Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of a Bystander Intervention Program to Increase Bystander Behaviors Across Latent Risk Groups of High Schoolers

Bystander intervention programs have established efficacy to increase bystander behaviors to prevent interpersonal violence (IPV). Little research has investigated intervention efficacy among latent risk classes among high school students. Data from a five-year randomized control trial were used to conduct multigroup path analyses to assess the association between type of training received and bystander outcomes moderated by risk groups identified via latent profile analysis (LPA). LPA was used to identify risk based on six indicators related to violence exposure, association with aggressive friends, and alcohol use. Bystander training received was the primary independent variable characterized as: no training, overview speech alone, or skills training. Outcomes included (a) observed bystander behaviors; (b) reactive bystander behaviors; or (c) proactive bystander behaviors. Three risk groups were identified via LPA: low risk, moderate risk witnesses of IPV, and highest risk victims and perpetrators. Of the bystander trainings received, overview speeches only increased reactive bystander behaviors among low risk students. The skills training was effective at increasing most bystander outcomes among all risk groups, with the largest effect sizes observed among the highest risk victims and perpetrators profile. Findings suggest that tailoring or modifying bystander training based on the risk profiles of youth may lead to greater potential to increase bystander behaviors to reduce risk of violence. Specifically, overview speech trainings should be targeted to low risk youth, while skills training primarily delivered to higher risk youth. These skills trainings could incorporate content related to trauma-informed care as well as associations with alcohol use, which may enhance their effectiveness further.
Metrics-Based Evaluation and Comparison of Visualization Notations

A visualization notation is a recurring pattern of symbols used to author specifications of visualizations, from data transformation to visual mapping. Programmatic notations use symbols defined by grammars or domain-specific languages (e.g. ggplot2, dplyr, Vega-Lite) or libraries (e.g. Matplotlib, Pandas). Designers and prospective users of grammars and libraries often evaluate visualization notations by inspecting galleries of examples. While such collections demonstrate usage and expressiveness, their construction and evaluation are usually ad hoc, making comparisons of different notations difficult. More rarely, experts analyze notations via usability heuristics, such as the Cognitive Dimensions of Notations framework. These analyses, akin to structured close readings of text, can reveal design deficiencies, but place a burden on the expert to simultaneously consider many facets of often complex systems. To alleviate these issues, we introduce a metrics-based approach to usability evaluation and comparison of notations in which metrics are computed for a gallery of examples across a suite of notations. While applicable to any visualization domain, we explore the utility of our approach via a case study considering statistical graphics that explores 40 visualizations across 9 widely used notations. We facilitate the computation of appropriate metrics and analysis via a new tool called NotaScope. We gathered feedback via interviews with authors or maintainers of prominent charting libraries ($n=6$). We find that this approach is a promising way to formalize, externalize, and extend evaluations and comparisons of visualization notations.
On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions

Game theory offers an interpretable mathematical framework for modeling multi-agent interactions. However, its applicability in real-world robotics applications is hindered by several challenges, such as unknown agents' preferences and goals. To address these challenges, we show a connection between differential games, optimal control, and energy-based models and demonstrate how existing approaches can be unified under our proposed Energy-based Potential Game formulation. Building upon this formulation, this work introduces a new end-to-end learning application that combines neural networks for game-parameter inference with a differentiable game-theoretic optimization layer, acting as an inductive bias. The experiments using simulated mobile robot pedestrian interactions and real-world automated driving data provide empirical evidence that the game-theoretic layer improves the predictive performance of various neural network backbones.
BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge

Pre-trained large language models have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks and to be appropriately specialized to particular domains. Here, we target bioinformatics due to the amount of specialized domain knowledge, algorithms, and data operations this discipline requires. We present BioCoder, a benchmark developed to evaluate large language models (LLMs) in generating bioinformatics-specific code. BioCoder spans a broad spectrum of the field and covers cross-file dependencies, class declarations, and global variables. It incorporates 1026 Python functions and 1243 Java methods extracted from GitHub, along with 253 examples from the Rosalind Project, all pertaining to bioinformatics. Using topic modeling we show that overall coverage of the included code is representative of the full spectrum of bioinformatics calculations. BioCoder incorporates a fuzz-testing framework for evaluation. We have applied it to evaluate many models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, GPT-3.5, and GPT-4. Furthermore, we finetuned StarCoder, demonstrating how our dataset can effectively enhance the performance of LLMs on our benchmark (by>15% in terms of Pass@K in certain prompt configurations and always>3%). The results highlight two key aspects of successful models: (1) Successful models accommodate a long prompt (>~2600 tokens) with full context, for functional dependencies. (2) They contain specific domain knowledge of bioinformatics, beyond just general coding knowledge. This is evident from the performance gain of GPT-3.5/4 compared to the smaller models on the benchmark (50% vs up to ~25%). Our dataset, benchmark, Docker images, and scripts required for testing are all available at https://github.com/gersteinlab/biocoder.
TPIA2: an updated tea plant information archive for Camellia genomics

Abstract The genus Camellia consists of about 200 species, which include many economically important species widely used for making tea, ornamental flowers and edible oil. Here, we present an updated tea plant information archive for Camellia genomics (TPIA2; http://tpia.teaplants.cn) by integrating more novel large-scale genomic, transcriptomic, metabolic and genetic variation datasets as well as a variety of useful tools. Specifically, TPIA2 hosts all currently available and well assembled 10 Camellia genomes and their comprehensive annotations from three major sections of Camellia. A collection of 15 million SNPs and 950 950 small indels from large-scale genome resequencing of 350 diverse tea accessions were newly incorporated, followed by the implementation of a novel ‘Variation’ module to facilitate data retrieval and analysis of the functionally annotated variome. Moreover, 116 Camellia transcriptomes were newly assembled and added, leading to a significant extension of expression profiles of Camellia genes to 13 developmental stages and eight abiotic/biotic treatments. An updated ‘Expression’ function has also been implemented to provide a comprehensive gene expression atlas for Camellia. Two novel analytic tools (e.g. Gene ID Convert and Population Genetic Analysis) were specifically designed to facilitate the data exchange and population genomics in Camellia. Collectively, TPIA2 provides diverse updated valuable genomic resources and powerful functions, and will continue to be an important gateway for functional genomics and population genetic studies in Camellia.
Learning-Induced Plasticity Enhances the Capacity of Visual Working Memory

Visual working memory (VWM) is limited in capacity, though memorizing meaningful objects may refine this limitation. However, meaningful and meaningless stimuli typically differ perceptually, and objects’ associations with meaning are usually already established outside the laboratory, potentially confounding experimental findings. Here, in two experiments with young adults (N = 45 and N = 20), we controlled for these influences by having observers actively learn associations of (for them) initially meaningless stimuli: Chinese characters, half of which were consistently paired with pictures of animals or everyday objects in a learning phase. This phase was preceded and followed by a (pre- and postlearning) change-detection task to assess VWM performance. The results revealed that short-term retention was enhanced after learning, particularly for meaning-associated characters, although participants did not quite reach the accuracy level attained by native Chinese observers (young adults, N = 20). These results thus provide direct experimental evidence that participants’ VWM of objects is boosted by them having acquired a long-term-memory association with meaning.
Convolutional neural network models of neuronal responses in macaque V1 reveal limited non-linear processing

Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple non-linearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies on far more non-linear computations than previously thought. Specifically, unit responses in an intermediate layer of VGG-19 were found to best predict macaque V1 responses to thousands of natural and synthetic images. Here, we evaluated the hypothesis that the poor performance of lower-layer units in VGG-19 might be attributable to their small receptive field size rather than to their lack of complexity per se. We compared VGG-19 with AlexNet, which has much larger receptive fields in its lower layers. Whereas the best-performing layer of VGG-19 occurred after seven non-linear steps, the first convolutional layer of AlexNet best predicted V1 responses. Although VGG-19’s predictive accuracy was somewhat better than standard AlexNet, we found that a modified version of AlexNet could match VGG-19’s performance after only a few non-linear computations. Control analyses revealed that decreasing the size of the input images caused the best-performing layer of VGG-19 to shift to a lower layer, consistent with the hypothesis that the relationship between image size and receptive field size can strongly affect model performance. We conducted additional analyses using a Gabor pyramid model to test for non-linear contributions of normalization and contrast saturation. Overall, our findings suggest that the feedforward responses of V1 neurons can be well explained by assuming only a few non-linear processing stages.
Observations of a black hole x-ray binary indicate formation of a magnetically arrested disk

Accretion of material onto a black hole drags any magnetic fields present inwards, increasing their strength. Theory predicts that sufficiently strong magnetic fields can halt the accretion flow, producing a magnetically arrested disk (MAD). We analyzed archival multiwavelength observations of an outburst from the black hole x-ray binary MAXI J1820+070 in 2018. The radio and optical fluxes were delayed compared with the x-ray flux by about 8 and 17 days, respectively. We interpret this as evidence for the formation of a MAD. In this scenario, the magnetic field is amplified by an expanding corona, forming a MAD around the time of the radio peak. We propose that the optical delay is due to thermal viscous instability in the outer disk. Description Editor’s summary As material falls toward a black hole, it forms an accretion disk that emits x-rays and optical light and sometimes also a jet that is visible at radio wavelengths. Theory predicts that if the disk contains a sufficiently strong magnetic field, then it can resist the gravitational pull of the black hole and temporarily halt the accretion process. You et al. compared observations of a transient black hole accretion event at x-ray, optical, and radio wavelengths. They found time delays between brightening at the different wavelengths and used models to show that this behavior is produced by a magnetically arrested disk. —Keith T. Smith Multiwavelength observations show that accretion onto a black hole was halted by a magnetic field in the accretion disk.
DECODE: DilatEd COnvolutional neural network for Detecting Extreme-mass-ratio inspirals

The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences. While matched filtering-based techniques are known for their computational demands, existing deep learning-based methods primarily handle time-domain data and are often constrained by data duration and SNR. In addition, most existing work ignores time-delay interferometry (TDI) and applies the long-wavelength approximation in detector response calculations, thus limiting their ability to handle laser frequency noise. In this study, we introduce DECODE, an end-to-end model focusing on EMRI signal detection by sequence modeling in the frequency domain. Centered around a dilated causal convolutional neural network, trained on synthetic data considering TDI-1.5 detector response, DECODE can efficiently process a year's worth of multichannel TDI data with an SNR of around 50. We evaluate our model on 1-year data with accumulated SNR ranging from 50 to 120 and achieve a true positive rate of 96.3% at a false positive rate of 1%, keeping an inference time of less than 0.01 seconds. With the visualization of three showcased EMRI signals for interpretability and generalization, DECODE exhibits strong potential for future space-based gravitational wave data analyses.
Testing the validity of a new scale designed to assess beliefs and perceptions about colorectal cancer and colorectal cancer screening in Malaysia: a principal component analysis

Objective To conduct a cultural adaptation and validation of the Champion Health Belief Model Scale (CHBMS) for colorectal cancer (CRC) screening (CHBMS-CRC-M) in order to assess and investigate perceptions and beliefs about CRC screening in Malaysia. Designs and participants The results from an evidence synthesis and the outcomes from an expert panel discussion were used to shape CHBMS scale content into an assessment of beliefs about CRC screening (CHBMS-CRC). This questionnaire assessment was translated into the official language of Malaysia. An initial study tested the face validity of the new scale or questionnaire with 30 men and women from various ethnic groups. Factorial or structural validity was investigated in a community sample of 954 multiethnic Malaysians. Setting Selangor state, Malaysia. Results The new scale was culturally acceptable to the three main ethnic groups in Malaysia and achieved good face validity. Cronbach’s alpha coefficients ranged from 0.66 to 0.93, indicating moderate to good internal consistency. Items relating to perceived susceptibility to CRC ‘loaded’ on Factor 1 (with loadings scoring above 0.90); perceived benefits of CRC screening items loaded on factor 2 and were correlated strongly (loadings ranged between 0.63 and 0.83) and perceived barriers (PBA) to CRC screening (PBA) items loaded on factor 3 (range 0.30–0.72). Conclusion The newly developed CHBMS-CRC-M fills an important gap by providing a robust scale with which to investigate and assess CRC screening beliefs and contribute to efforts to enhance CRC screening uptake and early detection of CRC in Malaysia and in other Malay-speaking communities in the region.
Examining the roles of visual imagery and working memory in the retrieval of autobiographical memories using a dual-task paradigm

The retrieval of autobiographical memories involves the construction of mental representations of past personal events. Many researchers examining the processes underlying memory retrieval argue that visual imagery plays a fundamental role. Other researchers, however, have argued that working memory is an integral component involved in memory retrieval. The goal of this study was to resolve these conflicting arguments by comparing the relative contributions of visual imagery and working memory during the retrieval of autobiographical memories in a dual-task paradigm. While following a moving dot, viewing a dynamic visual noise (DVN), or viewing a blank screen, 95 participants recalled their memories and subsequently rated them on different memory characteristics. The results suggest that inhibiting visual imagery by having participants view DVN merely delayed memory retrieval but did not affect the phenomenological quality of the memories retrieved. Taxations to the working memory by having participants follow a moving dot, on the contrary, resulted in only longer retrieval latencies and no reductions in the specificity, vividness, or the emotional intensity of the memories retrieved. Whereas the role of visual imagery during retrieval is clear, future studies could further examine the role of working memory during retrieval by administering a task that is less difficult or by recruiting a larger sample than this study. The results of this study seem to suggest that both visual imagery and working memory play a role during the retrieval of autobiographical memory, but more research needs to be conducted to determine their exact roles.
Timely Computation

This paper addresses the question “what is a digital circuit? ” in relation to the fundamentally analog nature of actual (physical) circuits. A simple informal definition is given and then formalized in the proof assistant Agda. At the heart of this definition is the timely embedding of discrete information in temporally continuous signals. Once this embedding is defined (in constructive logic, i.e., type theory), it is extended in a generic fashion from one signal to many and from simple boolean operations (logic gates) to arbitrarily sophisticated sequential and parallel compositions, i.e., to computational circuits. Rather than constructing circuits and then trying to prove their correctness, a compositionally correct methodology maintains specification, implementation, timing, and correctness proofs at every step. Compositionality of each aspect and of their combination is supported by a single, shared algebraic vocabulary and related by homomorphisms. After formally defining and proving these notions, a few key transformations are applied to reveal the linearity of circuit timing (over a suitable semiring), thus enabling practical, modular, and fully verified timing analysis as linear maps over higher-dimensional time intervals. An emphasis throughout the paper is simplicity and generality of specification, minimizing circuit-specific definitions and proofs while highlighting a broadly applicable methodology of scalable, compositionally correct engineering through simple denotations and homomorphisms.
Diet selection by conditioned and unconditioned goats in the sagebrush steppe of Eastern Oregon

approved: Redacted for Privacy Doit Vas n This research examined the diets of angora goats on a sagebrush bunchgrass rangeland. Research objectives were to 1) determine the effects of a positive conditioning method on both mature and immature angora goats; and 2) to quantify plant selection and provide preliminary information as to the potential for using goats to rehabilitate degraded sagebrush rangelands. Goat diets were ascertained using focal animal bite-count observations during five consecutive seasons, summer 1990 through summer 1991. Treatment group goats were conditioned for 8 months by including everincreasing amounts of sagebrush (Artemisia tridentate subspp. wyomingensi) in their daily ration, up to 25% of their total intake. Conditioning effects were evaluated in a rangeland setting by comparing relative amounts of sagebrush consumption between groups. Our results indicate that while conditioning did not significantly impact sagebrush consumption, young animals consumed significantly more sagebrush than adults. Additionally, learning throughout the first year altered dietary selection by the second summer. Both does and kids were primarily gramnivorous, however there was strong seasonality in species preference and a significant age difference in diets selected. Age differences in the plant species selected persisted throughout the study until the summer of 1991 when kids were eighteen months old. Diet Selection by Conditioned and Unconditioned Goats in the Sagebrush Steppe of Eastern Oregon
Robust bursting dynamics based on the interaction of persistent Na and Na/K pump currents: a dynamic clamp approach

10 Life-supporting rhythmic motor functions like heart-beating in invertebrates and breathing in 11 vertebrates require an indefatigable generation of a robust rhythm by specialized oscillatory 12 circuits, Central Pattern Generators (CPGs). These CPGs should be sufficiently flexible to 13 adjust to environmental changes and behavioral goals. Continuous self-sustained operation of 14 bursting neurons requires intracellular Na concentration to remain in a functional range and to 15 have checks and balances of the Na fluxes met on a cycle-to-cycle basis during bursting. We 16 hypothesize that at a high excitability state, the interaction of the Na/K pump current, Ipump and 17 persistent Na current, INaP, produces a mechanism supporting functional bursting. INaP is a low 18 voltage-activated inward current that initiates and supports the bursting phase. This current 19 does not inactivate and is a significant source of Na influx. Ipump is an outward current activated 20 by [Na]i and is the major source of Na efflux. Both currents are active and counteract each 21 other between and during bursts. We apply a combination of electrophysiology, computational 22 modeling, and dynamic clamp to investigate the role of Ipump and INaP in the leech heartbeat 23 CPG interneurons (HN neurons). Applying dynamic clamp to introduce additional Ipump and INaP 24