Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotational transitions of C3N− induced by collision with H2

Rate coeﬃcients for state-to-state rotational transitions of C 3 N − induced by collision with both ortho – and para –H 2 are presented. Quantum calculations are performed at the close-coupling level using the uniform J -shifting method and a new potential energy surface specially developed for this purpose. Rate coeﬃcients are obtained for state-to-state transitions among the ﬁrst 28 rotational levels of C 3 N − and for temperatures ranging from 10 to 300 K. The para – H 2 rate coeﬃcients are shown to diﬀer strongly from the mass-scaled He rate coeﬃcients previously computed. The ortho – and para –H 2 rate coeﬃcients are very similar, as it was already observed for the rotational transitions of CN − and C 6 H − . There is also an unexpected similarity between the rates coeﬃcients of the rotational de-excitations of CN − , C 3 N − , and C 6 H − . This may open to door to quantitative extrapolations of the rate coeﬃcients for larger anions.
Promotion of diced cartilage survival and regeneration with grafting of small intestinal submucosa loaded with urine‐derived stem cells

Abstract Cartilage absorption and calcification are prone to occur after the implantation of diced cartilage wrapped with autologous materials, as well as prolong the operation time, aggravate surgical trauma and postoperative pain during the acquisition process. Small intestinal submucosa (SIS) has suitable toughness and excellent degradability, which has been widely used in the clinic. Urine‐derived stem cells (USCs), as a new type of stem cells, have multi‐directional differentiation potential. In this study, we attempt to create the tissue engineering membrane material, termed USCs‐SIS (U‐SIS), and wrap the diced cartilage with it, assuming that they can promote the survival and regeneration of cartilage. In this study, after co‐culture with the SIS and U‐SIS, the proliferation, migration and chondrogenesis ability of the auricular‐derived chondrocyte cells (ACs) were significantly improved. Further, the expression levels of chondrocyte phenotype‐related genes were up‐regulated, whilst that of dedifferentiated genes was down‐regulated. The signal pathway proteins (Wnt3a and Wnt5a) were also participated in regulation of chondrogenesis. In vivo, compared with perichondrium, the diced cartilage wrapped with the SIS and U‐SIS attained higher survival rate, less calcification and absorption in both short and long terms. Particularly, USCs promoted chondrogenesis and modulated local immune responses via paracrine pathways. In conclusion, SIS have the potential to be a new choice of membrane material for diced cartilage graft. U‐SIS can enhance survival and regeneration of diced cartilage as a bioactive membrane material.
Multi-dimensional reflected McKean-Vlasov BSDEs with the obstacle depending on both the first unknown and its distribution

The paper studies a multi-dimensional mean-field reflected backward stochastic differential equation (MF-RBSDE) with a reflection constraint depending on both the value process $Y$ and its distribution $[Y]$. We establish the existence, uniqueness and the stability of the solution of MF-RBSDE. We also investigate the associated interacting particle systems of RBSDEs and prove a propagation of chaos result. Lastly, we investigate the relationship between MF-RBSDE and an obstacle problem for partial differential equations in Wasserstein space within a Markovian framework. Our work provides a connection between the work of Briand et al. (2020) on BSDEs with normal reflection in law and the work of Gegout-Petit and Pardoux (1996) on classical multi-dimensional RBSDEs.
Pendular mechanism determinants and elastic energy usage during walking of obese and non‐obese children

The mechanical and metabolic responses of walking by obese children are not yet well understood. The objectives of this study were (1) to compare the pendular mechanism (recovery, phase shift by α and β values, and ratio between forward and vertical mechanical work), the maximum possible elastic energy usage and the bilateral coordination during walking between non‐obese and obese children, and (2) to verify if the bilateral coordination could contribute to understanding the pendular mechanism and elastic energy usage in these populations. Nine obese (six female, 8.7 ± 0.5 years, 1.38 ± 0.04 m, 44.4 ± 6.3 kg and 24.1 ± 3.50 kg/m2) and eight non‐obese (four female, 7.4 ± 0.5 years, 1.31 ± 0.08 m, 26.6 ± 2.1 kg and 16.4 ± 1.40 kg/m2) children were analysed during walking on a treadmill at five speeds: 1, 2, 3, 4 and 5 km/h. The results indicated that although the mechanical energy response of the centre of mass during walking is similar between obese and non‐obese children, the obese children showed a lower pendulum‐like mechanism and greater elastic energy usage during level walking. Therefore, obese children seem to use more elastic energy during walking compared to non‐obese children, which may be related to their apparent higher positive work production during the double support phase. Finally, bilateral coordination presented high values at slow speeds in both groups and requires further attention due to its association with falls.
Bridging the Spoof Gap: A Unified Parallel Aggregation Network for Voice Presentation Attacks

Automatic Speaker Verification (ASV) systems are increasingly used in voice bio-metrics for user authentication but are susceptible to logical and physical spoofing attacks, posing security risks. Existing research mainly tackles logical or physical attacks separately, leading to a gap in unified spoofing detection. Moreover, when existing systems attempt to handle both types of attacks, they often exhibit significant disparities in the Equal Error Rate (EER). To bridge this gap, we present a Parallel Stacked Aggregation Network that processes raw audio. Our approach employs a split-transform-aggregation technique, dividing utterances into convolved representations, applying transformations, and aggregating the results to identify logical (LA) and physical (PA) spoofing attacks. Evaluation of the ASVspoof-2019 and VSDC datasets shows the effectiveness of the proposed system. It outperforms state-of-the-art solutions, displaying reduced EER disparities and superior performance in detecting spoofing attacks. This highlights the proposed method's generalizability and superiority. In a world increasingly reliant on voice-based security, our unified spoofing detection system provides a robust defense against a spectrum of voice spoofing attacks, safeguarding ASVs and user data effectively.
SlimPajama-DC: Understanding Data Combinations for LLM Training

This paper aims to understand the impacts of various data combinations (e.g., web text, wikipedia, github, books) on the training of large language models using SlimPajama. SlimPajama is a rigorously deduplicated, multi-source dataset, which has been refined and further deduplicated to 627B tokens from the extensive 1.2T tokens RedPajama dataset contributed by Together. We've termed our research as SlimPajama-DC, an empirical analysis designed to uncover fundamental characteristics and best practices associated with employing SlimPajama in the training of large language models. During our research with SlimPajama, two pivotal observations emerged: (1) Global deduplication vs. local deduplication. We analyze and discuss how global (across different sources of datasets) and local (within the single source of dataset) deduplications affect the performance of trained models. (2) Proportions of high-quality/highly-deduplicated multi-source datasets in the combination. To study this, we construct six configurations of SlimPajama dataset and train individual ones using 1.3B Cerebras-GPT model with Alibi and SwiGLU. Our best configuration outperforms the 1.3B model trained on RedPajama using the same number of training tokens by a significant margin. All our 1.3B models are trained on Cerebras 16$\times$ CS-2 cluster with a total of 80 PFLOP/s in bf16 mixed precision. We further extend our discoveries (such as increasing data diversity is crucial after global deduplication) on a 7B model with large batch-size training. Our models and the separate SlimPajama-DC datasets are available at: https://huggingface.co/MBZUAI-LLM and https://huggingface.co/datasets/cerebras/SlimPajama-627B.
Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Pictures

We present an exploration of machine learning architectures for predicting brain responses to realistic images on occasion of the Algonauts Challenge 2023. Our research involved extensive experimentation with various pretrained models. Initially, we employed simpler models to predict brain activity but gradually introduced more complex architectures utilizing available data and embeddings generated by large-scale pre-trained models. We encountered typical difficulties related to machine learning problems, e.g. regularization and overfitting, as well as issues specific to the challenge, such as difficulty in combining multiple input encodings, as well as the high dimensionality, unclear structure, and noisy nature of the output. To overcome these issues we tested single edge 3D position-based, multi-region of interest (ROI) and hemisphere predictor models, but we found that employing multiple simple models, each dedicated to a ROI in each hemisphere of the brain of each subject, yielded the best results - a single fully connected linear layer with image embeddings generated by CLIP as input. While we surpassed the challenge baseline, our results fell short of establishing a robust association with the data.
Strong Carbon Features and a Red Early Color in the Underluminous Type Ia SN 2022xkq

We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining Type Ia supernova (SN Ia) in NGC 1784 (D ≈ 31 Mpc), from <1 to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion, which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess that is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived C i 1.0693 μm feature that persists until 5 days post-maximum. We also detect C ii λ6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic data set of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes that produce faint SNe Ia.
Gravitational redshift revisited: inertia, geometry, and charge

Gravitational redshift effects undoubtedly exist; moreover, the experimental setups which confirm the existence of these effects - the most famous of which being the Pound-Rebka experiment - are well-known. Nonetheless - and perhaps surprisingly - there remains a great deal of confusion in the literature regarding what these experiments really establish. Our goal in the present article is to clarify these issues, in three concrete ways. First, although (i) Brown and Read (2016) are correct to point out that, given their sensitivity, the outcomes of experimental setups such as the original Pound-Rebka configuration can be accounted for using solely the machinery of accelerating frames in special relativity (barring some subtleties due to the Rindler spacetime necessary to model the effects rigorously), nevertheless (ii) an explanation of the results of more sensitive gravitational redshift outcomes does in fact require more. Second, although typically this 'more' is understood as the invocation of spacetime curvature within the framework of general relativity, in light of the so-called 'geometric trinity' of gravitational theories, in fact curvature is not necessary to explain even these results. Thus (a) one can explain the results of these experiments using only the resources of special relativity, and (b) even when one cannot, one need not invoke spacetime curvature. And third: while one might think that the absence of gravitational redshift effects would imply that spacetime is flat, this can be called into question given the possibility of the 'shielding' of gravitational effects by charge. This argument is shown to be valid and both attractive forces as well as redshift effects can be effectively shielded (and even be repulsive or blueshifted) in the charged setting. Thus, it is not the case that the absence of gravitational effects implies a Minkowskian spacetime setting.
Signature of Non-Minimal Scalar-Gravity Coupling with an Early Matter Domination on the Power Spectrum of Gravitational Waves

The signal strength of primordial gravitational waves experiencing an epoch of early scalar domination is reduced with respect to radiation domination. In this paper, we demonstrate that the specific pattern of this reduction is sensitive to the coupling between the dominant field and gravity. When this coupling is zero, the impact of early matter domination on gravitational waves is solely attributed to the alteration of the Hubble parameter and the scale factor. In the presence of non-zero couplings, on the other hand, the evolution of primordial gravitational waves is directly affected as well, resulting in a distinct step-like feature in the power spectrum of the gravitational wave as a function of frequency. This feature serves as a smoking gun signature of this model. In this paper, we provide an analytical expression of the power spectrum that illustrates the dependence of power spectrum on model parameters and initial conditions. Furthermore, we provide analytical relations that specify the frequency interval in which the step occurs. We compare the analytical estimates with numerical analysis and show they match well.
Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK

Lineage dynamics The scale of genome-sequencing efforts for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. The United Kingdom has contributed more than 26,000 sequences to this effort. This volume of data allowed du Plessis et al. to develop a detailed picture of the influxes of virus reaching U.K. shores as the pandemic developed during the first months of 2020 (see the Perspective by Nelson). Before lockdown, high travel volumes and few restrictions on international travel allowed more than 1000 lineages to become established. This accelerated local epidemic growth and exceeded contact tracing capacity. The authors were able to quantify the abundance, size distribution, and spatial range of the lineages that were transmitted. Transmission was highly heterogeneous, favoring some lineages that became widespread and subsequently harder to eliminate. This dire history indicates that rapid or even preemptive responses should have been used as they were elsewhere where containment was successful. Science, this issue p. 708; see also p. 680 Large-scale virus genome sequencing reveals the genetic structure and importation dynamics of a national COVID-19 epidemic. The United Kingdom’s COVID-19 epidemic during early 2020 was one of world’s largest and was unusually well represented by virus genomic sampling. We determined the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes, including 26,181 from the UK sampled throughout the country’s first wave of infection. Using large-scale phylogenetic analyses combined with epidemiological and travel data, we quantified the size, spatiotemporal origins, and persistence of genetically distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown tended to be larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, whereas lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.
Memory-based Controllers for Efficient Data-driven Control of Soft Robots

Controller design for soft robots is challenging due to nonlinear deformation and high degrees of freedom of flexible material. The data-driven approach is a promising solution to the controller design problem for soft robots. However, the existing data-driven controller design methods for soft robots suffer from two drawbacks: (i) they require excessively long training time, and (ii) they may result in potentially inefficient controllers. This paper addresses these issues by developing two memory-based controllers for soft robots that can be trained in a data-driven fashion: the finite memory controller (FMC) approach and the long short-term memory (LSTM) based approach. An FMC stores the tracking errors at different time instances and computes the actuation signal according to a weighted sum of the stored tracking errors. We develop three reinforcement learning algorithms for computing the optimal weights of an FMC using the Q-learning, soft actor-critic, and deterministic policy gradient (DDPG) methods. An LSTM-based controller is composed of an LSTM network where the inputs of the network are the robot's desired configuration and current configuration. The LSTM network computes the required actuation signal for the soft robot to follow the desired configuration. We study the performance of the proposed approaches in controlling a soft finger where, as benchmarks, we use the existing reinforcement learning (RL) based controllers and proportional-integral-derivative (PID) controllers. Our numerical results show that the training time of the proposed memory-based controllers is significantly shorter than that of the classical RL-based controllers. Moreover, the proposed controllers achieve a smaller tracking error compared with the classical RL algorithms and the PID controller.
Minimum width for universal approximation using ReLU networks on compact domain

It has been shown that deep neural networks of a large enough width are universal approximators but they are not if the width is too small. There were several attempts to characterize the minimum width $w_{\min}$ enabling the universal approximation property; however, only a few of them found the exact values. In this work, we show that the minimum width for $L^p$ approximation of $L^p$ functions from $[0,1]^{d_x}$ to $\mathbb R^{d_y}$ is exactly $\max\{d_x,d_y,2\}$ if an activation function is ReLU-Like (e.g., ReLU, GELU, Softplus). Compared to the known result for ReLU networks, $w_{\min}=\max\{d_x+1,d_y\}$ when the domain is $\smash{\mathbb R^{d_x}}$, our result first shows that approximation on a compact domain requires smaller width than on $\smash{\mathbb R^{d_x}}$. We next prove a lower bound on $w_{\min}$ for uniform approximation using general activation functions including ReLU: $w_{\min}\ge d_y+1$ if $d_x<d_y\le2d_x$. Together with our first result, this shows a dichotomy between $L^p$ and uniform approximations for general activation functions and input/output dimensions.
Language Modeling Is Compression

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model.
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval

Open-domain table question answering aims to provide answers to a question by retrieving and extracting information from a large collection of tables. Existing studies of open-domain table QA either directly adopt text retrieval methods or consider the table structure only in the encoding layer for table retrieval, which may cause syntactical and structural information loss during table scoring. To address this issue, we propose a syntax- and structure-aware retrieval method for the open-domain table QA task. It provides syntactical representations for the question and uses the structural header and value representations for the tables to avoid the loss of fine-grained syntactical and structural information. Then, a syntactical-to-structural aggregator is used to obtain the matching score between the question and a candidate table by mimicking the human retrieval process. Experimental results show that our method achieves the state-of-the-art on the NQ-tables dataset and overwhelms strong baselines on a newly curated open-domain Text-to-SQL dataset.
Persistence of Rademacher-type and Sobolev-to-Lipschitz properties

We consider the Rademacher- and Sobolev-to-Lipschitz-type properties for arbitrary quasi-regular strongly local Dirichlet spaces. We discuss the persistence of these properties under localization, globalization, transfer to weighted spaces, tensorization, and direct integration. As byproducts we obtain: necessary and sufficient conditions to identify a quasi-regular strongly local Dirichlet form on an extended metric topological $\sigma$-finite possibly non-Radon measure space with the Cheeger energy of the space; the tensorization of intrinsic distances; the tensorization of the Varadhan short-time asymptotics.
Wreath products and cascaded FSRs

We show that the transition function of the cascaded connection of two FSRs can be viewed as a wreath product element. This allows us to study periods of cascaded connections with algebraic methods, obtaining both a general, nontrivial upper bound on the maximum period of a cascaded connection and a complete, explicit understanding of the periods in the important case of the cascaded connection of an $n$-dimensional De Bruijn sequence into an $m$-dimensional linear FSR.
Methanol on the rocks: green rust transformation promotes the oxidation of methane

Shared coordination geometries between metal ions within reactive minerals and enzymatic metal cofactors hints at mechanistic and possibly evolutionary homology between particular abiotic chemical mineralogies and biological metabolism. The octahedral coordination of reactive Fe2+/3+ minerals such as green rusts, endemic to anoxic sediments and the early Earth's oceans, mirrors the di-iron reaction centre of soluble methane monooxygenase (sMMO), responsible for methane oxidation in methanotrophy. We show that methane oxidation occurs in tandem with the oxidation of green rust to lepidocrocite and magnetite, mimicking radical-mediated methane oxidation found in sMMO to yield not only methanol but also halogenated hydrocarbons in the presence of seawater. This naturally occurring geochemical pathway for CH4 oxidation elucidates a previously unidentified carbon cycling mechanism in modern and ancient environments and reveals clues into mineral-mediated reactions in the synthesis of organic compounds necessary for the emergence of life.