problems.
Close-up and Whispering: An Understanding of Multimodal and Parasocial Interactions in YouTube ASMR videos

ASMR (Autonomous Sensory Meridian Response) has grown to immense popularity on YouTube and drawn HCI designers’ attention to its effects and applications in design. YouTube ASMR creators incorporate visual elements, sounds, motifs of touching and tasting, and other scenarios in multisensory video interactions to deliver enjoyable and relaxing experiences to their viewers. ASMRtists engage viewers by social, physical, and task attractions. Research has identified the benefits of ASMR in mental wellbeing. However, ASMR remains an understudied phenomenon in the HCI community, constraining designers’ ability to incorporate ASMR in video-based designs. This work annotates and analyzes the interaction modalities and parasocial attractions of 2663 videos to identify unique experiences. YouTube comment sections are also analyzed to compare viewers’ responses to different ASMR interactions. We find that ASMR videos are experiences of multimodal social connection, relaxing physical intimacy, and sensory-rich activity observation. Design implications are discussed to foster future ASMR-augmented video interactions.
A Tight (1.5+ε)-Approximation for Unsplittable Capacitated Vehicle Routing on Trees

In the unsplittable capacitated vehicle routing problem (UCVRP) on trees, we are given a rooted tree with edge weights and a subset of vertices of the tree called terminals. Each terminal is associated with a positive demand between 0 and 1. The goal is to find a minimum length collection of tours starting and ending at the root of the tree such that the demand of each terminal is covered by a single tour (i.e., the demand cannot be split), and the total demand of the terminals in each tour does not exceed the capacity of 1. For the special case when all terminals have equal demands, a long line of research culminated in a quasi-polynomial time approximation scheme [Jayaprakash and Salavatipour, TALG 2023] and a polynomial time approximation scheme [Mathieu and Zhou, TALG 2023]. In this work, we study the general case when the terminals have arbitrary demands. Our main contribution is a polynomial time (1 . 5 + ϵ )-approximation algorithm for the UCVRP on trees. This is the first improvement upon the 2-approximation algorithm more than 30 years ago. Our approximation ratio is essentially best possible, since it is NP-hard to approximate the UCVRP on trees to better than a 1.5 factor.
1 ARRAY ANTENNAS DIAGNOSTICS THROUGH PHASELESS MEASUREMENTS AND SPARSITY-PROMOTING APPROACHES

The problem of characterizing a radiating source by using measurements setup as simple as possible and reduced measurement time gains great interest in the literature in different research areas. Amongst them, the problem of performing the diagnostics of an antenna [or near-field (NF) to far-field (FF) transformations] by using the least possible number of measurements is worth to be mentioned. Interestingly, the recently introduced compressive sensing (CS) theory [1] blends very well the above requirements. In fact, it allows the diagnosis of an array antenna by using a number of measurements (say M) much lower than the number of array elements (say N). Of course, because of the underlying CS theory, M is supposed to anyway be larger or even much larger than the number faulty elements (say S). By following a different goal, phaseless measurements have also been the subject of intensive research. In fact, measuring a complex FF pattern requires a stable phase reference and accurate positioning of the probe in each measurement point, so that measurements may become problematic [2]. For all the above, the possible exploitation of CS for phaseless antenna diagnostics is of interest. Unfortunately, CS theory is established for linear problems, which is not the case at hand. However, recent research activities at Università Mediterranea have shown the possibility of performing very accurate phaseless array antennas diagnosis by taking advantage from CS [3],[4]. In particular, three different strategies have been developed and assessed for the case of both linear and planar arrays, including cases where mutual coupling plays a relevant role.
Essential Oils as Phytogenic Feed Additive: Potential Benefits on Environment, Livestock Health and Production

1ICAR-Central Institute for Research on Buffaloes, Hisar, Haryana (125 001), India 2ICARCentral Sheep and Wool Research Institute, Avikanagar, Rajasthan (304 501), India 3Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana (125 001), India 4 ICAR-Indian Veterinary Research Institute, Eastern Regional Station, Kolkata, West Bengal (700 037), India 5ICAR-National Dairy Research Institute, Karnal, Haryana (132 001), India 6The University of Sydney, Faculty of Agriculture and Environment, Camden, NSW-2570, Australia
Doubly heavy tetraquarks in a chiral-diquark picture

Energy spectrum of doubly heavy tetraquarks, T QQ ( QQ ¯ q ¯ q with Q = c, b and q = u, d, s ), is studied in the potential chiral-diquark model. Using the chiral eﬀective theory of diquarks and the quark-diquark-based potential model, the T bb , T cc , and T cb tetraquarks are described as a three-body system composed of two heavy quarks and an antidiquark. We ﬁnd several T bb bound states, while no T cc and T cb (deep) bound state is seen. We also study the change of the T QQ tetraquark masses under restoration of chiral symmetry.
Lorentzian spectral zeta functions on asymptotically Minkowski spacetimes

In this note, we consider perturbations of Minkowski space as well as more general spacetimes on which the wave operator g is essentially selfadjoint. We review a recent result which gives the meromorphic continuation of the Lorentzian spectral zeta function density, i.e. of the trace density of complex powers α 7→ ( g − iε) . In even dimension n > 4, the residue at n 2 − 1 is shown to be a multiple of the scalar curvature in the limit ε → 0. This yields a spectral action for gravity in Lorentzian signature.
Service-oriented enterprise architecture

This paper defined service-oriented enterprise architecture,service-oriented business process and activity types firstly,analyzed the evolution of meaning,the concept framework and service view of enterprise architecture,proposed an SOEA and its planning process based on the traditional enterprise modeling,IT planning and enterprise architecture which was the common understanding and communication foundation of the analyzing of service-oriented business,IT and their alignment.It helped to monitor the operation state of enterprise IT architecture,then predicted,accessed and optimized enterprise business performance,and realized inter-enterprise business processes collaboration modeling and enactment.
Multi-Modal Knowledge Graph Construction and Application: A Survey

Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strengths and weaknesses of different solutions. We finalize this survey with open research problems relevant to MMKGs.
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation

Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module that can be placed on top of existing segmentation networks for performing few-shot segmentation. This module, called the task-adaptive feature transformer (TAFT), linearly transforms task-specific high-level features to a set of task agnostic features well-suited to conducting few-shot segmentation. The task-conditioned feature transformation allows an effective utilization of the semantic information in novel classes to generate tight segmentation masks. We also propose a semantic enrichment (SE) module that utilizes a pixel-wise attention module for high-level feature and an auxiliary loss from an auxiliary segmentation network conducting the semantic segmentation for all training classes. Experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets confirm that the added modules successfully extend the capability of existing segmentators to yield highly competitive few-shot segmentation performances.
Inference of Multiscale Gaussian Graphical Model

Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields such as genomics, ecology, psychometry. In a high-dimensional setting, when the number of variables exceeds the number of observations by several orders of magnitude, the estimation of GGM is a difficult and unstable optimization problem. Clustering of variables or variable selection is often performed prior to GGM estimation. We propose a new method allowing to simultaneously infer a hierarchical clustering structure and the graphs describing the structure of independence at each level of the hierarchy. This method is based on solving a convex optimization problem combining a graphical lasso penalty with a fused type lasso penalty. Results on real and synthetic data are presented.
Deepchorus: A Hybrid Model of Multi-Scale Convolution And Self-Attention for Chorus Detection

Chorus detection is a challenging problem in musical signal processing as the chorus often repeats more than once in popular songs, usually with rich instruments and complex rhythm forms. Most of the existing works focus on the receptiveness of chorus sections based on some explicit features such as loudness and occurrence frequency. These pre-assumptions for chorus limit the generalization capacity of these methods, causing misdetection on other repeated sections such as verse. To solve the problem, in this paper we propose an end-to-end chorus detection model DeepChorus, reducing the engineering effort and the need for prior knowledge. The proposed model includes two main structures: i) a Multi-Scale Network to derive preliminary representations of chorus segments, and ii) a Self-Attention Convolution Network to further process the features into probability curves representing chorus presence. To obtain the final results, we apply an adaptive threshold to binarize the original curve. The experimental results show that DeepChorus outperforms existing state-of-the-art methods in most cases.
Measuring dissimilarity with diffeomorphism invariance

Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms. We introduce DID, a pairwise dissimilarity measure applicable to a wide range of data spaces, which leverages the data's internal structure to be invariant to diffeomorphisms. We prove that DID enjoys properties which make it relevant for theoretical study and practical use. By representing each datum as a function, DID is defined as the solution to an optimization problem in a Reproducing Kernel Hilbert Space and can be expressed in closed-form. In practice, it can be efficiently approximated via Nystr\"om sampling. Empirical experiments support the merits of DID.