Miracle-dz/UniFAR-FTUs huggingface dataset:
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models

Current speech large language models build upon discrete speech representations, which can be categorized into semantic tokens and acoustic tokens. However, existing speech tokens are not specifically designed for speech language modeling. To assess the suitability of speech tokens for building speech language models, we established the first benchmark, SLMTokBench. Our results indicate that neither semantic nor acoustic tokens are ideal for this purpose. Therefore, we propose SpeechTokenizer, a unified speech tokenizer for speech large language models. SpeechTokenizer adopts the Encoder-Decoder architecture with residual vector quantization (RVQ). Unifying semantic and acoustic tokens, SpeechTokenizer disentangles different aspects of speech information hierarchically across different RVQ layers. Furthermore, We construct a Unified Speech Language Model (USLM) leveraging SpeechTokenizer. Experiments show that SpeechTokenizer performs comparably to EnCodec in speech reconstruction and demonstrates strong performance on the SLMTokBench benchmark. Also, USLM outperforms VALL-E in zero-shot Text-to-Speech tasks. Code and models are available at https://github.com/ZhangXInFD/SpeechTokenizer/.
Flu-CNN: predicting host tropism of influenza A viruses via character-level convolutional networks

Throughout history, Influenza A viruses (IAVs) have caused significant harm and catastrophic pandemics. The presence of host barriers results in viral host tropism, where infected hosts are subject to strict restrictions due to the hindered spread of viruses across hosts. Therefore, the identification of host tropism of IAVs, particularly in humans, is crucial to preventing the cross-host transmission of avian viruses and their outbreaks in humans. Nevertheless, efficiently and effectively identifying host tropism, especially for early host susceptibility warnings based on viral genome sequences during outbreak onset, remains challenging. To address this challenge, we propose Flu-CNN, a deep neural network model based on classical character-level convolutional networks. By analyzing the genomic segments of IAVs, Flu-CNN can accurately identify the host tropism, with a particular focus on avian influenza viruses that may infect humans. According to our experimental evaluations, Flu-CNN achieved an accuracy of 99% in identifying virus hosts via only a single genomic segment, even for subtypes with a relatively small number of viral strains such as H5N1, H7N9, and H9N2. The superiority of Flu-CNN demonstrates its effectiveness in screening for critical amino acid mutations, which is important to host adaptation, and zoonotic risk prediction of viral strains. Flu-CNN is a valuable tool for identifying evolutionary characterization, monitoring potential outbreaks, and preventing epidemical spreads of IAVs, which contribute to the effective surveillance of influenza A viruses.
Space Partitioning Schemes and Algorithms for Generating Regular and Spiral Treemaps

Treemaps have been widely applied to the visualization of hierarchical data. A treemap takes a weighted tree and visualizes its leaves in a nested planar geometric shape, with sub-regions partitioned such that each sub-region has an area proportional to the weight of its associated leaf nodes. Efficiently generating visually appealing treemaps that also satisfy other quality criteria is an interesting problem that has been tackled from many directions. We present an optimization model and five new algorithms for this problem, including two divide and conquer approaches and three spiral treemap algorithms. Our optimization model is able to generate superior treemaps that could serve as a benchmark for comparing the quality of more computationally efficient algorithms. Our divide and conquer and spiral algorithms either improve the performance of their existing counterparts with respect to aspect ratio and stability or perform competitively. Our spiral algorithms also expand their applicability to a wider range of input scenarios. Four of these algorithms are computationally efficient as well with quasilinear running times and the last algorithm achieves a cubic running time. A full version of this paper with all appendices, data, and source codes is available at \anonymizeOSF{\OSFSupplementText}.
Online gallery facilitated art activities for people with dementia during the COVID-19 pandemic and beyond: A narrative review

Art activities for people with dementia have a range of therapeutic benefits including psychosocial wellbeing and enhanced quality of life. Successful art programs promote social engagement, are inclusive and empowering, and enable opportunity for people with dementia to express themselves verbally and non-verbally. The COVID-19 pandemic and associated social distancing precautions have impacted the capacity of art galleries and museums to deliver in-person programs. However, they have also provided a new opportunity. This paper explores the potential benefits, challenges, and future directions for research relating to the online delivery of gallery-facilitated art activities for people with dementia. The evidence revealed that increased digitisation of programs increased access for participants, however, the majority of the research was published before the pandemic. Nevertheless, COVID-19 has necessitated many museums and galleries to engage with people with dementia online. Future research is needed to improve the usability of online delivery platforms and a comparison of online and onsite delivery is recommended, particularly to evaluate benefits to people living in rural and remote areas where access to museums and galleries may be limited.
Latent Variable Multi-output Gaussian Processes for Hierarchical Datasets

Multi-output Gaussian processes (MOGPs) have been introduced to deal with multiple tasks by exploiting the correlations between different outputs. Generally, MOGPs models assume a flat correlation structure between the outputs. However, such a formulation does not account for more elaborate relationships, for instance, if several replicates were observed for each output (which is a typical setting in biological experiments). This paper proposes an extension of MOGPs for hierarchical datasets (i.e. datasets for which the relationships between observations can be represented within a tree structure). Our model defines a tailored kernel function accounting for hierarchical structures in the data to capture different levels of correlations while leveraging the introduction of latent variables to express the underlying dependencies between outputs through a dedicated kernel. This latter feature is expected to significantly improve scalability as the number of tasks increases. An extensive experimental study involving both synthetic and real-world data from genomics and motion capture is proposed to support our claims.
On Model-Checking Higher-Order Effectful Programs

Model-checking is one of the most powerful techniques for verifying systems and programs, which since the pioneering results by Knapik et al., Ong, and Kobayashi, is known to be applicable to functional programs with higher-order types against properties expressed by formulas of monadic second-order logic. What happens when the program in question, in addition to higher-order functions, also exhibits algebraic effects such as probabilistic choice or global store? The results in the literature range from those, mostly positive, about nondeterministic effects, to those about probabilistic effects, in the presence of which even mere reachability becomes undecidable. This work takes a fresh and general look at the problem, first of all showing that there is an elegant and natural way of viewing higher-order programs producing algebraic effects as ordinary higher-order recursion schemes. We then move on to consider effect handlers, showing that in their presence the model checking problem is bound to be undecidable in the general case, while it stays decidable when handlers have a simple syntactic form, still sufficient to capture so-called generic effects. Along the way, we hint at how a general specification language could look like, this way justifying some of the results in the literature, and deriving new ones.
Advances in wave propagation in heterogeneous earth

Preface (V.Maupin, R.S. Wu). 1. Seismic ray method: recent developments (V. Cerveny. L. Klimes, I. Psencik). 2. Introduction to mode coupling methods for surface waves (V. Maupin). 3. Boundary integral equations and boundary elements methods in elastodynamics (M. Bouchon, F. Sanchez-Sesma). 4. Generation and propagation of seismic SH waves in multilayered media with irregular interfaces (X.F. Chen). 5. One-way and one-return approximations (de Wolf approximation) for fast elastic wave modelling in complex media (R.S. Wu, X.B. Xie, X.Y. Wu). 6. Simulation of high-frequency wave propagation in complex crustal waveguides using generalized screen propagators (R.S. Wu, X.Y. Wu, X.B. Xie). 7. Spectral-element analysis in seismology (E. Chaljub, D. Komatitsch, et al.). 8. The finite-difference time-domain method for modeling of seismic wave propagation (P. Moczo, J. Robertson, L. Eisner). 9. A Lattice Boltzmann approach to acoustic-wave propagation (L. Huang). 10. Synthesis of seismogram envelopes in heterogeneous media (H. Sato, M. Fehler).
Two-Stage Violence Detection Using ViTPose and Classification Models at Smart Airports

This study introduces an innovative violence detection framework tailored to the unique requirements of smart airports, where prompt responses to violent situations are crucial. The proposed framework harnesses the power of ViTPose for human pose estimation and employs a CNN-BiLSTM network to analyse spatial and temporal information within keypoints sequences, enabling the accurate classification of violent behaviour in real-time. Seamlessly integrated within the SAAB’s SAFE (Situational Awareness for Enhanced Security) framework, the solution underwent integrated testing to ensure robust performance in real-world scenarios. The AIRTLab dataset, characterized by its high video quality and relevance to surveillance scenarios, is utilized in this study to enhance the model's accuracy and mitigate false positives. As airports face increased foot traffic in the post-pandemic era, the implementation of AI-driven violence detection systems, such as the one proposed, is paramount for improving security, expediting response times, and promoting data-informed decision-making. The implementation of this framework not only diminishes the probability of violent events but also assists surveillance teams in effectively addressing potential threats, ultimately fostering a more secure and protected aviation sector. Codes are available at: https://github.com/Asami-1/GDP.
Association Between Sexual Harassment Intervention Strategies and the Sexual Harassment Perception and Attitude of University Students in Beijing, China

Sexual harassment in universities harms the physical and psychological health and development of students. Although Chinese universities are required to implement sexual harassment interventions, few studies have estimated how well interventions are perceived by students and the association with their perceptions and attitudes toward sexual harassment. This study aims to examine the perceptions and attitudes toward sexual harassment and perceived sexual harassment interventions among university students. Further, it evaluated the association between sexual harassment interventions and sexual harassment perceptions and attitudes among university students and explored potential gender differences in the observed associations. We categorized sexual harassment interventions into three types of strategies (informal education activities, prevention mechanisms, and multiformat publicity) and designed an 8-item scale for sexual harassment perception and a 10-item scale for sexual harassment attitude. A total of 872 students were recruited from six universities in Beijing, China. Association between intervention strategies and sexual harassment perceptions and attitudes was analyzed using multiple linear regression. The results showed that 78.56% of the university students recognized all sexual harassment behaviors, and 11.58% felt angry about all sexual harassment behaviors. Male students felt angrier at same-sex harassment than female students (p < .001). Overall, the students were aware of approximately 3 of the 13 sexual harassment interventions. None of the three intervention strategies was significantly associated with perceptions of sexual harassment (p > .050). The attitude toward sexual harassment was positively associated with informal education activities (coeff = 0.055, p = .015) and multiformat publicity (coeff = 0.077, p = .030) among female students, and negatively associated with prevention mechanisms (coeff = −0.123, p = .033) among male students. Our findings imply that sexual harassment interventions are not well known among university students. Universities should develop and propagate more sexual harassment informal education activities and multiformat publicity intervention strategies and pay more attention to gender differences in intervention strategies.
Evolution of a cross-feeding interaction following a key innovation in a long-term evolution experiment with Escherichia coli

The evolution of a novel trait can profoundly change an organism’s effects on its environment, which can in turn affect the further evolution of that organism and any coexisting organisms. We examine these effects and feedbacks following the evolution of a novel function in the Long-Term Evolution Experiment (LTEE) with Escherichia coli . A characteristic feature of E. coli is its inability to grow aerobically on citrate (Cit−). Nonetheless, a Cit+ variant with this capacity evolved in one LTEE population after 31 000 generations. The Cit+ clade then coexisted stably with another clade that retained the ancestral Cit− phenotype. This coexistence was shaped by the evolution of a cross-feeding relationship based on C4-dicarboxylic acids, particularly succinate, fumarate, and malate, that the Cit+ variants release into the medium. Both the Cit− and Cit+ cells evolved to grow on these excreted resources. The evolution of aerobic growth on citrate thus led to a transition from an ecosystem based on a single limiting resource, glucose, to one with at least five resources that were either shared or partitioned between the two coexisting clades. Our findings show that evolutionary novelties can change environmental conditions in ways that facilitate diversity by altering ecosystem structure and the evolutionary trajectories of coexisting lineages.
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs

Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a suite of three generalizable robust architectural design principles: (a) optimal range for depth and width configurations, (b) preferring convolutional over patchify stem stage, and (c) robust residual block design through adopting squeeze and excitation blocks and non-parametric smooth activation functions. Through extensive experiments across a wide spectrum of dataset scales, adversarial training methods, model parameters, and network design spaces, our principles consistently and markedly improve AutoAttack accuracy: 1-3 percentage points (pp) on CIFAR-10 and CIFAR-100, and 4-9 pp on ImageNet. The code is publicly available at https://github.com/poloclub/robust-principles.
Diffusion Models for Interferometric Satellite Aperture Radar

Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models, achieving high performance in natural image generation. However, their performance relative to non-natural images, like radar-based satellite data, remains largely unknown. Generating large amounts of synthetic (and especially labelled) satellite data is crucial to implement deep-learning approaches for the processing and analysis of (interferometric) satellite aperture radar data. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue. Indeed, accelerated sampling strategies, which work well on simple image datasets like MNIST, fail on our radar datasets. We provide a simple and versatile open-source https://github.com/thomaskerdreux/PDM_SAR_InSAR_generation to train, sample and evaluate PDMs using any dataset on a single GPU.
GA-NIFS: JWST/NIRSpec IFU observations of HFLS3 reveal a dense galaxy group at z~6.3

Massive, starbursting galaxies in the early Universe represent some of the most extreme objects in the study of galaxy evolution. One such source is HFLS3 (z~6.34), which was originally identified as an extreme starburst galaxy with mild gravitational magnification ($\mu$~2.2). Here, we present new observations of HFLS3 with the JWST/NIRSpec IFU in both low (PRISM/CLEAR; R~100) and high spectral resolution (G395H/290LP; R~2700), with high spatial resolution (~0.1") and sensitivity. Thanks to the combination of the NIRSpec data and a new lensing model with accurate spectroscopic redshifts, we find that the 3"x3"field is crowded, with a lensed arc (C, $z=6.3425\pm0.0002$), two galaxies to the south (S1 and S2, $z=6.3592\pm0.0001$), two galaxies to the west (W1, $z=6.3550\pm0.0001$; W2, $z=6.3628\pm0.0001$), and two low-redshift interlopers (G1, $z=3.4806\pm0.0001$; G2, $z=2.00\pm0.01$). We present spectral fits and morpho-kinematic maps for each bright emission line from the R2700 data for all sources except G2. From a line ratio analysis, the galaxies in component C are likely powered by star formation, while we cannot rule out or confirm the presence of AGN in the other high-redshift sources. We perform gravitational lens modelling, finding evidence for a two-source composition of the lensed central object and a comparable magnification factor ($\mu$=2.1-2.4) to previous work. The projected distances and velocity offsets of each galaxy suggest that they will merge within the next ~1Gyr. Finally, we examine the dust extinction-corrected SFR(Ha) of each z>6 source, finding that the total star formation ($510\pm140$Msol/yr, magnification-corrected) is distributed across the six z~6.34-6.36 objects over a region of diameter ~11kpc. Altogether, this suggests that HFLS3 is not a single starburst galaxy, but instead is a merging system of star-forming galaxies in the Epoch of Reionisation.
Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis

Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows them to pass human cell boundaries. For applications with specified requirements regarding size, producing tailored microgels in a continuous flow reactor is advantageous because the microgel properties can be controlled tightly. However, no fully-specified mechanistic models are available for continuous microgel synthesis, as the physical properties of the included components are only studied partly. To address this gap and accelerate tailor-made microgel development, we propose a data-driven optimization in a hardware-in-the-loop approach to efficiently synthesize microgels with defined sizes. We optimize the synthesis regarding conflicting objectives (maximum production efficiency, minimum energy consumption, and the desired microgel radius) by applying Bayesian optimization via the solver ``Thompson sampling efficient multi-objective optimization'' (TS-EMO). We validate the optimization using the deterministic global solver ``McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization'' (MAiNGO) and verify three computed Pareto optimal solutions via experiments. The proposed framework can be applied to other desired microgel properties and reactor setups and has the potential of efficient development by minimizing number of experiments and modelling effort needed.
Sofia Quintero Romero: Protection and Support of Breastfeeding With a Feminist and Social Justice Lens

Sofia Quintero Romero graduated as a medical doctor at Universidad del Rosario, Bogotà, Colombia, in 1977. She spent a compulsory rural year working in a remote indigenous community in the Sierra Nevada de Santa Marta in Columbia. In 1979 she worked in Bolivia for Terre des Hommes and Oxfam, evaluating their health projects with the Aymara Indians and in the tin mines. She had to leave Colombia for political reasons and went to England, where she obtained, in 1981, an MSc in Community Health at the London School of Hygiene & Tropical Medicine (LSHTM). That’s where she met her husband, Adriano Cattaneo. She then worked in Mozambique and Nicaragua where she was in charge of maternal and child health services at the regional level. Since 1990, she devoted her time to the protection, promotion, and support of breastfeeding. Sofia obtained a PhD in Maternal and Child Health at the University of Bologna, Italy, and a Diploma in Breastfeeding Theory and Practice at the Child Health Institute in London UK. She taught hundreds of breastfeeding courses for health professionals and peer counsellors in Italy and in dozens of countries abroad, using the World Health Organization/United Nations Children’s Fund (WHO/UNICEF) manuals. In the past 15 years, she changed her approach for breastfeeding education to biological nurturing. Sofia has been a member of the International Baby Food Action Network (IBFAN) since 1992 and coordinated the Nestlé Boycott in Italy. She retired in 2018.
Quantitative Toolchain Assurance

The software bill of materials (SBOM) concept aims to include more information about a software build such as copyrights, dependencies and security references. But SBOM lacks visibility into the process for building a package. Efforts such as Supply-chain Levels for Software Artifacts (SLSA) try to remedy this by focusing on the quality of the build process. But they lack quantitative assessment of that quality. They are purely qualitative. A new form of assurance case and new technique for structuring it, called process reduction, are presented. An assurance case for a toolchain is quantitative and when structured as a process reduction can measure the strength of the toolchain via the strength of the reduction. An example is given for a simple toolchain.
Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations

We introduce Affective Visual Dialog, an emotion explanation and reasoning task as a testbed for research on understanding the formation of emotions in visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and (3) Affective emotion explanation generation based on the dialog. Our key contribution is the collection of a large-scale dataset, dubbed AffectVisDial, consisting of 50K 10-turn visually grounded dialogs as well as concluding emotion attributions and dialog-informed textual emotion explanations, resulting in a total of 27,180 working hours. We explain our design decisions in collecting the dataset and introduce the questioner and answerer tasks that are associated with the participants in the conversation. We train and demonstrate solid Affective Visual Dialog baselines adapted from state-of-the-art models. Remarkably, the responses generated by our models show promising emotional reasoning abilities in response to visually grounded conversations. Our project page is available at https://affective-visual-dialog.github. io.