Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets. These models are typically pre-trained in self-supervised manner with the goal to improve automatic speech recognition performance -- and thus, to understand linguistic information. In this work, we investigate the extent in which this information is exploited during SER fine-tuning. Using a reproducible methodology based on open-source tools, we synthesise prosodically neutral speech utterances while varying the sentiment of the text. Valence predictions of the transformer model are very reactive to positive and negative sentiment content, as well as negations, but not to intensifiers or reducers, while none of those linguistic features impact arousal or dominance. These findings show that transformers can successfully leverage linguistic information to improve their valence predictions, and that linguistic analysis should be included in their testing.
A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledges on the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.
Reaching a Consensus with Limited Information

In its simplest form the well known consensus problem for a networked family of autonomous agents is to devise a set of protocols or update rules, one for each agent, which can enable all of the agents to adjust or tune their "agreement variable" to the same value by utilizing real-time information obtained from their "neighbors" within the network. The aim of this paper is to study the problem of achieving a consensus in the face of limited information transfer between agents. By this it is meant that instead of each agent receiving an agreement variable or real-valued state vector from each of its neighbors, it receives a linear function of each state instead. The specific problem of interest is formulated and provably correct algorithms are developed for a number of special cases of the problem.
End-to-End Multi-Speaker ASR with Independent Vector Analysis

We develop an end-to-end system for multi-channel, multi-speaker automatic speech recognition. We propose a frontend for joint source separation and dereverberation based on the independent vector analysis (IVA) paradigm. It uses the fast and stable iterative source steering algorithm together with a neural source model. Unlike conventional neural beamforming, the number of speakers can be dynamically changed during or after training. The parameters from the ASR module and the neural source model are optimized jointly from the ASR loss itself. We demonstrate competitive performance with previous systems using neural beamforming frontends with only one-ninth of the trainable parameter. First, we explore the trade-offs when using various number of channels for training and testing. Second, we demonstrate that the proposed IVA frontend performs well on noisy data, even when trained on clean mixtures only. Third, we demonstrate recognition of mixtures of three and four speakers with a model trained on mixtures of two only.
LDP-IDS: Local Differential Privacy for Infinite Data Streams

Local differential privacy (LDP) is promising for private streaming data collection and analysis. However, existing few LDP studies over streams either apply to finite streams only or may suffer from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel w-event LDP paradigm to provide practical privacy guarantee for infinite streams. By constructing a unified error analysis, we adapt the existing budget division framework in centralized differential privacy (CDP) for LDP-IDS, which however incurs prohibitive noise and expensive communication cost. To this end, we propose a novel and extensible framework of population division and recycling, as well as online adaptive population division algorithms for LDP-IDS. We provide theoretical guarantees and demonstrate, through extensive discussions, that our proposed framework not only achieves significant reduction in utility loss and communication overhead, but also enjoys great compatibility for varied analytic tasks and flexibility of incorporating ideas of many existing stream algorithms. Extensive experiments on synthetic and real-world datasets validate the high effectiveness, efficiency, and flexibility of our proposed framework and methods.
Comments on Comments: Where Code Review and Documentation Meet

A central function of code review is to increase understanding; helping reviewers understand a code change aids in knowledge transfer and finding bugs. Comments in code largely serve a similar purpose, helping future readers understand the program. It is thus natural to study what happens when these two forms of understanding collide. We ask: what documentation-related comments do reviewers make and how do they affect understanding of the contribution? We analyze ca. 700K review comments on 2,000 (Java and Python) GitHub projects, and propose several filters to identify which comments are likely to be either in response to a change in documentation and/or call for such a change. We identify 65K such cases. We next develop a taxonomy of the reviewer intents behind such “comments on comments”. We find that achieving a shared under-standing of the code is key: reviewer comments most often focused on clarification, followed by pointing out issues to fix, such as typos and outdated comments. Curiously, clarifying comments were frequently suggested (often verbatim) by the reviewer, indicating a desire to persist their understanding acquired during code review. We conclude with a discussion of implications of our comments-on-comments dataset for research on improving code review, including the potential benefits for automating code review.
The solitary route to chimera states

We show how solitary states in a system of globally coupled FitzHugh-Nagumo oscillators can lead to the emergence of chimera states. By a numerical bifurcation analysis of a suitable reduced system in the thermodynamic limit we demonstrate how solitary states, after emerging from the synchronous state, become chaotic in a period-doubling cascade. Subsequently, states with a single chaotic oscillator give rise to states with an increasing number of incoherent chaotic oscillators. In large systems, these chimera states show extensive chaos. We demonstrate the coexistence of many of such chaotic attractors with diﬀerent Lyapunov dimensions, due to diﬀerent numbers of incoherent oscillators.
A Robin‐Neumann scheme with quasi‐Newton acceleration for partitioned fluid‐structure interaction

The Dirichlet–Neumann scheme is the most common partitioned algorithm for fluid‐structure interaction (FSI) and offers high flexibility concerning the solvers employed for the two subproblems. Nevertheless, it is not without shortcomings: to begin with, the inherent added‐mass effect often destabilizes the numerical solution severely. Moreover, the Dirichlet–Neumann scheme cannot be applied to FSI problems in which an incompressible fluid is fully enclosed by Dirichlet boundaries, as it is incapable of satisfying the volume constraint. In the last decade, interface quasi‐Newton methods have proven to control the added‐mass effect and substantially speed up convergence by adding a Newton‐like update step to the Dirichlet–Neumann coupling. They are, however, without effect on the incompressibility dilemma. As an alternative, the Robin‐Neumann scheme generalizes the fluid's boundary condition to a Robin condition by including the Cauchy stresses. While this modification in fact successfully tackles both drawbacks of the Dirichlet–Neumann approach, the price to be paid is a strong dependency on the Robin weighting parameter, with very limited a priori knowledge about good choices. This work proposes a strategy to merge these two ideas and benefit from their combined strengths. The resulting quasi‐Newton‐accelerated Robin‐Neumann scheme is compared to both Robin‐ and Dirichlet–Neumann variants. The numerical tests demonstrate that it does not only provide faster convergence, but also massively reduces the influence of the Robin parameter, mitigating the main drawback of the Robin‐Neumann algorithm.
Sense disambiguation of compound constituents

In distributional semantic accounts of the meaning of noun-noun compounds (e.g. starfish, bank account, houseboat) the important role of constituent polysemy remains largely unaddressed(cf. the meaning of star in starfish vs. star cluster vs. star athlete). Instead of semantic vectors that average over the different meanings of a constituent, disambiguated vectors of the constituents would be needed in order to see what these more specific constituent meanings contribute to the meaning of the compound as a whole. This paper presents a novel approach to this specific problem of word sense disambiguation: set expansion. We build on the approach developed by Mahabal et al. (2018) which was originally designed to solve the analogy problem. We modified their method in such a way that it can address the problem of sense disambiguation of compound constituents. The results of experiments with a data set of almost 9000 compounds (LADEC, Gagn\'e et al. 2019) suggest that this approach is successful, yet the success is sensitive to the frequency with which the compounds are attested.
MS-HLMO: Multiscale Histogram of Local Main Orientation for Remote Sensing Image Registration

Multisource image registration is challenging due to intensity, rotation, and scale differences among the images. Considering the characteristics and differences in multisource remote sensing images, a feature-based registration algorithm named multiscale histogram of local main orientation (MS-HLMO) is proposed. Harris corner detection is first adopted to generate feature points. The HLMO feature of each Harris feature point is extracted on a partial main orientation map (PMOM) with a generalized gradient location and orientation histogram-like (GGLOH) feature descriptor, which provides high intensity, rotation, and scale invariance. The feature points are matched through a multiscale matching strategy. Comprehensive experiments on 17 multisource remote sensing scenes demonstrate that the proposed MS-HLMO and its simplified version MS-HLMO+ outperform other competitive registration algorithms in terms of effectiveness and generalization.
Selecting task with optimal transport self-supervised learning for few-shot classification

Few-Shot classification aims at solving problems that only a few samples are available in the training process. Due to the lack of samples, researchers generally employ a set of training tasks from other domains to assist the target task, where the distribution between assistant tasks and the target task is usually different. To reduce the distribution gap, several lines of methods have been proposed, such as data augmentation and domain alignment. However, one common drawback of these algorithms is that they ignore the similarity task selection before training. The fundamental problem is to push the auxiliary tasks close to the target task. In this paper, we propose a novel task selecting algorithm, named Optimal Transport Task Selecting (OTTS), to construct a training set by selecting similar tasks for Few-Shot learning. Specifically, the OTTS measures the task similarity by calculating the optimal transport distance and completes the model training via a self-supervised strategy. By utilizing the selected tasks with OTTS, the training process of Few-Shot learning become more stable and effective. Other proposed methods including data augmentation and domain alignment can be used in the meantime with OTTS. We conduct extensive experiments on a variety of datasets, including MiniImageNet, CIFAR, CUB, Cars, and Places, to evaluate the effectiveness of OTTS. Experimental results validate that our OTTS outperforms the typical baselines, i.e., MAML, matchingnet, protonet, by a large margin (averagely 1.72\% accuracy improvement).
Power consumption of electromagnetic valvetrain system

The electromagnetic valvetrain (EMVT) has been widely investigated and considered to have great capability to improve the fuel economy, the emissions, and the torque output performance of the engine. While the mechanical camshaft driven valvetrain consumes mechanical energy to drive the valves, the EMVT consumes electrical energy. If the power consumption is not properly controlled, the improvement of engine fuel economy brought by camless valvetrain will be weakened. This paper focus on the power consumption of EMVT. The detailed power losses derivation of the EMVT was discussed and compared with conventional valvetrain. The power consumption and pumping losses at various valve operating modes are shown and compared, including various engine speed, variable transition time, variable valve lift, and valve deactivation. The result shows that the power consumption is almost proportional to the engine speed. At low engine speeds and low engine loads, the power consumption can be reduced by applying long transition time and small valve lift. The power consumption can also be reduced by deactivating half of the intake valves at low engine loads. Key-Words: Camless, Electromagnetic valvetrain, Power consumption, Variable valve, Pumping loss
CAsimulations: Modeling of topological dynamics in a disease using cellular automata

The prediction of the behavior of the disease, the level of affectation in a population and the ways to control it are the most important aspects studied by epidemiology using tools such as historical data and mathematical models. So, our objective is (1) to provide a tool capable of analyzing epidemiological phenomena starting from the most common social interactions within a group of individuals. (2) To provide a methodology to build epidemiological models from patterns and logical rules. (3) Determine the impact of social interactions on the spread of a disease. This paper describes the logical construction of two epidemiological models in cellular automata together with two of their variations based on topological and dynamical principles.
Arithmetic logical Irreversibility and the Turing's Halt Problem

: A different explanation to the halting problem in Turing's machine because of the phenomenon of arithmetic logical irreversibility and memory erasure that introduce computing uncertainty or improbability into the machine and therefore make it impossible to predict how the calculation or computation will develop. Characteristics of arithmetic logical irreversibility, Landauer principle and memory erasure. Definition of the concept of arithmetic logical entropy as a measure of uncertainty and indicator of loss 1 of information. Turing machine as a series of computational states that can be transformed through the λ calculus and the Turing/Church thesis into a series of arithmetic 2 logical operations equivalent to this machine and then applying different interpretations of the Shannon measure of information on this series of computational arithmetic logical operations may be defined the measure of computer uncertainty or improbability. The indecision in the halting problem of the Turing machine because of computational uncertainty that is expressed as lack of information to determine how computing will develop. Based on these facts 3 it can be shown that there is a local computational determinism that is determined by immediate computing conditions, but there is a global indeterminism over the entire series of computational operations jointed. This also leads us to a different explanation of Turing's oracle machine as such that it introduces external information into the computational performance to control computational uncertainty and thus decide the outcome of the computation. The Shannon's interpretations for the measure of the information show that indecision over the halting conditions of a Turing machine through an algorithm is a consequence of uncertainty and lack of
VNF and Container Placement: Recent Advances and Future Trends

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for most mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, referred to as Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network’s performance, reliability, and operating costs. The VNF placement is NP-Hard. Hence, there is a need for placement methods that can scale with the issue’s complexity and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include micro-services, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of containers in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, security, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.
Spatial Loss for Unsupervised Multi-channel Source Separation

We propose a spatial loss for unsupervised multi-channel source separation. The proposed loss exploits the duality of direction of arrival (DOA) and beamforming: the steering and beamforming vectors should be aligned for the target source, but orthogonal for interfering ones. The spatial loss encour-ages consistency between the mixing and demixing systems from a classic DOA estimator and a neural separator, respec-tively. With the proposed loss, we train the neural separators based on minimum variance distortionless response (MVDR) beamforming and independent vector analysis (IVA). We also investigate the effectiveness of combining our spatial loss and a signal loss , which uses the outputs of blind source separation as the references. We evaluate our proposed method on synthetic and recorded (LibriCSS) mixtures. We find that the spatial loss is most effective to train IVA-based separators. For the neural MVDR beamformer, it performs best when combined with a signal loss. On synthetic mixtures, the proposed unsupervised loss leads to the same performance as a supervised loss in terms of word error rate. On LibriCSS, we obtain close to state-of-the-art performance without any labeled training data.
Epipolar Focus Spectrum: A Novel Light Field Representation and Application in Dense-view Reconstruction

Existing light field representations, such as epipolar plane image (EPI) and sub-aperture images, do not consider the structural characteristics across the views, so they usually require additional disparity and spatial structure cues for follow-up tasks. Besides, they have difficulties dealing with occlusions or larger disparity scenes. To this end, this paper proposes a novel Epipolar Focus Spectrum (EFS) representation by rearranging the EPI spectrum. Different from the classical EPI representation where an EPI line corresponds to a specific depth, there is a one-to-one mapping from the EFS line to the view. Accordingly, compared to a sparsely-sampled light field, a densely-sampled one with the same field of view (FoV) leads to a more compact distribution of such linear structures in the double-cone-shaped region with the identical opening angle in its corresponding EFS. Hence the EFS representation is invariant to the scene depth. To demonstrate its effectiveness, we develop a trainable EFS-based pipeline for light field reconstruction, where a dense light field can be reconstructed by compensating the"missing EFS lines"given a sparse light field, yielding promising results with cross-view consistency, especially in the presence of severe occlusion and large disparity. Experimental results on both synthetic and real-world datasets demonstrate the validity and superiority of the proposed method over SOTA methods.
QUANTUM FRICTION

This work is devoted to several translation-invariant models in non-relativistic quantum ﬁeld theory (QFT), describing a non-relativistic quantum particle interacting with a quantized relativistic ﬁeld of bosons. In this setting, we aim at the rigorous study of Cherenkov radiation or friction eﬀects at small disorder, which amounts to the metastability of the embedded mass shell of the free non-relativistic particle when the coupling to the quantized ﬁeld is turned on. Although this problem is naturally approached by means of Mourre’s celebrated commutator method, important regularity issues are known to be inherent to QFT models and restrict the application of this method. In this perspective, we introduce a novel non-standard construction procedure for Mourre conjugate operators, which diﬀers from second quantization and allows to circumvent regularity issues. To show its versatility, we apply this construction to the Nelson model with massive bosons, to Fröhlich’s polaron model, and to a quantum friction model with massless bosons introduced by Bruneau and De Bièvre: for each of these examples, we improve on previous results. MSC-class: 81T10; 81V73; 47B25; 81Q10; 47A55; 81Q15; 47A40; 81U24.