Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Leave-one-out Cross Validation for Regression with $\ell_1$ Regularizers (extended version)

The out-of-sample error (OO) is the main quantity of interest in risk estimation and model selection. Leave-one-out cross validation (LO) offers a (nearly) distribution-free yet computationally demanding approach to estimate OO. Recent theoretical work showed that approximate leave-one-out cross validation (ALO) is a computationally efficient and statistically reliable estimate of LO (and OO) for generalized linear models with differentiable regularizers. For problems involving non-differentiable regularizers, despite significant empirical evidence, the theoretical understanding of ALO's error remains unknown. In this paper, we present a novel theory for a wide class of problems in the generalized linear model family with non-differentiable regularizers. We bound the error |ALO - LO| in terms of intuitive metrics such as the size of leave-i-out perturbations in active sets, sample size n, number of features p and regularization parameters. As a consequence, for the $\ell_1$-regularized problems, we show that |ALO - LO| goes to zero as p goes to infinity while n/p and SNR are fixed and bounded.
Stability of Homomorphisms, Coverings and Cocycles I: Equivalence

This paper is motivated by recent developments in group stability, high dimensional expansion, local testability of error correcting codes and topological property testing. In Part I, we formulate and motivate three stability problems: 1. Homomorphism stability: Are almost homomorphisms close to homomorphisms? 2. Covering stability: Are almost coverings of a cell complex close to genuine coverings of it? 3. Cocycle stability: Are 1-cochains whose coboundary is small close to 1-cocycles? We then prove that these three problems are equivalent.
Conditionally Combining Robot Skills using Large Language Models

This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call"Language-World,"which allows a large language model to operate in a simulated robotic environment using semi-structured natural language queries and scripted skills described using natural language. By using the same set of tasks as Meta-World, Language-World results can be easily compared to Meta-World results, allowing for a point of comparison between recent methods using Large Language Models (LLMs) and those using Deep Reinforcement Learning. Second, we introduce a method we call Plan Conditioned Behavioral Cloning (PCBC), that allows finetuning the behavior of high-level plans using end-to-end demonstrations. Using Language-World, we show that PCBC is able to achieve strong performance in a variety of few-shot regimes, often achieving task generalization with as little as a single demonstration. We have made Language-World available as open-source software at https://github.com/krzentner/language-world/.
SPLUS J142445.34–254247.1: An r-process–enhanced, Actinide-boost, Extremely Metal-poor Star Observed with GHOST

We report on a chemo-dynamical analysis of SPLUS J142445.34−254247.1 (SPLUS J1424−2542), an extremely metal-poor halo star enhanced in elements formed by the rapid neutron-capture process (r-process). This star was first selected as a metal-poor candidate from its narrowband S-PLUS photometry and followed up spectroscopically in medium resolution with Gemini-South/GMOS, which confirmed its low-metallicity status. High-resolution spectroscopy was gathered with GHOST at Gemini-South, allowing for the determination of the chemical abundances for 36 elements, from carbon to thorium. At [Fe/H] = −3.39, SPLUS J1424−2542 is one of the lowest-metallicity stars with measured Th and has the highest logϵ(Th/Eu) observed to date, making it part of the “actinide-boost” category of r-process–enhanced stars. The analysis presented here suggests that the gas cloud from which SPLUS J1424−2542 formed must have been enriched by at least two progenitor populations. The light-element (Z ≤ 30) abundance pattern is consistent with the yields from a supernova explosion of metal-free stars with 11.3–13.4 M ⊙, and the heavy-element (Z ≥ 38) abundance pattern can be reproduced by the yields from a neutron star merger (1.66 M ⊙ and 1.27 M ⊙) event. A kinematical analysis also reveals that SPLUS J1424−2542 is a low-mass, old halo star with a likely in situ origin, not associated with any known early merger events in the Milky Way.
MO-YOLO: End-to-End Multiple-Object Tracking Method with YOLO and MOTR

This paper aims to address critical issues in the field of Multi-Object Tracking (MOT) by proposing an efficient and computationally resource-efficient end-to-end multi-object tracking model, named MO-YOLO. Traditional MOT methods typically involve two separate steps: object detection and object tracking, leading to computational complexity and error propagation issues. Recent research has demonstrated out-standing performance in end-to-end MOT models based on Transformer architectures, but they require substantial hardware support. MO-YOLO combines the strengths of YOLO and RT-DETR models to construct a high-efficiency, lightweight, and resource-efficient end-to-end multi-object tracking network, offering new opportunities in the multi-object tracking domain. On the MOT17 dataset, MOTR[22] requires training with 8 GeForce 2080 Ti GPUs for 4 days to achieve satisfactory results, while MO-YOLO only requires 1 GeForce 2080 Ti GPU and 12 hours of training to achieve comparable performance.
Observation of atmospheric nitrogen deposition into forestland

From 2004 November to 2005 October, the inputting of nitrogen fluxes into forestland from atmospheric deposition and its relative contributions of three kinds of physical form (gas, particulate and rainwater) and four kinds of nitrogen compounds (NH3, NH4+-N, NO3--N and NO2) were studied with study object of broadleaf forest (Quercus chenii) at Forest Micrometeorological Experiment Sub-Station, Experiment Station of Red Soil Ecology, Chinese Academy of Sciences. Total amount of atmospheric nitrogen deposition was 82.8 kg/(hm2·a), in which dry deposition occupied 67%, NH3 was the main contribution, occupying 82% of the dry deposition, and 56% of the total nitrogen deposition amount. Reduced nitrogen compound (NH3, NH4+-N) occupied 65% of the total nitrogen deposition amount. Therefore, the contribution of atmospheric nitrogen deposition should be considered when studying the forest soil acidification and management.
Beyond MLE: Convex Learning for Text Generation

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, especially for closed-ended text generation tasks like machine translation. In these tasks, the goal of model is to generate the most appropriate response, which does not necessarily require it to estimate the entire data distribution with MLE. To this end, we propose a novel class of training objectives based on convex functions, which enables text generation models to focus on highly probable outputs without having to estimate the entire data distribution. We investigate the theoretical properties of the optimal predicted distribution when applying convex functions to the loss, demonstrating that convex functions can sharpen the optimal distribution, thereby enabling the model to better capture outputs with high probabilities. Experiments on various text generation tasks and models show the effectiveness of our approach. It enables autoregressive models to bridge the gap between greedy and beam search, and facilitates the learning of non-autoregressive models with a maximum improvement of 9+ BLEU points. Moreover, our approach also exhibits significant impact on large language models (LLMs), substantially enhancing their generative capability on various tasks. Source code is available at \url{https://github.com/ictnlp/Convex-Learning}.
Real-time Neonatal Chest Sound Separation using Deep Learning

Auscultation for neonates is a simple and non-invasive method of providing diagnosis for cardiovascular and respiratory disease. Such diagnosis often requires high-quality heart and lung sounds to be captured during auscultation. However, in most cases, obtaining such high-quality sounds is non-trivial due to the chest sounds containing a mixture of heart, lung, and noise sounds. As such, additional preprocessing is needed to separate the chest sounds into heart and lung sounds. This paper proposes a novel deep-learning approach to separate such chest sounds into heart and lung sounds. Inspired by the Conv-TasNet model, the proposed model has an encoder, decoder, and mask generator. The encoder consists of a 1D convolution model and the decoder consists of a transposed 1D convolution. The mask generator is constructed using stacked 1D convolutions and transformers. The proposed model outperforms previous methods in terms of objective distortion measures by 2.01 dB to 5.06 dB in the artificial dataset, as well as computation time, with at least a 17-time improvement. Therefore, our proposed model could be a suitable preprocessing step for any phonocardiogram-based health monitoring system.
VST-A: A Foundationally Sound Annotation Verifier

Program verifiers for imperative languages such as C may be annotation-based, in which assertions and invariants are put into source files and then checked, or tactic-based, where proof scripts separate from programs are interactively developed in a proof assistant such as Coq. Annotation verifiers have been more automated and convenient, but some interactive verifiers have richer assertion languages and formal proofs of soundness. We present VST-A, an annotation verifier that uses the rich assertion language of VST, leverages the formal soundness proof of VST, but allows users to describe functional correctness proofs intuitively by inserting assertions. VST-A analyzes control flow graphs, decomposes every C function into control flow paths between assertions, and reduces program verification problems into corresponding straightline Hoare triples. Compared to existing foundational program verification tools like VST and Iris, in VST-A such decompositions and reductions can nonstructural, which makes VST-A more flexible to use. VST-A's decomposition and reduction is defined in Coq, proved sound in Coq, and computed call-by-value in Coq. The soundness proof for reduction is totally logical, independent of the complicated semantic model (and soundness proof) of VST's Hoare triple. Because of the rich assertion language, not all reduced proof goals can be automatically checked, but the system allows users to prove residual proof goals using the full power of the Coq proof assistant.
The interplay of self-acceptance, social comparison and attributional style in adolescent mental health: cross-sectional study

Background Adolescence is a pivotal stage vulnerable to mental health problems such as anxiety and depression. Although self-acceptance and social comparison are known to affect adolescent mental health, their interactive and moderating roles are not fully understood. Aims To explore the role of self-acceptance, social comparison and attributional style in predicting these mental health outcomes among adolescents in clinical settings. Method A cross-sectional study was conducted on a sample of 242 adolescents. Participants completed measures assessing self-acceptance, social comparison, attributional style and mental health outcomes (depression and anxiety). Mediation models and multi-group analysis were used to examine the relationships among these variables. Results Our findings demonstrated a significant relationship between self-acceptance, social comparison, depression and anxiety (rs = 0.32–0.88). Specifically, lower self-acceptance and higher social comparison were associated with higher levels of depression and anxiety. Additionally, individuals with external attributional tendencies reported higher depression (Cohen's d = 0.61) and anxiety (d = 0.58) compared with those with internal tendencies. Mediation modelling showed that social comparison is a mediator between self-acceptance and depression (effect size −0.04, 95% CI −0.08 to −0.01) and anxiety (effect size −0.06, 95% CI −0.10 to −0.02). Crucially, multi-group analysis showed that the impact of social comparison on mental health outcomes varied significantly based on attributional style. Conclusions These findings underscore the importance of considering self-acceptance, social comparison and attributional style in understanding and addressing mental health challenges during adolescence. This could inform the development of targeted interventions to promote mental health and well-being among adolescents. However, further research is needed to confirm these findings in diverse populations and to explore the underlying mechanisms in greater detail.
Role of Seasonal Importation and Random Genetic Drift on Selection for Drug-Resistant Genotypes of Plasmodium falciparum in High Transmission Settings

Historically Plasmodium falciparum has followed a pattern of drug resistance first appearing in low transmission settings before spreading to high transmission settings. Several features of low-transmission regions are hypothesized as explanations: higher chance of symptoms and treatment seeking, better treatment access, less within-host competition among clones, and lower rates of recombination. Here, we test whether importation of drug-resistant parasites is more likely to lead to successful emergence and establishment in low-transmission or high-transmission periods of the same epidemiological setting, using a spatial, individual-based stochastic model of malaria and drug-resistance evolution calibrated for Burkina Faso. Upon controlling for the timing of importation of drug-resistant genotypes and examination of key model variables, we found that drug-resistant genotypes imported during the low transmission season were, (1) more susceptible to stochastic extinction due to the action of random genetic drift, and (2) more likely to lead to establishment of drug resistance when parasites are able to survive early stochastic loss due to drift. This implies that rare importation events are more likely to lead to establishment if they occur during a high-transmission season, but that constant importation (e.g., neighboring countries with high levels of resistance) may produce a greater risk during low-transmission periods.
A dynamic scale-mixture model of motion in natural scenes

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in videos of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.
Anti-CRISPR proteins trigger a burst of CRISPR-Cas9 expression that enhances phage defense

CRISPR-Cas immune systems provide bacteria with adaptive immunity against bacteriophages, but they are often transcriptionally downregulated to mitigate autoimmunity. In some cases, CRISPR-Cas expression increases in response to a phage infection, but the mechanisms of induction are largely unknown, and it is unclear whether induction occurs strongly and quickly enough to benefit the bacterial host. In S. pyogenes, Cas9 is both an immune effector and autorepressor of CRISPR-Cas expression. Here, we show that phage-encoded anti-CRISPR proteins relieve Cas9 autorepression and trigger a rapid increase in CRISPR-Cas levels during a single phage infective cycle. As a result, fewer cells succumb to lysis leading to a striking survival benefit after multiple rounds of infection. CRISPR-Cas induction also reduces lysogeny, thereby limiting a route for horizontal gene transfer. Altogether, we show that Cas9 is not only a CRISPR-Cas effector and repressor, but also a phage sensor that can mount an anti-anti- CRISPR transcriptional response.
Post-transcriptional mechanisms modulate the consequences of adaptive copy number variation

Copy-number variants (CNVs) are large-scale amplifications or deletions of DNA that can drive rapid adaptive evolution and result in large-scale changes in gene expression. Whereas alterations in the copy number of one or more genes within a CNV can confer a selective advantage, other genes within a CNV can decrease fitness when their dosage is changed. Dosage compensation - in which the gene expression output from multiple gene copies is less than expected - is one means by which an organism can mitigate the fitness costs of deleterious gene amplification. Previous research has shown evidence for dosage compensation at both the transcriptional level and at the level of protein expression; however, the extent of compensation differs substantially between genes, strains, and studies. Here, we investigated sources of dosage compensation at multiple levels of gene expression regulation by defining the transcriptome, translatome and proteome of experimentally evolved yeast (Saccharomyces cerevisiae) strains containing adaptive CNVs. We quantified the gene expression output at each step and found evidence of widespread dosage compensation at the protein abundance (∼47%) level. By contrast we find only limited evidence for dosage compensation at the transcriptional (∼8%) and translational (∼3%) level. We also find substantial divergence in the expression of unamplified genes in evolved strains that could be due to either the presence of a CNV or adaptation to the environment. Detailed analysis of 82 amplified and 411 unamplified genes with significantly discrepant relationships between RNA and protein abundances identified enrichment for upstream open reading frames (uORFs). These uORFs are enriched for binding site motifs for SSD1, an RNA binding protein that has previously been associated with tolerance of aneuploidy. Our findings suggest that, in the presence of CNVs, SSD1 may act to alter the expression of specific genes by potentiating uORF mediated translational regulation.
Fast myosin binding protein C knockout in skeletal muscle alters length-dependent activation and myofilament structure

In striated muscle, some sarcomere proteins regulate crossbridge cycling by varying the propensity of myosin heads to interact with actin. Myosin-binding protein C (MyBP-C) is bound to the myosin thick filament and is predicted to interact and stabilize myosin heads in a docked position against the thick filament and limit crossbridge formation, the so-called OFF state. Via an unknown mechanism, MyBP-C is thought to release heads into the so-called ON state, where they are more likely to form crossbridges. To study this proposed mechanism, we used the C2-/- mouse line to knock down fast-isoform MyBP-C completely and total MyBP-C by ∼24%, and conducted mechanical functional studies in parallel with small-angle X-ray diffraction to evaluate the myofilament structure. We report that C2−/− fibers presented deficits in force production and reduced calcium sensitivity. Structurally, passive C2-/- fibers presented altered SL-independent and SL-dependent regulation of myosin head ON/OFF states, with a shift of myosin heads towards the ON state. Unexpectedly, at shorter sarcomere lengths, the thin filament was axially extended in C2-/- vs. non-transgenic controls, which we postulate is due to increased low-level crossbridge formation arising from relatively more ON myosins in the passive muscle that elongates the thin filament. The downstream effect of increasing crossbridge formation in a passive muscle on contraction performance is not known. Such widespread structural changes to sarcomere proteins provide testable mechanisms to explain the etiology of debilitating MyBP-C-associated diseases.
Curvature formation in the visual cortex: How do we sample?

Does the human visual system sample shapes at discrete points? During adaptation when the neurons are fatigued, one observes the underlying principles that were once less prominent than the fatigued features. Operating under deficit, these less prominent features expose the original contributions from the fatigued neurons that are now absent. An underlying lower order neural process, is thus, now revealed. In this paper, we conduct experiments using a modified version of the circle-polygon illusion in order to reveal the brain’s sampling pattern. The circle-polygon illusion produces polygonal percepts during adaptation when a static dark outline circle is pulsed at 2 Hz alternating with a gradient luminance circle. We perform two experiments. In the first experiment, we present circles of size [2,4,8,16] deg presented at eccentricity [0,1,2,4,8] deg in a cross design. In the second experiment, we modify the method of Sakurai (2014) and display arc lengths that are 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8 and 1 (whole) of a circle, of size 4 and 8 deg, presented centrally. The observers report the edge length seen instead of a polygon order. We find that the stimulus size and presentation eccentricity, taken together, best explain the edge length reported by the users and that size alone is the most reasonable fixed parameter. The users, as a random effect, do not influence the mean of the edge length reported when considering the best model reported (size and eccentricity together) according to loglikelihood. However, the users do influence edge length reported only when using mean eccentricity or eccentricity as the parameter influencing edge length. Arc lengths of a circle produce same or similar edge lengths. The length of the curve does not play a significant role signifying that biological neurophysiology at an eccentricity controls the edge length formation.