Miracle-dz/UniFAR-FTUs huggingface dataset:
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution

We present CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation), a benchmark consisting of 800 Python functions (3-13 lines). Each function comes with an input-output pair, leading to two natural tasks: input prediction and output prediction. First, we propose a generic recipe for generating our execution benchmark which can be used to create future variation of the benchmark. Second, we evaluate twenty code models on our benchmark and discover that many recent high-scoring models on HumanEval do not show the same improvements on our benchmark. Third, we show that simple CoT and fine-tuning schemes can improve performance on our benchmark but remain far from solving it. The best setup, GPT-4 with chain of thought (CoT), achieves a pass@1 of 75% and 81% on input and output prediction, respectively. In contrast, Code Llama 34B achieves a pass@1 of 50% and 46% on input and output prediction, highlighting the gap between open and closed source models. As no model is close to acing CRUXEval, we provide examples of consistent GPT-4 failures on simple programs as a lens into its code reasoning capabilities and areas for improvement.
Soils-on-a-chip reveal unforeseen motility parameters of conﬁned Bradyrhizobium diazoefﬁciens

Soil bacterium Bradyrhizobium diazoefﬁciens is an N 2 -ﬁxing symbiont of soybean that helps to improve grain quality. With two ﬂagellar systems, it swims in water-saturated pores, but its motility, potentially crucial for root nodulation competitiveness, remains unclear. We designed and fabricated microﬂuidic soil-on-a-chip (SOC) devices, offering sustainable agriculture an innovative tool to directly visualize bacteria conﬁned-behavior. We measured velocities and changes of direction for two strains, wild-type and a mutant with one ﬂagellum, and found a reduction in speed and an increase in 180° changes of direction for both strains in channels of decreasing microscopic cross-section. Although the wild-type swims faster in unconﬁned spaces, this advantage disappears in SOCs. Furthermore, we used the measured motility parameters to model and simulate B. diazoefﬁciens motion in SOC devices for extended periods and larger scales, enabling further predictions of diffusion in real soils.
Neural interference between real and imagined visual stimuli

Evidence suggests that mental imagery and veridical perception recruit similar components of the human visual system. If so, neural representations of imagined and real stimuli should interact with one another, combining constructively or competing antagonistically. To determine if and how real and imagined visual stimuli interact in the brain, we asked participants to mentally visualise white bars at specific orientations after a rhythmic countdown while their brain activity was recorded using electroencephalography. Stimuli were imagined in isolation, or while another stimulus at a highly or poorly congruent orientation appeared on-screen. Multivariate pattern analysis was used to assess whether overlap between imagined and real stimulus features enhanced or diminished stimulus-specific sensory information in the brain. Findings showed that imagined and real orientation could be decoded from brain activity, with real orientation decoding mildly amplified by highly congruent, but not poorly congruent, imagined orientations. Although interactions between real and imagined stimuli were observed, no evidence was detected to suggest that imagined and real stimuli use the same neural activity patterns to encode sensory information. Instead, congruent imagery seemed only to amplify activity which had already been induced by real percepts, targeting late- but not early-stage perceptual representations. Ultimately, this study suggests that imagined and real stimuli interact in a mildly constructive manner, with imagination mostly acting in a modulatory capacity.
Sea-Level Rise and Venezuela: Potential Impacts and Responses

I VOLONTE, C.R. and ARISMENDI, J., 1995. Sea-level rise and Venezuela: Potential impacts and responses. Journal of Coastal Research, SI No. 14, 285-302. Fort Lauderdale (Florida). ISSN 0749-0208. This is the first quantitative national assessment of the vulnerability of the Venezuelan coast to the impacts of accelerated sea-level rise (inundation of low-lying areas and coastal erosion). The study utilizes aerial videotape-assisted vulnerability analysis (or AVVA). Sea-level rise is assumed to be the driving force of future coastal change, with most attention focussed on a scenario of a one-meter rise by 2100. The Bruun Rule is used to model erosion, while simple inundation is ap plied to low-lying areas. All analyses assume the present pattern of coastal development (July, 1991). It is found presently that only 385 km, or 13%, of the shoreline has medium to high levels of development. Assuming a No Protection response and a one-meter rise in sea level, nearly 6,000 km2 of land could be lost, mainly by inundation of the Orinoco delta. Land loss due to erosion is minor compared to inundation, but it could damage Venezuela's coastal oil infrastructure and sev eral coastal urban areas. In total, land and buildings presently worth about U.S. $350 million could be destroyed (excluding damage to harbors) and 56,000 to 62,000 people could be displaced from their homes. Nourishment of recreational beaches and construction of seawalls along 200 km of developed coastlines combined with harbor upgrade could cost U.S. $1.0 to $1.5 billion for a one meter rise in sea level. Assuming this investment occurred linearly over 50 years (2051 to 2100), this represents annual expenditure of 0.46% to 0.70% of Venezuela's gross national investment (in 1990). While Venezuela's developed coastline appears vulnerable to accelerated sea-level rise, increasing planning and coastal management efforts could avoid increasing future vulnerability, particularly for the large coastal frontage (>2,000 km) where little or no development exists today. ADDITIONAL INDEX WORDS: Coastal management, land loss, coastline protection, Bruun Rule, erosion, inundation, ports, Orinoco delta.
A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes

Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment. Surrounding each patient's treatment, communication among HCPs can be examined as temporal networks, constructed from Electronic Health Record (EHR) access logs. This paper introduces a visual analytics system designed to study the effectiveness and efficiency of temporal communication networks mediated by the EHR system. We present a method that associates network measures with patient survival outcomes and devises effectiveness metrics based on these associations. To analyze communication efficiency, we extract the latencies and frequencies of EHR accesses. Our visual analytics system is designed to assist in inspecting and understanding the composed communication effectiveness metrics and to enable the exploration of communication efficiency by encoding latencies and frequencies in an information flow diagram. We demonstrate and evaluate our system through multiple case studies and an expert review.
Active Diffusion of Self-Propelled Particles in Semi-Flexible Polymer Networks

Mesh-like structures, such as mucus gel or cytoskeleton networks, are ubiquitous in biological systems. These intricate structures are composed of cross-linked, semi-flexible bio-filaments, crucial to numerous biological processes. In many biological systems, active self-propelled particles like motor proteins or bacteria navigate these intricate polymer networks. In this study, we develop a computational model of three-dimensional cubic-topological, swollen polymer networks of semi-flexible filaments. We perform Langevin dynamics simulations to investigate the diffusion of active tracer particles navigating through these networks. By analyzing various physical observables, we investigate the effects of mesh-to-particle size ratio, P\'eclet number of active particles, and bending stiffness of the polymer networks upon active trapped-and-hopping diffusion of the tracer. When the tracer size is equal to or larger than the mesh size, the polymer stiffness substantially enhances trapping while suppressing the hopping process. Notably, the mean trapped time exhibits an exponential growth law to the bending stiffness with an activity-dependent slope. An analytic theory based on the mean first-passage time of active particles in a harmonic potential is developed. Our findings deepen the comprehension of the intricate interplay between the polymer's bending stiffness, tracer size, and the activity of tracer particles. This knowledge can shed light on important biological processes, such as motor-driven cargo transport or drug delivery, which hinge on the behavior of active particles within biological gels.
Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

Metadata, often termed"data about data,"is crucial for organizing, understanding, and managing vast omics datasets. It aids in efficient data discovery, integration, and interpretation, enabling users to access, comprehend, and utilize data effectively. Its significance spans the domains of scientific research, facilitating data reproducibility, reusability, and secondary analysis. However, numerous perceptual and technical barriers hinder the sharing of metadata among researchers. These barriers compromise the reliability of research results and hinder integrative meta-analyses of omics studies . This study highlights the key barriers to metadata sharing, including the lack of uniform standards, privacy and legal concerns, limitations in study design, limited incentives, inadequate infrastructure, and the dearth of well-trained personnel for metadata management and reuse. Proposed solutions include emphasizing the promotion of standardization, educational efforts, the role of journals and funding agencies, incentives and rewards, and the improvement of infrastructure. More accurate, reliable, and impactful research outcomes are achievable if the scientific community addresses these barriers, facilitating more accurate, reliable, and impactful research outcomes.
A developmental brain-wide screen identifies retrosplenial cortex as a key player in the emergence of persistent memory

Memories formed early in life are short-lived while those formed later persist. Recent work revealed that infant memories are stored in a latent state. But why they fail to be retrieved is poorly understood. Here we investigated brain-wide circuit mechanisms underlying infantile amnesia in mice. We performed a screen that combined activity-dependent neuronal tagging at different postnatal ages, tissue clearing and light sheet microscopy. We observed striking developmental transitions in the organization of fear memory networks and changes in the activity and functional connectivity of the retrosplenial cortex (RSP) that aligned with the emergence of persistent memory. 7 days after learning, chemogenetic reactivation of tagged RSP ensembles enhanced memory in adults but not in infants. But after 33 days, reactivating infant-tagged RSP ensembles recovered forgotten memories. These studies show that RSP ensembles store latent infant memories, reveal the time course of RSP functional maturation, and suggest that immature RSP functional networks contribute to infantile amnesia.
Genetic Toolbox for Photorhabdus and Xenorhabdus: pSEVA based heterologous expression systems and CRISPR/Cpf1 based genome editing for rapid natural product profiling

Background Bacteria of the genus Photorhabdus and Xenorhabdus are motile, Gram-negative bacteria that live in symbiosis with entomopathogenic nematodes. Due to their complex life cycle, they produce a large number of specialized metabolites (natural products) encoded in biosynthetic gene clusters (BGC). Genetic tools for this genus have been rare and applicable to only a few strains. In the past, several tools have been developed for the activation of BGCs and the deletion of individual genes. However, these often have limited efficiency or are time consuming. Among the limitations, it is essential to have versatile expression systems and genome editing tools that could facilitate the practical work. Results In the present study, we developed several expression vectors and a CRISPR-Cpf1 genome editing vector for genetic manipulations in Photorhabdus and Xenorhabdus using SEVA plasmids. The SEVA collection is based on modular vectors that allow exchangeability of different elements (e.g. origin of replication and antibiotic selection markers with the ability to insert desired sequences for different end applications. Initially, we tested different SEVA vectors containing the broad host range origins and three different resistance genes for kanamycin, gentamycin and chloramphenicol, respectively. We demonstrated that these vectors are replicative not only in well-known representatives, e.g. Photorhabdus laumondii TTO1, but also in other rarely described strains like Xenorhabdus sp. TS4. For our CRISPR/Cpf1-based system, we used the pSEVA231 backbone to delete not only small genes but also large parts of BGCs. Furthermore, we were able to activate and refactor BGCs to obtain high production titers of high value compounds such as safracin B, a semisynthetic precursor for the anti-cancer drug ET-743. Conclusions The results of this study provide new inducible expression vectors and a CRISPR/CPf1 encoding vector all based on the SEVA (Standard European Vector Architecture) collection, which can improve genetic manipulation and genome editing processes in Photorhabdus and Xenorhabdus.
Cosmic evolution of black hole spin and galaxy orientations: Clues from the NewHorizon and Galactica simulations

Black holes (BHs) are ubiquitous components of the center of most galaxies. In addition to their mass, the BH spin, through its amplitude and orientation, is a key factor in the galaxy formation process, as it controls the radiative efficiency of the accretion disk and relativistic jets. Using the recent cosmological high-resolution zoom-in simulations and in which the evolution of the BH spin is followed on the fly, we have tracked the cosmic history of a hundred BHs with a mass greater than $2 For each of them, we have studied the variations of the three-dimensional angle (Psi ) subtended between the BH spins and the angular momentum vectors of their host galaxies (estimated from the stellar component). The analysis of the individual evolution of the most massive BHs suggests that they are generally passing by three different regimes. First, for a short period after their birth, low-mass BHs BH <$3times 10$^4$M$_ are rapidly spun up by gas accretion and their spin tends to be aligned with their host galaxy spin. Then follows a second phase in which the accretion of gas onto low-mass BHs BH is quite chaotic and inefficient, reflecting the complex and disturbed morphologies of forming proto-galaxies at high redshifts. The variations of Psi are rather erratic during this phase and are mainly driven by the rapid changes of the direction of the galaxy angular momentum. Then in a third and long phase, BHs are generally well settled in the center of galaxies around which the gas accretion becomes much more coherent BH >$10$^5$ M$_ In this case, the BH spins tend to be well aligned with the angular momentum of their host galaxy and this configuration is generally stable even though BH merger episodes can temporally induce misalignment. We even find a few cases of BH-galaxy spin anti-alignment that lasts for a long time in which the gas component is counter-rotating with respect to the stellar component. We have also derived the distributions of cos(Psi ) at different redshifts and found that BHs and galaxy spins are generally aligned. Our analysis suggests that the fraction of BH-galaxy pairs with low Psi values reaches maximum at zsim 4-3 and then decreases until zsim 1.5 due to the high BH-merger rate. Afterward, it remains almost constant probably due to the fact that BH mergers becomes rare, except for a slight increase at late times. Finally, based on a Monte Carlo method, we also predict statistics for the 2-d projected spin-orbit angles lambda . In particular, the distribution of lambda traces the alignment tendency well in the three-dimensional analysis. Such predictions provide an interesting background for future observational analyses.
An Ohta-Kawasaki Model set on the space

We examine a non-local diffuse interface energy with Coulomb repulsion in three dimensions inspired by the Thomas-Fermi-Dirac-von Weizs\"{a}cker, and the Ohta-Kawasaki models. We consider the corresponding mass-constrained variational problem and show the existence of minimizers for small masses, and the absence of minimizers for large masses.
LLMs for Robotic Object Disambiguation

The advantages of pre-trained large language models (LLMs) are apparent in a variety of language processing tasks. But can a language model's knowledge be further harnessed to effectively disambiguate objects and navigate decision-making challenges within the realm of robotics? Our study reveals the LLM's aptitude for solving complex decision making challenges that are often previously modeled by Partially Observable Markov Decision Processes (POMDPs). A pivotal focus of our research is the object disambiguation capability of LLMs. We detail the integration of an LLM into a tabletop environment disambiguation task, a decision making problem where the robot's task is to discern and retrieve a user's desired object from an arbitrarily large and complex cluster of objects. Despite multiple query attempts with zero-shot prompt engineering (details can be found in the Appendix), the LLM struggled to inquire about features not explicitly provided in the scene description. In response, we have developed a few-shot prompt engineering system to improve the LLM's ability to pose disambiguating queries. The result is a model capable of both using given features when they are available and inferring new relevant features when necessary, to successfully generate and navigate down a precise decision tree to the correct object--even when faced with identical options.
Mapping of invasive phragmites (common reed) in Gulf of Mexico coastal wetlands using multispectral imagery and small unmanned aerial systems

ABSTRACT In coastal wetlands of the Gulf of Mexico, the invasive plant species Phragmites australis (common reed) rapidly alters the ecology of a site by shifting plant communities from heterogeneous mixtures of plant species to homogenous stands of Phragmites. Phragmites grows in very dense stands at an average height of 4.6 m and outcompetes native plants for resources. To restore affected wetlands, resource managers require an accurate map of Phragmites locations. Previous studies have used satellite and manned aircraft-based remote-sensing images to map Phragmites in relatively large areas at a coarse scale; however, low-altitude high-spatial-resolution pixel-based classification approaches would improve the mapping accuracy. This study explores the supervised classification methods to accurately map Phragmites in the coastal wetlands at the delta of the Pearl River in Louisiana and Mississippi, USA, using high-resolution (8 cm ground sample distance; GSD) multispectral imagery collected from a small unmanned aerial system platform at an altitude of 120 m. We create a map through pixel-based Support Vector Machines (SVM) classification using blue, green, red, red edge, and near-infrared spectral bands along with a digital surface model (DSM), vegetation indices, and morphological attribute profiles (MAPs) as features. This study also demonstrates the effects of different features and their usefulness in generating an accurate map of Phragmites locations. Accuracy assessment based on a) a subset of training/testing samples (to show classifier performance) and b) the entire ground reference (GR) map (to show the quality of mapping) is demonstrated. Kappa, overall accuracy (OA), class accuracies, and their confidence intervals (CIs) are reported. An OA of 91% and kappa of 63 is achieved. The results of this study indicate that features such as MAPs are very useful in accurately mapping invasive Phragmites compared with existing region-based approaches.
Atom-by-Atom Mapping and Understanding of In-Plane Anisotropy in GaTe

Main-group chalcogenides with layered crystal structures and high in-plane anisotropy are attracting increasing interest for a range of practical applications. The III-VI semiconductor, monoclinic gallium monotelluride (m-GaTe), has been recently used in high-sensitivity photodetectors/phototransistors and electronic memory applications due to its anisotropic properties yielding superior optical and electrical performance. Despite these applications, the origin of such anisotropy, namely the complex structural and bonding environments in GaTe nanostructures remain to be fully understood. In the present work, we report a comprehensive atomic-scale characterization of m-GaTe by state-of-the-art element-resolved atomic-scale microscopy experiments. By performing imaging for two different view directions, we are able to directly measure the in-plane anisotropy of m-GaTe at the sub-Angstrom level, and show that it compares well with the results of first-principles modeling. Quantum-chemical bonding analyses provide a detailed picture of the atomic neighbor interactions within the layers, revealing that vertical Ga-Ga homopolar bonds get stronger when they are distorted and rotated, inducing the strong in-plane anisotropy.
Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos

Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems. Previous single-stage TAD methods primarily rely on frame prediction, making them vulnerable to interference from dynamic backgrounds induced by the rapid movement of the dashboard camera. While two-stage TAD methods appear to be a natural solution to mitigate such interference by pre-extracting background-independent features (such as bounding boxes and optical flow) using perceptual algorithms, they are susceptible to the performance of first-stage perceptual algorithms and may result in error propagation. In this paper, we introduce TTHF, a novel single-stage method aligning video clips with text prompts, offering a new perspective on traffic anomaly detection. Unlike previous approaches, the supervised signal of our method is derived from languages rather than orthogonal one-hot vectors, providing a more comprehensive representation. Further, concerning visual representation, we propose to model the high frequency of driving videos in the temporal domain. This modeling captures the dynamic changes of driving scenes, enhances the perception of driving behavior, and significantly improves the detection of traffic anomalies. In addition, to better perceive various types of traffic anomalies, we carefully design an attentive anomaly focusing mechanism that visually and linguistically guides the model to adaptively focus on the visual context of interest, thereby facilitating the detection of traffic anomalies. It is shown that our proposed TTHF achieves promising performance, outperforming state-of-the-art competitors by +5.4% AUC on the DoTA dataset and achieving high generalization on the DADA dataset.