Miracle-dz/UniFAR-FTUs huggingface dataset:
On Speaker Attribution with SURT

The Streaming Unmixing and Recognition Transducer (SURT) has recently become a popular framework for continuous, streaming, multi-talker speech recognition (ASR). With advances in architecture, objectives, and mixture simulation methods, it was demonstrated that SURT can be an efficient streaming method for speaker-agnostic transcription of real meetings. In this work, we push this framework further by proposing methods to perform speaker-attributed transcription with SURT, for both short mixtures and long recordings. We achieve this by adding an auxiliary speaker branch to SURT, and synchronizing its label prediction with ASR token prediction through HAT-style blank factorization. In order to ensure consistency in relative speaker labels across different utterance groups in a recording, we propose"speaker prefixing"-- appending each chunk with high-confidence frames of speakers identified in previous chunks, to establish the relative order. We perform extensive ablation experiments on synthetic LibriSpeech mixtures to validate our design choices, and demonstrate the efficacy of our final model on the AMI corpus.
STUDIES ON SPATIO-TEMPORAL DYNAMICS OF PHYTOPLANKTON IN BURULLUS LAGOON, SOUTHERN MEDITERRANEAN COAST, EGYPT

This work was carried out to shed light on the present status of the Burullus Lagoon's phytoplankton and the long-term changes that had resulted from phytoplankton abundance, community structure, dominant species and diversity indices. Phytoplankton dynamics were examined seasonally from summer 2013 to spring 2014 at twelve sampling stations represented by three basins (eastern, middle and western). Water was alkaline, well oxygenated and with an annual average salinity of 3.89 PSU. Nutrient concentrations were generally high. A total of 163 taxa from five classes were recorded; diatoms were the most diversified group. The three dominant classes: Chlorophyceae, Bacillariophyceae and Cyanobacteria had a progressively lower abundance from west to east, while species diversity showed the opposite trend. The results showed that Chlorophyceae was the dominant group in the three basins, followed by Bacillariophyceae in the eastern and western basins, while Cyanobacteria followed chlorophytes in the middle basin. Shannon-Wiener diversity index values were higher than those recorded during the previous years. Notable phytoplankton species recorded can be regarded as possible indicators for each basin. Providing evidence that phytoplankton abundance decreased from year to year and that a nuisance species such as Geitlerinema amphibium was evidently environmentally harmful, hence suggesting that a major improvement in water quality must be done.
Neuromorphic Valence and Arousal Estimation

Recognizing faces and their underlying emotions is an important aspect of biometrics. In fact, estimating emotional states from faces has been tackled from several angles in the literature. In this paper, we follow the novel route of using neuromorphic data to predict valence and arousal values from faces. Due to the difficulty of gathering event-based annotated videos, we leverage an event camera simulator to create the neuromorphic counterpart of an existing RGB dataset. We demonstrate that not only training models on simulated data can still yield state-of-the-art results in valence-arousal estimation, but also that our trained models can be directly applied to real data without further training to address the downstream task of emotion recognition. In the paper we propose several alternative models to solve the task, both frame-based and video-based.
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You

Text-to-image generation models have recently achieved astonishing results in image quality, flexibility, and text alignment and are consequently employed in a fast-growing number of applications. Through improvements in multilingual abilities, a larger community now has access to this kind of technology. Yet, as we will show, multilingual models suffer similarly from (gender) biases as monolingual models. Furthermore, the natural expectation is that these models will provide similar results across languages, but this is not the case and there are important differences between languages. Thus, we propose a novel benchmark MAGBIG intending to foster research in multilingual models without gender bias. We investigate whether multilingual T2I models magnify gender bias with MAGBIG. To this end, we use multilingual prompts requesting portrait images of persons of a certain occupation or trait (using adjectives). Our results show not only that models deviate from the normative assumption that each gender should be equally likely to be generated, but that there are also big differences across languages. Furthermore, we investigate prompt engineering strategies, i.e. the use of indirect, neutral formulations, as a possible remedy for these biases. Unfortunately, they help only to a limited extent and result in worse text-to-image alignment. Consequently, this work calls for more research into diverse representations across languages in image generators.
Resonant Graviton-Photon Conversion with Stochastic Magnetic Field in the Expanding Universe

We investigate graviton-photon oscillations sourced by cosmological magnetic fields from Gertsenshtein effect. We adopt a robust perturbative approach and we find that the conversion probability from graviton to photon can be resonantly enhanced in monochromatic, multi-chromatic and scale invariant spectrum models of stochastic magnetic field fluctuations. In addition, the expansion of the Universe acts as a decoherence factor, which demands a natural discretization scheme along the line of sight. Including also decoherence from cosmic acceleration, we find that conversion probabilities for stochastic magnetic fields are completely different than results predicted from existing magnetic domain-like models in a wide range of magnetic strengths and coherence lengths. Resonances can be tested by radio telescopes as a probe of high frequency gravitational wave sources and primordial magnetogenesis mechanisms.
Article A Doppler Radar and Conceptual Analysis of a Horizontal Longitudinal Vortex Influencing the Bastrop Complex Wildfire

The Bastrop Complex wildfire (BCW) occurred during the record-breaking Texas drought of 2011. Although the 139.03 km –2 (34 356 ac) fire burned an area much smaller than the state’s largest wildfires, it was the largest wildland fire on record in central Texas, and the resultant property losses were unprecedented in Texas history. The BCW is of meteorological interest because of its simultaneous occurrence and interaction with a transient ambient horizontal longitudinal vortex (HLV). This study presents a conceptual review of HLVs from the perspectives of both meteorology and fire behavior. A thorough Doppler radar analysis of the BCW and its interaction with a transient pre-existing HLV is provided through observations of the remotely sensed debris plume. A basic conceptual model for the phasing of a wildland fire’s circulation with an ambient HLV is presented, underscoring the advantages of using Doppler radar to diagnose near-fire environments for tactical firefighting applications.
The geometric error is less than the pollution error when solving the high-frequency Helmholtz equation with high-order FEM on curved domains

We consider the $h$-version of the finite-element method, where accuracy is increased by decreasing the meshwidth $h$ while keeping the polynomial degree $p$ constant, applied to the Helmholtz equation. Although the question"how quickly must $h$ decrease as the wavenumber $k$ increases to maintain accuracy? "has been studied intensively since the 1990s, none of the existing rigorous wavenumber-explicit analyses take into account the approximation of the geometry. In this paper we prove that for nontrapping problems solved using straight elements the geometric error is order $kh$, which is then less than the pollution error $k(kh)^{2p}$ when $k$ is large; this fact is then illustrated in numerical experiments. More generally, we prove that, even for problems with strong trapping, using degree four (in 2-d) or degree five (in 3-d) polynomials and isoparametric elements ensures that the geometric error is smaller than the pollution error for most large wavenumbers.
LLaMandement: Large Language Models for Summarization of French Legislative Proposals

This report introduces LLaMandement, a state-of-the-art Large Language Model, fine-tuned by the French government and designed to enhance the efficiency and efficacy of processing parliamentary sessions (including the production of bench memoranda and documents required for interministerial meetings) by generating neutral summaries of legislative proposals. Addressing the administrative challenges of manually processing a growing volume of legislative amendments, LLaMandement stands as a significant legal technological milestone, providing a solution that exceeds the scalability of traditional human efforts while matching the robustness of a specialized legal drafter. We release all our fine-tuned models and training data to the community.
Lubricating motion of a sphere towards a thin porous slab with Saffman slip condition

Near-contact hydrodynamic interactions between a solid sphere and a plane porous slab are investigated in the framework of lubrication theory. The size of pores in the slab is small compared with the slab thickness so that the Darcy law holds there. The slab is thin: that is, its thickness is small compared with the sphere radius. The considered problem involves a sphere translating above the slab together with a permeation flow across the slab and a uniform pressure below. The pressure is continuous across both slab interfaces and the Saffman slip condition applies on its upper interface. An extended Reynolds-like equation is derived for the pressure in the gap between the sphere and the slab. This equation is solved numerically and the drag force on the sphere is calculated therefrom for a wide range of values of the slab interface slip length and of the permeability parameter $\unicode[STIX]{x1D6FD}=24k^{\ast }R/(e\unicode[STIX]{x1D6FF}^{2})$ , where $k^{\ast }$ is the permeability, $e$ is the porous slab thickness, $R$ is the sphere radius and $\unicode[STIX]{x1D6FF}$ is the gap. Moreover, asymptotics expansions for the pressure and drag are derived for high and low $\unicode[STIX]{x1D6FD}$ . These expansions, which agree with the numerics, are also handy formulae for practical use. All results match with those of other authors in particular cases. The settling trajectory of a sphere towards a porous slab in a fluid at rest is calculated from these results and, as expected, the time for reaching the slab decays for increasing slab permeability and upper interface slip length.
On the monotonicity of discrete entropy for log-concave random vectors on Zd

We prove the following type of discrete entropy monotonicity for isotropic log-concave sums of independent identically distributed random vectors $X_1,\dots,X_{n+1}$ on $\mathbb{Z}^d$: $$ H(X_1+\cdots+X_{n+1}) \geq H(X_1+\cdots+X_{n}) + \frac{d}{2}\log{\Bigl(\frac{n+1}{n}\Bigr)} +o(1), $$ where $o(1)$ vanishes as $H(X_1) \to \infty$. Moreover, for the $o(1)$-term we obtain a rate of convergence $ O\Bigl({H(X_1)}{e^{-\frac{1}{d}H(X_1)}}\Bigr)$, where the implied constants depend on $d$ and $n$. This generalizes to $\mathbb{Z}^d$ the one-dimensional result of the second named author (2023). As in dimension one, our strategy is to establish that the discrete entropy $H(X_1+\cdots+X_{n})$ is close to the differential (continuous) entropy $h(X_1+U_1+\cdots+X_{n}+U_{n})$, where $U_1,\dots, U_n$ are independent and identically distributed uniform random vectors on $[0,1]^d$ and to apply the theorem of Artstein, Ball, Barthe and Naor (2004) on the monotonicity of differential entropy. However, in dimension $d\ge2$, more involved tools from convex geometry are needed because a suitable position is required. We show that for a log-concave function on $\mathbb{R}^d$ in isotropic position, its integral, its barycenter and its covariance matrix are close to their discrete counterparts. One of our technical tools is a discrete analogue to the upper bound on the isotropic constant of a log-concave function, which generalises a result of Bobkov, Marsiglietti and Melbourne (2022) and may be of independent interest.
Regressing Transformers for Data-efficient Visual Place Recognition

Visual place recognition is a critical task in computer vision, especially for localization and navigation systems. Existing methods often rely on contrastive learning: image descriptors are trained to have small distance for similar images and larger distance for dissimilar ones in a latent space. However, this approach struggles to ensure accurate distance-based image similarity representation, particularly when training with binary pairwise labels, and complex re-ranking strategies are required. This work introduces a fresh perspective by framing place recognition as a regression problem, using camera field-of-view overlap as similarity ground truth for learning. By optimizing image descriptors to align directly with graded similarity labels, this approach enhances ranking capabilities without expensive re-ranking, offering data-efficient training and strong generalization across several benchmark datasets.
Truss topology design under harmonic loads: Peak power minimization with semidefinite programming

Designing lightweight yet stiff vibrating structures has been a long-standing objective in structural optimization. Here, we consider the optimization of a structural design subject to forces in the form of harmonic oscillations. We develop a unifying framework for the minimization of compliance and peak-power functions, while avoiding the assumptions of single-frequency and in-phase loads. We utilize the notion of semidefinite representable (SDr) functions and show that for compliance such a representation is immediate, while for peak power, the SDP representation is non-trivial. Subsequently, we propose convex relaxations of the minimization of an SDr function subject to the equilibrium equation as a constraint, and show promising computational results on well-known instances of Heidari et al. and others.
Land Use Sustainability

This book is about sustainability of land use practices in Chittagong Hill Tracts (CHTs), Bangladesh. Shifting cultivation, agro-forestry and plough cultivation are the prominant land use practices in CHTs. However, long lasting debate among government, communities, NGOs and scholars regarding sustainability level of the land use practices has been a major problem. In this book, the author has developed criteria, indicators and verifiers under the three core sustainability principles i.e. conservation of biodiversity, intergenerational equity and the precautionary principle for each of the land use practices to facilitate the assessment of land use sustainability. Sustainability assessment implies that none of the prominent land use practices are truly sustainable although agro-forestry has the potential to become a sustainable land use practice.
Sliced Wasserstein with Random-Path Projecting Directions

Slicing distribution selection has been used as an effective technique to improve the performance of parameter estimators based on minimizing sliced Wasserstein distance in applications. Previous works either utilize expensive optimization to select the slicing distribution or use slicing distributions that require expensive sampling methods. In this work, we propose an optimization-free slicing distribution that provides a fast sampling for the Monte Carlo estimation of expectation. In particular, we introduce the random-path projecting direction (RPD) which is constructed by leveraging the normalized difference between two random vectors following the two input measures. From the RPD, we derive the random-path slicing distribution (RPSD) and two variants of sliced Wasserstein, i.e., the Random-Path Projection Sliced Wasserstein (RPSW) and the Importance Weighted Random-Path Projection Sliced Wasserstein (IWRPSW). We then discuss the topological, statistical, and computational properties of RPSW and IWRPSW. Finally, we showcase the favorable performance of RPSW and IWRPSW in gradient flow and the training of denoising diffusion generative models on images.
Vectorized implementation of primal hybrid FEM in MATLAB

We present efficient MATLAB implementations of the lowest-order primal hybrid finite element method (FEM) for linear second-order elliptic and parabolic problems with mixed boundary conditions in two spatial dimensions. We employ the Crank-Nicolson finite difference scheme for the complete discrete setup of the parabolic problem. All the codes presented are fully vectorized using matrix-wise array operations. Numerical experiments are conducted to show the performance of the software.
Rethinking the Producer-Consumer Relationship in Modern DRAM-Based Systems

Generational improvements to commodity DRAM throughout half a century have long solidified its prevalence as main memory across the computing industry. However, overcoming today's DRAM technology scaling challenges requires new solutions driven by both DRAM producers and consumers. In this paper, we observe that the separation of concerns between producers and consumers specified by industry-wide DRAM standards is becoming a liability to progress in addressing scaling-related concerns. To understand the problem, we study four key directions for overcoming DRAM scaling challenges using system-memory cooperation: (i) improving memory access latencies; (ii) reducing DRAM refresh overheads; (iii) securely defending against the RowHammer vulnerability; and (iv) addressing worsening memory errors. We find that the single most important barrier to advancement in all four cases is the consumer's lack of insight into DRAM reliability. Based on an analysis of DRAM reliability testing, we recommend revising the separation of concerns to incorporate limited information transparency between producers and consumers. Finally, we propose adopting this revision in a two-step plan, starting with immediate information release through crowdsourcing and publication and culminating in widespread modifications to DRAM standards.
An Efficient, High-Rate Scheme for Private Information Retrieval over the Gaussian MAC

This paper addresses the challenge of the private information retrieval (PIR) problem wherein there are $N$ replicated non-communicating databases containing the same $M$ messages and a user who wants to retrieve one of the messages without revealing the wanted message's index to the databases. In addition, we assume a block-fading additive white Gaussian noise multiple access channel (AWGN MAC) linking the user and the databases. Shmuel's contribution \cite{shmuel2021private}, presenting a joint channel-PIR scheme utilizing the C\&F protocol, has shown the potential of a joint channel-PIR scheme over a separated scheme. In this paper, we propose an improved joint channel-PIR approach tailored for the PIR problem with $N$ databases over a block-fading AWGN. Unlike the C\&F protocol, our scheme offers reduced computational complexity while improving the scaling laws governing the achievable rate. Our achievable rate scales with the number of databases $N$ and the power $P$ similarly to the channel capacity without the privacy constraint and outperforms the C\&F-based approach. Furthermore, our analysis demonstrates that our improved rate exhibits only a finite gap from the channel capacity of one bit as $N$ increases.
A Class-aware Optimal Transport Approach with Higher-Order Moment Matching for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. In this paper, we introduce a novel approach called class-aware optimal transport (OT), which measures the OT distance between a distribution over the source class-conditional distributions and a mixture of source and target data distribution. Our class-aware OT leverages a cost function that determines the matching extent between a given data example and a source class-conditional distribution. By optimizing this cost function, we find the optimal matching between target examples and source class-conditional distributions, effectively addressing the data and label shifts that occur between the two domains. To handle the class-aware OT efficiently, we propose an amortization solution that employs deep neural networks to formulate the transportation probabilities and the cost function. Additionally, we propose minimizing class-aware Higher-order Moment Matching (HMM) to align the corresponding class regions on the source and target domains. The class-aware HMM component offers an economical computational approach for accurately evaluating the HMM distance between the two distributions. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms existing state-of-the-art baselines.
NoFunEval: Funny How Code LMs Falter on Requirements Beyond Functional Correctness

Existing evaluation benchmarks of language models of code (code LMs) focus almost exclusively on whether the LMs can generate functionally-correct code. In real-world software engineering, developers think beyond functional correctness. They have requirements on"how"a functionality should be implemented to meet overall system design objectives like efficiency, security, and maintainability. They would also trust the code LMs more if the LMs demonstrate robust understanding of requirements and code semantics. We propose a new benchmark NoFunEval to evaluate code LMs on non-functional requirements and simple classification instances for both functional and non-functional requirements. We propose a prompting method, Coding Concepts (CoCo), as a way for a developer to communicate the domain knowledge to the LMs. We conduct an extensive evaluation of twenty-two code LMs. Our finding is that they generally falter when tested on our benchmark, hinting at fundamental blindspots in their training setups. Surprisingly, even the classification accuracy on functional-correctness instances derived from the popular HumanEval benchmark is low, calling in question the depth of their comprehension and the source of their success in generating functionally-correct code in the first place. We will release our benchmark and evaluation scripts publicly at https://aka.ms/NoFunEval.