Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech foundation models on intelligibility prediction for hearing-impaired listeners

Speech foundation models (SFMs) have been benchmarked on many speech processing tasks, often achieving state-of-the-art performance with minimal adaptation. However, the SFM paradigm has been significantly less explored for applications of interest to the speech perception community. In this paper we present a systematic evaluation of 10 SFMs on one such application: Speech intelligibility prediction. We focus on the non-intrusive setup of the Clarity Prediction Challenge 2 (CPC2), where the task is to predict the percentage of words correctly perceived by hearing-impaired listeners from speech-in-noise recordings. We propose a simple method that learns a lightweight specialized prediction head on top of frozen SFMs to approach the problem. Our results reveal statistically significant differences in performance across SFMs. Our method resulted in the winning submission in the CPC2, demonstrating its promise for speech perception applications.
Constraint-Aware Mesh Refinement Method by Reachability Set Envelope of Curvature Bounded Paths

This paper presents an enhanced direct-method-based approach for the real-time solution of optimal control problems to handle path constraints, such as obstacles. The principal contributions of this work are twofold: first, the existing methods for constructing reachability sets in the literature are extended to derive the envelope of these sets, which determines the region swept by all feasible trajectories between adjacent sample points. Second, we propose a novel method to guarantee constraint violation-free between discrete states in two dimensions through mesh refinement approach. To illustrate the effectiveness of the proposed methodology, numerical simulations are conducted on real-time path planning for fixed-wing unmanned aerial vehicles.
Supporting Sensemaking of Large Language Model Outputs at Scale

Large language models (LLMs) are capable of generating multiple responses to a single prompt, yet little effort has been expended to help end-users or system designers make use of this capability. In this paper, we explore how to present many LLM responses at once. We design five features, which include both pre-existing and novel methods for computing similarities and differences across textual documents, as well as how to render their outputs. We report on a controlled user study (n=24) and eight case studies evaluating these features and how they support users in different tasks. We find that the features support a wide variety of sensemaking tasks and even make tasks previously considered to be too difficult by our participants now tractable. Finally, we present design guidelines to inform future explorations of new LLM interfaces.
Harnack inequalities for kinetic integral equations

We deal with a wide class of kinetic equations, $$ \big[ \partial_t + v\cdot\nabla_x\big] f = \mathcal{L}_v f. $$ Above, the diffusion term $\mathcal{L}_v$ is an integro-differential operator, whose nonnegative kernel is of fractional order $s\in(0,1)$ having merely measurable coefficients. Amongst other results, we are able to prove that nonnegative weak solutions $f$ do satisfy $$ \sup_{Q^-} f \ \leq \ c\inf_{Q^+} f, $$ where $Q^{\pm}$ are suitable slanted cylinders. No a-priori boundedness is assumed, as usually in the literature, since we are also able to prove a general interpolation inequality in turn giving local boundedness which is valid even for weak subsolutions with no sign assumptions. To our knowledge, this is the very first time that a strong Harnack inequality is proven for kinetic integro-differential-type equations. A new independent result, a Besicovitch-type covering argument for very general kinetic geometries, is also stated and proved.
Convolutional Neural Networks can achieve binary bail judgement classification

There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data. The lower courts and data from the different regional languages of India are often overlooked. In this paper, we deploy a Convolutional Neural Network (CNN) architecture on a corpus of Hindi legal documents. We perform a bail Prediction task with the help of a CNN model and achieve an overall accuracy of 93\% which is an improvement on the benchmark accuracy, set by Kapoor et al. (2022), albeit in data from 20 districts of the Indian state of Uttar Pradesh.
Efficient photon-pair generation empowered by dual quasi-bound states in the continuum

Here we demonstrate the efficient photon-pair generation via spontaneous parametric down conversion from a semiconductor metasurface supporting dual quasi-bound states in the continuum (quasi-BICs). In a simple metasurface design composed of AlGaAs ellipse nano-cyclinders, the two high-$Q$ quasi-BIC resonances that coincide with the generated signal and idler frequencies significantly boost the local electric field. This leads to a substantial enhancement in the reverse classical nonlinear process of sum frequency generation and subsequently the remarkable high generation rate of photon pairs under the quantum-classical correspondence principle. Within a narrowband wavelength regime around the quasi-BIC resonances, the rate of pair production is enhanced up to $\sim10^{4}$ Hz, two orders of magnitude larger than that in the Mie resonant AlGaAs nanoantennas. Moreover, the photon pair emission is mainly concentrated in the normal direction with respect to the metasurface, and shows tunable rate with the $Q$ factor by engineering the rotation angle of nano-cylinders. The presented work enables nanoscale sources of high-quality entangled photons which will find applications in advanced quantum imaging and communications.
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation

Recently, text-to-3D approaches have achieved high-fidelity 3D content generation using text description. However, the generated objects are stochastic and lack fine-grained control. Sketches provide a cheap approach to introduce such fine-grained control. Nevertheless, it is challenging to achieve flexible control from these sketches due to their abstraction and ambiguity. In this paper, we present a multi-view sketch-guided text-to-3D generation framework (namely, Sketch2NeRF) to add sketch control to 3D generation. Specifically, our method leverages pretrained 2D diffusion models (e.g., Stable Diffusion and ControlNet) to supervise the optimization of a 3D scene represented by a neural radiance field (NeRF). We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF. In the experiments, we collected two kinds of multi-view sketch datasets to evaluate the proposed method. We demonstrate that our method can synthesize 3D consistent contents with fine-grained sketch control while being high-fidelity to text prompts. Extensive results show that our method achieves state-of-the-art performance in terms of sketch similarity and text alignment.
Principal Component Regression to Study the Impact of Economic Factors on Disadvantaged Communities

The Council on Environmental Quality's Climate and Economic Justice Screening Tool defines"disadvantaged communities"(DAC) in the USA, highlighting census tracts where benefits of climate and energy investments are not accruing. We use a principal component generalized linear model, which addresses the intertwined nature of economic factors, income and employment and model their relationship to DAC status. Our study 1) identifies the most significant income groups and employment industries that impact DAC status, 2) provides the probability of DAC status across census tracts and compares the predictive accuracy with widely used machine learning approaches, 3) obtains historical predictions of the probability of DAC status, 4) obtains spatial downscaling of DAC status across block groups. Our study provides valuable insights for policymakers and stakeholders to develop strategies that promote sustainable development and address inequities in climate and energy investments in the USA.
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition

Unsupervised skeleton based action recognition has achieved remarkable progress recently. Existing unsupervised learning methods suffer from severe overfitting problem, and thus small networks are used, significantly reducing the representation capability. To address this problem, the overfitting mechanism behind the unsupervised learning for skeleton based action recognition is first investigated. It is observed that the skeleton is already a relatively high-level and low-dimension feature, but not in the same manifold as the features for action recognition. Simply applying the existing unsupervised learning method may tend to produce features that discriminate the different samples instead of action classes, resulting in the overfitting problem. To solve this problem, this paper presents an Unsupervised spatial-temporal Feature Enrichment and Fidelity Preservation framework (U-FEFP) to generate rich distributed features that contain all the information of the skeleton sequence. A spatial-temporal feature transformation subnetwork is developed using spatial-temporal graph convolutional network and graph convolutional gate recurrent unit network as the basic feature extraction network. The unsupervised Bootstrap Your Own Latent based learning is used to generate rich distributed features and the unsupervised pretext task based learning is used to preserve the information of the skeleton sequence. The two unsupervised learning ways are collaborated as U-FEFP to produce robust and discriminative representations. Experimental results on three widely used benchmarks, namely NTU-RGB+D-60, NTU-RGB+D-120 and PKU-MMD dataset, demonstrate that the proposed U-FEFP achieves the best performance compared with the state-of-the-art unsupervised learning methods. t-SNE illustrations further validate that U-FEFP can learn more discriminative features for unsupervised skeleton based action recognition.
SoK: Game-Theoretic Cybersecurity: Assumptions, Models, Gaps, and Bridges

The discipline of game theory was introduced in the context of economics, and has been applied to study cyber attacker and defender behaviors. While adaptions have been made to accommodate features in the cyber domain, these studies are inherently limited by the root of game theory in economic systems where players (i.e., agents) may be selfish but not malicious. In this SoK, we systematize the major cybersecurity problems that have been studied with the game-theoretic approach, the assumptions that have been made, the models and solution concepts that have been proposed. The systematization leads to a characterization of the technical gaps that must be addressed in order to make game-theoretic cybersecurity models truly useful. We explore bridges to address them.
Separating k-Median from the Supplier Version

Given a metric space $(V, d)$ along with an integer $k$, the $k$-Median problem asks to open $k$ centers $C \subseteq V$ to minimize $\sum_{v \in V} d(v, C)$, where $d(v, C) := \min_{c \in C} d(v, c)$. While the best-known approximation ratio of $2.613$ holds for the more general supplier version where an additional set $F \subseteq V$ is given with the restriction $C \subseteq F$, the best known hardness for these two versions are $1+1/e \approx 1.36$ and $1+2/e \approx 1.73$ respectively, using the same reduction from Max $k$-Coverage. We prove the following two results separating them. First, we show a $1.546$-parameterized approximation algorithm that runs in time $f(k) n^{O(1)}$. Since $1+2/e$ is proved to be the optimal approximation ratio for the supplier version in the parameterized setting, this result separates the original $k$-Median from the supplier version. Next, we prove a $1.416$-hardness for polynomial-time algorithms assuming the Unique Games Conjecture. This is achieved via a new fine-grained hardness of Max-$k$-Coverage for small set sizes. Our upper bound and lower bound are derived from almost the same expression, with the only difference coming from the well-known separation between the powers of LP and SDP on (hypergraph) vertex cover.
Existence of solution for two classes of quasilinear systems defined on a non-reflexive Orlicz-Sobolev Spaces

This paper proves the existence of nontrivial solution for two classes of quasilinear systems of the type \begin{equation*} \left\{\; \begin{aligned} -\Delta_{\Phi_{1}} u&=F_u(x,u,v)+\lambda R_u(x,u,v)\;\text{ in } \Omega&\\ -\Delta_{\Phi_{2}} v&=-F_v(x,u,v)-\lambda R_v(x,u,v)\;\text{ in } \Omega&\\ u=v&=0\;\text{ on } \partial\Omega&\end{aligned} \right. \end{equation*} where $\lambda>0$ is a parameter, $\Omega$ is a bounded domain in $\mathbb{R}^N$($N \geq 2$) with smooth boundary $\partial \Omega$. The first class we drop the $\Delta_2$-condition of the functions $\tilde{\Phi}_i$($i=1,2$) and assume that $F$ has a double criticality. For this class, we use a linking theorem without the Palais-Smale condition for locally Lipschitz functionals combined with a concentration-compactness lemma for nonreflexive Orlicz-Sobolev space. The second class, we relax the $\Delta_2$-condition of the functions ${\Phi}_i$($i=1,2$). For this class, we consider $F=0$ and $\lambda=1$ and obtain the proof based on a saddle-point theorem of Rabinowitz without the Palais-Smale condition for functionals Frechet differentiable combined with some properties of the weak$^*$ topology.
Velocity Acoustic Oscillations on Cosmic Dawn 21 cm Power Spectrum as a Probe of Small-scale Density Fluctuations

We investigate the feasibility of using the velocity acoustic oscillations (VAO) features on the Cosmic Dawn 21 cm power spectrum to probe small-scale density fluctuations. In the standard cold dark matter (CDM) model, Population III stars form in minihalos and affect the 21 cm signal through Lyα and X-ray radiation. Such a process is modulated by the relative motion between dark matter and baryons, generating the VAO wiggles on the 21 cm power spectrum. In the fuzzy or warm dark matter models for which the number of minihalos is reduced, the VAO wiggles are weaker or even fully invisible. We investigate the wiggle features in the CDM with different astrophysical models and in different dark matter models. We find that (1) in the CDM model the relative streaming velocities can generate the VAO wiggles for broad ranges of parameters f *, ζ X , and f esc,LW ζ LW, though for different parameters the wiggles would appear at different redshifts and have different amplitudes. (2) For the axion model with m a ≲ 10−19 eV, the VAO wiggles are negligible. In the mixed model, the VAO signal is sensitive to the axion fraction. For example, the wiggles almost disappear when f a ≳ 10% for m a = 10−21 eV. Therefore, the VAO signal can be an effective indicator for small-scale density fluctuations and a useful probe of the nature of dark matter. The Square Kilometre Array-low with ∼2000 hr observation time has the ability to detect the VAO signal and constrain dark matter models.
Image based Crop Monitoring Technologies in Protected Horticulture: A Review

Future food security is a major concern of the 21st century with the growing global population and climate changes. In addressing these challenges, protected cropping ensures food production year-round and increases crop production per land area by controlling environment conditions. Maintaining the growth and health of crops in these facilities is essential to ensure optimum food production. However, this is a laborious work and is currently done manually. Image-based non-destructive plant phenotyping is an emerging research area that reduces the skilled labour cost while enhancing the monitoring of crop growth, health, and identifying phenotype-genotype relations for plant breeding. With the proliferations of protected infrastructures and targeted plants, different technologies and sensor setups are needed for image-based crop monitoring. Conveyor-type plant-to-sensor systems, bench-top or gantry-based systems are commonly found in research facilities focussing on phenotyping of small, relatively short, or movable model plants. This review examines the literature on crop monitoring and phenotyping platforms in both field and protected facilities and explains different camera technologies and their ability to extract different plant traits. The review highlights the future research directions of image-based monitoring of commercial scale protected crops where crops can be relatively tall or vertically supported under semi controlled environments, which presents new challenges and is rarely covered in the literature.
Diverse and Lifespan Facial Age Transformation Synthesis with Identity Variation Rationality Metric

Face aging has received continuous research attention over the past two decades. Although previous works on this topic have achieved impressive success, two longstanding problems remain unsettled: 1) generating diverse and plausible facial aging patterns at the target age stage; 2) measuring the rationality of identity variation between the original portrait and its syntheses with age progression or regression. In this paper, we introduce DLAT + , the first algorithm that can realize Diverse and Lifespan Age Transformation on human faces, where the diversity jointly manifests in the transformation of facial textures and shapes. Apart from the diversity mechanism embedded in the model, multiple consistency restrictions are leveraged to keep it away from counterfactual aging syntheses. Moreover, we propose a new metric to assess the rationality of Identity Deviation under Age Gaps (IDAG) between the input face and its series of age-transformed generations, which is based on statistical laws summarized from plenty of genuine face-aging data. Extensive experimental results demonstrate the uniqueness and effectiveness of our method in synthesizing diverse and perceptually reasonable faces across the whole lifetime.
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition

Fine-grained vehicle recognition (FGVR) is an essential fundamental technology for intelligent transportation systems, but very difficult because of its inherent intra-class variation. Most previous FGVR studies only focus on the intra-class variation caused by different shooting angles, positions, etc., while the intra-class variation caused by image noise has received little attention. This paper proposes a progressive multi-task anti-noise learning (PMAL) framework and a progressive multi-task distilling (PMD) framework to solve the intra-class variation problem in FGVR due to image noise. The PMAL framework achieves high recognition accuracy by treating image denoising as an additional task in image recognition and progressively forcing a model to learn noise invariance. The PMD framework transfers the knowledge of the PMAL-trained model into the original backbone network, which produces a model with about the same recognition accuracy as the PMAL-trained model, but without any additional overheads over the original backbone network. Combining the two frameworks, we obtain models that significantly exceed previous state-of-the-art methods in recognition accuracy on two widely-used, standard FGVR datasets, namely Stanford Cars, and CompCars, as well as three additional surveillance image-based vehicle-type classification datasets, namely Beijing Institute of Technology (BIT)-Vehicle, Vehicle Type Image Data 2 (VTID2), and Vehicle Images Dataset for Make Model Recognition (VIDMMR), without any additional overheads over the original backbone networks. The source code is available at https://github.com/Dichao-Liu/Anti-noise_FGVR
Low-rank matrices, tournaments, and symmetric designs

Let $\mathbf{a} = (a_{i})_{i \geq 1}$ be a sequence in a field $\mathbb{F}$, and $f \colon \mathbb{F} \times \mathbb{F} \to \mathbb{F}$ be a function such that $f(a_{i},a_{i}) \neq 0$ for all $i \geq 1$. For any tournament $T$ over $[n]$, consider the $n \times n$ symmetric matrix $M_{T}$ with zero diagonal whose $(i,j)$th entry (for$(i<j$) is $f(a_{i},a_{j})$ if $i \to j$ in $T$, and $f(a_{j},a_{i})$ if $j \to i$ in $T$. It is known (cf. Balachandran et al, Linear Algebra Appl. 658 (2023), 310-318) that if $T$ is a uniformly random tournament over $[n]$, then $\operatorname{rank}(M_{T}) \geq (\frac{1}{2}-o(1))n$ with high probability when $\operatorname{char}(\mathbb{F}) \neq 2$ and $f$ is a linear function. In this paper, we investigate the other extremal question: how low can the ranks of such matrices be? We work with sequences $\mathbf{a}$ that take only two distinct values, so the rank of any such $n \times n$ matrix is at least $n/2$. First, we show that the rank of any such matrix depends on whether an associated bipartite graph has certain eigenvalues of high multiplicity. Using this, we show that if $f$ is linear, then there are real matrices $M_{T}(f;\mathbf{a})$ of rank at most $\frac{n}{2} + O(1)$. For rational matrices, we show that for each $\varepsilon>0$ we can find a sequence $\mathbf{a}(\varepsilon)$ for which there are matrices $M_{T}(f;\mathbf{a})$ of rank at most $(\frac{1}{2} + \varepsilon)n + O(1)$. These matrices are constructed from symmetric designs, and we also use them to produce bisection-closed families of size greater than $\lfloor 3n/2 \rfloor - 2$ for $n \leq 15$, which improves the previously best known bound (cf. Balachandran et al, Electron J. Combin. 26 (2019), #P2.40).
The Gross-Pitaevskii equation for a infinite square-well with a delta-function barrier

The Gross-Pitaevskii equation is solved by analytic methods for an external double-well potential that is an infinite square well plus a $\delta$-function central barrier. We find solutions that have the symmetry of the non-interacting Hamiltonian as well as asymmetric solutions that bifurcate from the symmetric solutions for attractive interactions and from the antisymmetric solutions for repulsive interactions. We present a variational approximation to the asymmetric state as well as an approximate numerical approach. Stability of the states is briefly considered.
Stoichiometric control of electron mobility and 2D superconductivity at LaAlO$_3$-SrTiO$_3$ interfaces

SrTiO$_3$-based conducting interfaces, which exhibit coexistence of gate-tunable 2D superconductivity and strong Rashba spin-orbit coupling (RSOC), are candidates to host topological superconductive phases. Yet, superconductivity is usually in the dirty limit, which tends to suppress nonconventional pairing and therefore challenges these expectations. Here we report on LaAlO$_3$/SrTiO$_3$ (LAO/STO) interfaces with remarkably large mobility and mean free paths comparable to the superconducting coherence length, approaching the clean limit for superconductivity. We further show that the carrier density, mobility, and formation of the superconducting condensate are controlled by the fine-tuning of La/Al chemical ratio in the LAO film. Interestingly, we find a region in the superconducting phase diagram where the critical temperature is not suppressed below the Lifshitz transition, at odds with previous experimental investigations. These findings point out the relevance of achieving a clean-limit regime to enhance the observation of unconventional pairing mechanisms in these systems
Stochastic Weakly Convex Optimization Beyond Lipschitz Continuity

This paper considers stochastic weakly convex optimization without the standard Lipschitz continuity assumption. Based on new adaptive regularization (stepsize) strategies, we show that a wide class of stochastic algorithms, including the stochastic subgradient method, preserve the $\mathcal{O} ( 1 / \sqrt{K})$ convergence rate with constant failure rate. Our analyses rest on rather weak assumptions: the Lipschitz parameter can be either bounded by a general growth function of $\|x\|$ or locally estimated through independent random samples.