Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Transducers through Adjacent Token Merging

Recent end-to-end automatic speech recognition (ASR) systems often utilize a Transformer-based acoustic encoder that generates embedding at a high frame rate. However, this design is inefficient, particularly for long speech signals due to the quadratic computation of self-attention. To address this, we propose a new method, Adjacent Token Merging (A-ToMe), which gradually combines adjacent tokens with high similarity scores between their key values. In this way, the total time step could be reduced, and the inference of both the encoder and joint network is accelerated. Experiments on LibriSpeech show that our method can reduce 57% of tokens and improve the inference speed on GPU by 70% without any notable loss of accuracy. Additionally, we demonstrate that A-ToMe is also an effective solution to reduce tokens in long-form ASR, where the input speech consists of multiple utterances.
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees

Infinite-horizon optimal control of constrained piecewise affine (PWA) systems has been approximately addressed by hybrid model predictive control (MPC), which, however, has computational limitations, both in offline design and online implementation. In this paper, we consider an alternative approach based on approximate dynamic programming (ADP), an important class of methods in reinforcement learning. We accommodate non-convex union-of-polyhedra state constraints and linear input constraints into ADP by designing PWA penalty functions. PWA function approximation is used, which allows for a mixed-integer encoding to implement ADP. The main advantage of the proposed ADP method is its online computational efficiency. Particularly, we propose two control policies, which lead to solving a smaller-scale mixed-integer linear program than conventional hybrid MPC, or a single convex quadratic program, depending on whether the policy is implicitly determined online or explicitly computed offline. We characterize the stability and safety properties of the closed-loop systems, as well as the sub-optimality of the proposed policies, by quantifying the approximation errors of value functions and policies. We also develop an offline mixed-integer linear programming-based method to certify the reliability of the proposed method. Simulation results on an inverted pendulum with elastic walls and on an adaptive cruise control problem validate the control performance in terms of constraint satisfaction and CPU time.
SE-PQA: Personalized Community Question Answering

Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experiments and evaluate models for personalized search. This paper contributes to filling this gap by introducing SE-PQA (StackExchange - Personalized Question Answering), a new curated resource to design and evaluate personalized models related to the task of community Question Answering (cQA). The contributed dataset includes more than 1 million queries and 2 million answers, annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. We describe the characteristics of SE-PQA and detail the features associated with questions and answers. We also provide reproducible baseline methods for the cQA task based on the resource, including deep learning models and personalization approaches. The results of the preliminary experiments conducted show the appropriateness of SE-PQA to train effective cQA models; they also show that personalization remarkably improves the effectiveness of all the methods tested. Furthermore, we show the benefits in terms of robustness and generalization of combining data from multiple communities for personalization purposes.
Global solutions and blow-up for the wave equation with variable coefficients: II. boundary supercritical source

In this paper, we consider the wave equation with variable coefficients and boundary damping and supercritical source terms. The goal of this work is devoted to prove the local and global existence, and classify decay rate of energy depending on the growth near zero on the damping term. Moreover, we prove the blow-up of the weak solution with positive initial energy as well as nonpositive initial energy.
Search for an Isotropic Gravitational-wave Background with the Parkes Pulsar Timing Array

Pulsar timing arrays aim to detect nanohertz-frequency gravitational waves (GWs). A background of GWs modulates pulsar arrival times and manifests as a stochastic process, common to all pulsars, with a signature spatial correlation. Here we describe a search for an isotropic stochastic gravitational-wave background (GWB) using observations of 30 millisecond pulsars from the third data release of the Parkes Pulsar Timing Array (PPTA), which spans 18 yr. Using current Bayesian inference techniques we recover and characterize a common-spectrum noise process. Represented as a strain spectrum hc=A(f/1yr−1)α , we measure A=3.1−0.9+1.3×10−15 and α = −0.45 ± 0.20, respectively (median and 68% credible interval). For a spectral index of α = −2/3, corresponding to an isotropic background of GWs radiated by inspiraling supermassive black hole binaries, we recover an amplitude of A=2.04−0.22+0.25×10−15 . However, we demonstrate that the apparent signal strength is time-dependent, as the first half of our data set can be used to place an upper limit on A that is in tension with the inferred common-spectrum amplitude using the complete data set. We search for spatial correlations in the observations by hierarchically analyzing individual pulsar pairs, which also allows for significance validation through randomizing pulsar positions on the sky. For a process with α = −2/3, we measure spatial correlations consistent with a GWB, with an estimated false-alarm probability of p ≲ 0.02 (approx. 2σ). The long timing baselines of the PPTA and the access to southern pulsars will continue to play an important role in the International Pulsar Timing Array.
Cascaded encoders for fine-tuning ASR models on overlapped speech

Multi-talker speech recognition (MT-ASR) has been shown to improve ASR performance on speech containing overlapping utterances from more than one speaker. Multi-talker models have typically been trained from scratch using simulated or actual overlapping speech datasets. On the other hand, the trend in ASR has been to train foundation models using massive datasets collected from a wide variety of task domains. Given the scale of these models and their ability to generalize well across a variety of domains, it makes sense to consider scenarios where a foundation model is augmented with multi-talker capability. This paper presents an MT-ASR model formed by combining a well-trained foundation model with a multi-talker mask model in a cascaded RNN-T encoder configuration. Experimental results show that the cascade configuration provides improved WER on overlapping speech utterances with respect to a baseline multi-talker model without sacrificing performance achievable by the foundation model on non-overlapping utterances.
OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection

Since the strong comorbid similarity in NDDs, such as attention-deficit hyperactivity disorder, can interfere with the accurate diagnosis of autism spectrum disorder (ASD), identifying unknown classes is extremely crucial and challenging from NDDs. We design a novel open set recognition framework for ASD-aided diagnosis (OpenNDD), which trains a model by combining autoencoder and adversarial reciprocal points learning to distinguish in-distribution and out-of-distribution categories as well as identify ASD accurately. Considering the strong similarities between NDDs, we present a joint scaling method by Min-Max scaling combined with Standardization (MMS) to increase the differences between classes for better distinguishing unknown NDDs. We conduct the experiments in the hybrid datasets from Autism Brain Imaging Data Exchange I (ABIDE I) and THE ADHD-200 SAMPLE (ADHD-200) with 791 samples from four sites and the results demonstrate the superiority on various metrics. Our OpenNDD achieves promising performance, where the accuracy is 77.38%, AUROC is 75.53% and the open set classification rate is as high as 59.43%.
OpenCitations Meta

OpenCitations Meta is a new database for open bibliographic metadata of scholarly publications involved in the citations indexed by the OpenCitations infrastructure, adhering to Open Science principles and published under a CC0 license to promote maximum reuse. It presently incorporates bibliographic metadata for publications recorded in Crossref, DataCite, and PubMed, making it the largest bibliographic metadata source using Semantic Web technologies. It assigns new globally persistent identifiers (PIDs), known as OpenCitations Meta Identifiers (OMIDs) to all bibliographic resources, enabling it both to disambiguate publications described using different external PIDS (e.g., a DOI in Crossref and a PMID in PubMed) and to handle citations involving publications lacking external PIDs. By hosting bibliographic metadata internally, OpenCitations Meta eliminates its former reliance on API calls to external resources and thus enhances performance in response to user queries. Its automated data curation, following the OpenCitations Data Model, includes deduplication, error correction, metadata enrichment, and full provenance tracking, ensuring transparency and traceability of data and bolstering confidence in data integrity, a feature unparalleled in other bibliographic databases. Its commitment to Semantic Web standards ensures superior interoperability compared to other machine-readable formats, with availability via a SPARQL endpoint, REST APIs, and data dumps.
Holography for the trace anomaly action

A recently proposed effective action for the trace anomaly describes a tensor-scalar theory that is weakly coupled up to a certain high energy scale, where it becomes strongly interacting. Its ultraviolet completion is obtained by coupling to gravity a quantum field theory in which conformal invariance is spontaneously broken. In this paper, we show that if the field theory that gives rise to the trace anomaly is a large $N_c$ conformal field theory, then the trace anomaly action has a completion above the strong scale in a holographic Randall-Sundrum two-brane theory, with the radion as a low energy remnant of the spontaneously broken conformal symmetry. Furthermore, we note that the sub-leading $N_c$ terms can be derived by adding localized fields to the UV brane, so that the theory remains weakly coupled. The sub-leading terms are also obtained by introducing the Weyl squared terms in the 5D bulk. These, however, exhibit strongly coupled behavior at the respective sub-Planckian energy scales.
End-to-End Integrated Simulation for Predicting the Fate of Contaminant and Remediating Nano-Particles in a Polluted Aquifer

Groundwater contamination caused by Dense Non-Aqueous Phase Liquid (DNAPL) has an adverse impact on human health and environment. Remediation techniques, such as the in-situ injection of nano Zero Valent Iron (nZVI) particles, are widely used in mitigating DNAPL-induced groundwater contamination. However, an effective remediation strategy requires predictive insights and understanding of the physiochemical interaction of nZVI and contamination along with the porous media properties. While several stand-alone models are widely used for predictive modeling, the integration of these models for better scalability and accuracy is still rarely utilized. This study presents an end-to-end integrated modeling framework for the remediation of DNAPL-contaminated aquifers using nZVI. The framework simulates the migration pathway of DNAPL and subsequently its dissolution in groundwater resulting in an aqueous contaminant plume. Additionally, the framework includes simulations of nZVI mobility, transport, and reactive behavior, allowing for the prediction of the radius of influence and efficiency of nZVI for contaminant degradation. The framework has been applied to a hypothetical 2-dimensional and heterogeneous silty sand aquifer, considering trichloroethylene (TCE) as the DNAPL contaminant and carboxymethyl cellulose (CMC) coated nZVI for remediation. The results demonstrate the framework's capability to provide comprehensive insights into the contaminant's behavior and the effectiveness of the remediation strategy. The proposed modeling framework serves as a reference for future studies and can be expanded to incorporate real field data and complex geometries for upscaled applications.
A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning

Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, research on why transferred knowledge works has not been extensively explored. To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. On this basis, we express DS using deep channel-wise correlation and intermediate spatial distribution, and propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy. This is especially useful in specific applications where the performance gap between dual modalities is relatively large. Furthermore, we collect a real-world CML dataset to promote community development. The dataset contains more than 10,000 paired optical and radar images and is continuously being updated. Experimental results on real-world and benchmark datasets validate the effectiveness of the proposed method.
Collective Wigner crystal tunneling in carbon nanotubes

The collective tunneling of a a Wigner necklace - a crystalline state of a small number of strongly interacting electrons confined to a suspended nanotube and subject to a double well potential - is theoretically analyzed and compared with experiments in [Shapir $\textit{et al.}$, Science $\textbf {364}$, 870 (2019)]. Density Matrix Renormalization Group computations, exact diagonalization, and instanton theory provide a consistent description of this very strongly interacting system, and show good agreement with experiments. Experimentally extracted and theoretically computed tunneling amplitudes exhibit a scaling collapse. Collective quantum fluctuations renormalize the tunneling, and substantially enhance it as the number of electrons increases.
Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation

The aim of this article is to infer the connectivity structures of brain regions before and during epileptic seizure. Our contributions are fourfold. First, we propose a 6N-dimensional stochastic differential equation for modelling the activity of N coupled populations of neurons in the brain. This model further develops the (single population) stochastic Jansen and Rit neural mass model, which describes human electroencephalography (EEG) rhythms, in particular signals with epileptic activity. Second, we construct a reliable and efficient numerical scheme for the model simulation, extending a splitting procedure proposed for one neural population. Third, we propose an adapted Sequential Monte Carlo Approximate Bayesian Computation algorithm for simulation-based inference of both the relevant real-valued model parameters as well as the {0,1}-valued network parameters, the latter describing the coupling directions among the N modelled neural populations. Fourth, after illustrating and validating the proposed statistical approach on different types of simulated data, we apply it to a set of multi-channel EEG data recorded before and during an epileptic seizure. The real data experiments suggest, for example, a larger activation in each neural population and a stronger connectivity on the left brain hemisphere during seizure.
Retransmission performance in a stochastic geometric cellular network model

Suppose sender-receiver transmission links in a downlink network at given data rate are subject to fading, path-loss and inter-cell interference, and that transmissions either pass, suffer loss, or incur retransmission delay. We introduce a method to obtain the average activity level of the system required for handling the buffered work and from this derive the resulting coverage probability and key performance measures. The technique involves a family of stationary buffer distributions which is used to solving iteratively a nonlinear balance equation for the unknown busy-link probability and then identifying throughput, loss probability and delay. The results allow for straightforward numerical investigation of performance indicators, are in special cases explicit, and may be easily used to study the trade-off between reliability, latency, and data rate.
Charge-Density Waves vs. Superconductivity: Some Results and Future Perspectives

Increasing experimental evidence suggests the occurrence of filamentary superconductivity in different (quasi) two-dimensional physical systems. In this piece of work, we discuss the proposal that under certain circumstances, this occurrence may be related to the competition with a phase characterized by charge ordering in the form of charge-density waves. We provide a brief summary of experimental evidence supporting our argument in two paradigmatic classes of materials, namely transition metal dichalcogenides and cuprates superconductors. We present a simple Ginzburg–Landau two-order-parameters model as a starting point to address the study of such competition. We finally discuss the outcomes of a more sophisticated model, already presented in the literature and encoding the presence of impurities, and how it can be further improved in order to really address the interplay between charge-density waves and superconductivity and the possible occurrence of filamentary superconductivity at the domain walls between different charge-ordered regions.
Bridging Gaps at V1: Neural Responses for Filling-in and Completion at the Blind Spot

No retinal input exists at the blind spot (BS). However, we do not perceive a hole in the visual field. Instead, within the BS, we perceive the same color, contour or texture as the stimuli surrounding the BS. This is called perceptual fillingin or completion at the BS. Filling-in and completion occurs not only at the BS but also in the retinal scotoma and in various phenomena in the normal visual field. This suggests that there are some mechanisms in our visual system that could interpolate incomplete retinal signals to form contiguous surface and contour. To elucidate the role of the primary visual cortex (V1) in perceptual filling-in and completion, we analyzed neural responses at the retinotopic representation of the BS in macaque V1. We addressed two questions: (1) Do V1 neurons respond to stimuli inducing filling-in or completion even though there is no direct retinal input to this region? (2) Do the responses of V1 neurons to such stimuli correlate with perception? We found that the answers to both of the above questions are 'yes. ' Our results suggest that V1 plays an important role in the occurrence of filling-in and completion at the BS.
Interstitial lung disease in systemic sclerosis: From immunopathogenesis to treatment

Abstract Interstitial lung disease (ILD) is a pulmonary involvement that is commonly manifested in systemic sclerosis (SSc) patients. The immunopathogenesis of SSc-ILD involves several mechanisms, including microvascular injury, alveolar epithelial cell defect, inflammation, genetics, epigenetics, telomeres, telomerase and inflammasome, which result in lung fibrosis. Detection of ILD should be performed in every SSc patient by using high-resolution chest tomography (HRCT) scan of the thorax, in addition to evaluation by pulmonary function tests. When ILD is discovered, the physician might start treatment considering factors such as the extent of the lesion, progressivity of the disease, prognosis and drug toxicity. The current guideline recommends cyclophosphamide, mycophenolate mofetil and nintedanib as the initial choices for SSc-ILD treatment. Other agents such as biologic immunotherapies, haematopoietic stem cell transplantation and lung transplantation could be an option if the disease becomes progressive.
Biomonitoring of radioactive contamination using benthic invertebrate communities in Manzala Lake, Egypt

Abstract Manzala Lake, the largest in Egypt’s Nile Delta, has significant human activity. The Lake’s ecological condition has recently been impacted by a significant increase in agricultural, industrial, and urban wastewater discharge. A valuable tool for monitoring the water quality of Manzala Lake is the sensitivity of invertebrate species to various types of pollution, such as radioactive contamination. Activity concentrations of radionuclides Radium-226 (226Ra), Thorium-232 (232Th), Potassium-40 (40K), and Caesium-137 (137Cs) were measured in water, sediments and benthic invertebrate samples in 2020. The benthic community’s spatial distribution and the radionuclides’ bioaccumulation were evaluated to determine possible relationships. Thirty taxa of benthic invertebrates were recognised. The data illustrated that the mean activity concentration of radionuclides in water was in the order of 40K > 232Th > 226Ra > 137Cs, which changed into 40K > 226Ra > 232Th > 137Cs in the sediment and benthic invertebrates. Gastropoda and Ostracoda are the dominating groups of benthos in the lake and are related to the highest concentrations of radionuclides. The benthos species with shells dominated at the sites with the highest activity concentration of 226Ra, while soft-bodied organisms dominated in sites with the highest average 137Cs activity in these samples.
Metaphoric construction of corruption in Nigerian media discourse

Abstract Conceptual metaphor theory (CMT) and cultural linguistics (CL), which form the chief theoretical anchor for this study, express the role of language, culture and cognition in the construal and expression of human experiences. The approaches posit that the metaphorical use of language by an individual or group is shaped by their ideological orientation, knowledge of their natural and social world, and their cultural worldview. This study investigates the metaphorical use of language by the Nigerian print media in their attempts to express ideation on corruption in the Nigerian public sector. There has not been any deliberate and rigorous attempt to study the metaphorical representation of corruption in the Nigerian media discourse, hence this study aims at addressing the lacuna. Thus, this study uses insights from CMT and CL to identify, classify and analyse some of these metaphors in six Nigerian newspapers, namely; The Guardian, Punch, Daily Trust, The Nation, Vanguard and The Sun. The study shows that print media reports of corruption in Nigeria involving powerful social actors such as politicians, government appointees, career civil servants, academics, the military, the media and others have often used metaphorical frames that emanate from the domains of disease, war and conflict, enmity, impediment to movement, violent natural forces, monstrosity, among others, to describe the incidence, scope, effects and consequences of public sector corruption in Nigeria.