Miracle-dz/UniFAR-FTUs huggingface dataset:
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenges are essential to gauge their advanced abilities. In this work, we present OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark, featuring 8,952 problems from Olympiad-level mathematics and physics competitions, including the Chinese college entrance exam. Each problem is detailed with expert-level annotations for step-by-step reasoning. Evaluating top-tier models on OlympiadBench, we implement a comprehensive assessment methodology to accurately evaluate model responses. Notably, the best-performing model, GPT-4V, attains an average score of 17.23% on OlympiadBench, with a mere 11.28% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. Our analysis orienting GPT-4V points out prevalent issues with hallucinations, knowledge omissions, and logical fallacies. We hope that our challenging benchmark can serve as a valuable resource for helping future AGI research endeavors.
Delving into Dark Regions for Robust Shadow Detection

Shadow detection is a challenging task as it requires a comprehensive understanding of shadow characteristics and global/local illumination conditions. We observe from our experiment that state-of-the-art deep methods tend to have higher error rates in differentiating shadow pixels from non-shadow pixels in dark regions (ie, regions with low-intensity values). Our key insight to this problem is that existing methods typically learn discriminative shadow features from the whole image globally, covering the full range of intensity values, and may not learn the subtle differences between shadow and non-shadow pixels in dark regions. Hence, if we can design a model to focus on a narrower range of low-intensity regions, it may be able to learn better discriminative features for shadow detection. Inspired by this insight, we propose a novel shadow detection approach that first learns global contextual cues over the entire image and then zooms into the dark regions to learn local shadow representations. To this end, we formulate an effective dark-region recommendation (DRR) module to recommend regions of low-intensity values, and a novel dark-aware shadow analysis (DASA) module to learn dark-aware shadow features from the recommended dark regions. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on three popular shadow detection datasets. Code is available at https://github.com/guanhuankang/ShadowDetection2021.git.
On multiplicative dependence between elements of polynomial orbits

We classify the pairs of polynomials $f,g \in \mathbb{C}[X]$ having orbits satisfying infinitely many multiplicative dependence relations, extending a result of Ghioca, Tucker and Zieve. Moreover, we show that given $f_1,\ldots, f_n$ from a certain class of polynomials with integer coefficients, the vectors of indices $(m_1,\ldots,m_n)$ such that $f_1^{m_1}(0),\ldots,f_n^{m_n}(0)$ are multiplictively dependent are sparse. We also classify the pairs $f,g \in \mathbb{Q}[X]$ such that there are infinitely many $(x,y) \in \mathbb{Z}^2$ satisfying $f(x)^k=g(y)^\ell$ for some (possibly varying) non-zero integers $k,\ell$.
What Linguistic Features and Languages are Important in LLM Translation?

Large Language Models (LLMs) demonstrate strong capability across multiple tasks, including machine translation. Our study focuses on evaluating Llama2's machine translation capabilities and exploring how translation depends on languages in its training data. Our experiments show that the 7B Llama2 model yields above 10 BLEU score for all languages it has seen, but not always for languages it has not seen. Most gains for those unseen languages are observed the most with the model scale compared to using chat versions or adding shot count. Furthermore, our linguistic distance analysis reveals that syntactic similarity is not always the primary linguistic factor in determining translation quality. Interestingly, we discovered that under specific circumstances, some languages, despite having significantly less training data than English, exhibit strong correlations comparable to English. Our discoveries here give new perspectives for the current landscape of LLMs, raising the possibility that LLMs centered around languages other than English may offer a more effective foundation for a multilingual model.
Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games

A universal feature of human societies is the adoption of systems of rules and norms in the service of cooperative ends. How can we build learning agents that do the same, so that they may flexibly cooperate with the human institutions they are embedded in? We hypothesize that agents can achieve this by assuming there exists a shared set of norms that most others comply with while pursuing their individual desires, even if they do not know the exact content of those norms. By assuming shared norms, a newly introduced agent can infer the norms of an existing population from observations of compliance and violation. Furthermore, groups of agents can converge to a shared set of norms, even if they initially diverge in their beliefs about what the norms are. This in turn enables the stability of the normative system: since agents can bootstrap common knowledge of the norms, this leads the norms to be widely adhered to, enabling new entrants to rapidly learn those norms. We formalize this framework in the context of Markov games and demonstrate its operation in a multi-agent environment via approximately Bayesian rule induction of obligative and prohibitive norms. Using our approach, agents are able to rapidly learn and sustain a variety of cooperative institutions, including resource management norms and compensation for pro-social labor, promoting collective welfare while still allowing agents to act in their own interests.
An anti-mycobacterial conjugated oligoelectrolyte effective against Mycobacterium abscessus

Infections caused by nontuberculous mycobacteria have increased more than 50% in the past two decades and more than doubled in the elderly population. Mycobacterium abscessus (Mab), one of the most prevalent of these rapidly growing species, is intrinsically resistant to numerous antibiotics. Current standard-of-care treatments are not satisfactory, with high failure rate and notable adverse effects. We report here a potent anti-Mab compound from the flexible molecular framework afforded by conjugated oligoelectrolytes (COEs). A screen of structurally diverse, noncytotoxic COEs identified a lead compound, COE-PNH2, which was bactericidal against replicating, nonreplicating persisters and intracellular Mab.COE-PNH2 had low propensity for resistance development, with a frequency of resistance below 1.25 × 10−9 and showed no detectable resistance upon serial passaging. Mechanism of action studies were in line with COE-PNH2 affecting the physical and functional integrity of the bacterial envelope and disrupting the mycomembrane and associated essential bioenergetic pathways. Moreover, COE-PNH2 was well-tolerated and efficacious in a mouse model of Mab lung infection. This study highlights desirable in vitro and in vivo potency and safety index of this COE structure, which represents a promising anti-mycobacterial to tackle an unmet medical need. We developed a conjugated oligoelectrolyte antibiotic candidate to treat Mycobacterium abscessus infection. Editor’s summary Nontuberculous mycobacteria cause substantial infective burden and can be difficult to treat using current regimens. Zhang et al. show that a conjugated oligoelectrolyte, whose structure is unrelated to current antibiotics, was bactericidal against both replicating and nonreplicating M. abscessus. The compound was also effective against intracellular infection. Treatment of a mouse lung infection model was efficacious with no signs of toxicity and low resistance development, illustrating the molecule’s therapeutic potential. —Catherine Charneski
Aspect-Based Sentiment Analysis as a Multi-Label Classification Task on the Domain of German Hotel Reviews

Aspect-Based Sentiment Analysis (ABSA) plays a crucial role in understanding fine-grained customer feedback, particularly in domains like hospitality where specific aspects of service often influence overall satisfaction. However, non-English languages such as German face a scarcity of readily available corpora and evaluated methods for ABSA, making it a challenging problem. This paper addresses this gap by utilizing BERT-based transformer models, known for their exceptional performance in context-sensitive natural language processing tasks, to perform ABSA in a multi-label classification setting. We demonstrate our approach on a novel dataset of German hotel reviews that we have collected and annotated from TripAdvisor , thus contributing a new resource to the field and proving the effectiveness of our methodology. With achieving a micro f1-score of up to 0.91 for aspect category classification and 0.81 for end-to-end ABSA, our approach aligns with the performance of similar meth-ods on other German-language datasets and surpasses performance achieved on English-language datasets in the hotel domain.
Personalized circuit modeling captures variation in cortical functional connectivity

Functional magnetic resonance imaging (fMRI) of the human cortex reveals patterns of correlated neural dynamics that are individual-specific and associated with phenotypic variation. However, circuit mechanisms underlying individual variation in functional connectivity (FC) are not well understood. Here, we fit individual-level FC patterns with a biophysically-based circuit model of large-scale cortical dynamics. This model is fit with a small number of neurophysiologically interpretable parameters, and incorporates a hierarchical gradient in local synaptic strengths across cortex parameterized via the structural MRI-derived T1w/T2w map. We applied our modeling framework to resting-state fMRI FC from a large cohort of subjects (N=842) from the Human Connectome Project. We found that the model captures a substantial portion of individual variation in FC, especially with personalized degrees of local synaptic specialization along the hierarchical gradient. Furthermore, the model can capture to the within-subject variation in FC across scans. Empirically, we found that principal modes of individual variation in FC follow interpretable topographic patterns. We developed a framework to assess model expressivity via how these empirical modes of FC variation align with variations in simulated FC induced by parameter perturbations. This framework reveals a straightforward mapping between key parameters and the leading modes of variation across subjects and provides a principled approach to extending computational models. Collectively, our modeling results establish a foundation for personalized computational modeling of functional dynamics in large-scale brain circuits.
Phase delays between mouse globus pallidus neurons entrained by common oscillatory drive arise from their intrinsic properties, not their coupling

A hallmark of Parkinson’s disease is the appearance of correlated oscillatory discharge throughout the cortico-basal ganglia (BG) circuits. In the primate globus pallidus (GP), where the discharge of GP neurons is normally uncorrelated, pairs of GP neurons exhibit oscillatory spike correlations with a broad distribution of pairwise phase delays in experimental parkinsonism. The transition to oscillatory correlations is considered an indication of the collapse of the normally segregated information channels traversing the BG, and the large phase delays are thought to reflect pathological changes in synaptic connectivity in the BG. Here we study the structure and phase delays of correlations measured from independent neurons in the mouse external GP (GPe) subjected to an identical 1-100 Hz sinusoidal drive. We find that spectral modes of a GPe neuron’s empirical instantaneous phase response curve (iPRC), elucidate at what phases of the oscillatory drive the GPe neuron locks when it is entrained, and the distribution of phases at which it fires when it is not. We show, mathematically, that the spike cross-intensity function (CIF) is the cross-correlation function of the spike phase distributions of a neuronal pair. Moreover, the distribution of GPe CIF phase delays arises from the diversity of iPRCs, and is broadened when the neurons become entrained. Modeling GPe networks with realistic intranuclear connectivity demonstrates that the connectivity decorrelates GPe neurons without affecting CIF phase delays. Thus, common oscillatory drive gives rise to GPe correlations whose diverse structure and pairwise phase delays reflect their intrinsic properties as captured by their iPRCs. Author’s summary The external globus pallidus (GPe) is a hub in the basal ganglia, whose neurons impose a barrage of inhibitory synaptic currents on neurons of the subthalamic nucleus, substantia nigra and internal globus pallidus. GPe neurons normally fire independently, but in experimental parkinsonism, they become correlated in the frequency range associated with the pathological rhythms seen in human Parkinson’s disease, raising the possibility that they may be generators of the pathological oscillation. We drove individual pallidal neurons with an oscillatory input over a wide range of frequencies. Cross-correlations of these neurons reproduced many of the features seen in parkinsonism, suggesting that their correlated oscillations might derive from a shared input rather than internal interconnections.
Delayed cerebrovascular dysfunction and social deficits after traumatic brain injury

Traumatic brain injury (TBI) survivors face debilitating long-term psychosocial consequences, including social isolation and depression. Acute TBI modifies neurovascular physiology and behavior but a gap in our understanding are the chronic physiological implications of altered brain perfusion on behavioral activities, particularly social interactions. We investigated longitudinal functional vascular networks across the brain for 2-months post- TBI and its impact on social behavior. Adult C57/BL6 male mice received a moderate cortical TBI. Behavior (foot-fault, open-field, 3-chamber social preference) was assessed at baseline, 3-, 7-, 14-, 30-, and 60-days post injury (dpi) followed by magnetic resonance imaging (MRI, 9.4T). Anatomical MRI (T2-weighted), dynamic susceptibility contrast (DSC) perfusion weighted MRI (PWI) were acquired at each temporal epoch. After the final 60dpi MRI, animals underwent transcardial perfusion fixation to map angioarchitecture. MRI data were analyzed using standardized protocols followed by cross-correlations between social behavior, cerebral perfusion, and vascular metrics. Social behavior deficits at 60dpi emerged as reduced interaction with a familiar cage-mate (partner). We observed multiphasic decrements in cerebral blood flow (CBF) encompassing lesion and perilesional cortex where acute reductions at 3-14dpi partially recovered by 30dpi, followed by significant reductions in perfusion at 60dpi. The CBF perturbations extended antero-posteriorly from the ipsilateral TBI impact site but also adulterated contralateral brain regions. CBF reductions impacted regions known to regulate social behavior including hippocampus, hypothalamus, and rhinal cortex. Alongside perfusion deficits at 60dpi, social isolation in TBI-mice emerged with a significant decline in preference to spend time with a cage mate. Cortical vascular density was also reduced corroborating the decline in brain perfusion and social interaction. Thus, the novel temporal neurovascular loss, and subsequent recovery followed by chronic decrements are broadly reflected by social interaction perturbations. Our correlations strongly implicate a linkage between vascular density, cerebral perfusion, and social interactions, where early evaluation can potentially predict long-term outcomes. Thus, our study provides a clinically relevant timeline of alterations in functional vascular recovery that can guide research for future therapeutics.
A multiscale model of the mammalian liver circadian clock supports synchronization of autonomous oscillations by intercellular communication

Expression of core circadian clock genes in hepatocytes across the liver lobule is temporally synchronized despite cell-autonomous oscillations in gene expression. This spatial synchronization has been attributed to an unknown intercellular coupling mechanism. Here we have developed multicellular computational models of the murine liver lobule with and without intercellular coupling to investigate the role of synchronization in circadian gene expression. Our models demonstrated that intercellular coupling was needed to generate sustained circadian oscillations with a near 24-hour period. Without coupling the simulated period was variable within the 21-28-hour range. Further model analysis revealed that a robust near-24-hour oscillation period can be generated with a wide range of circadian protein degradation rates. In contrast, only a small window of circadian gene transcription rates was able to generate realistic oscillatory periods. The coupled model accurately captured the temporal dynamics of circadian genes derived from single-nuclei transcriptomic data. Overall, this study provides novel insights into the mammalian hepatic circadian clock through modeling of spatial and temporal gene expression patterns and data-driven analysis.
Theta-burst direct electrical stimulation remodels human brain networks

Patterned brain stimulation is a powerful therapeutic approach for treating a wide range of brain disorders. In particular, theta-burst stimulation (TBS), characterized by rhythmic bursts of 3-8 Hz mirroring endogenous brain rhythms, is delivered by transcranial magnetic stimulation to improve cognitive functions and relieve symptoms of depression. However, the mechanism by which TBS alters underlying neural activity remains poorly understood. In 10 pre-surgical epilepsy participants undergoing intracranial monitoring, we investigated the neural effects of TBS. Employing intracranial EEG (iEEG) during direct electrical stimulation across 29 stimulation cortical locations, we observed that individual bursts of electrical TBS consistently evoked strong neural responses spanning broad cortical regions. These responses exhibited dynamic changes over the course of stimulation presentations including either increasing or decreasing voltage responses, suggestive of short-term plasticity in the amplitude of the local field potential voltage response. Notably, stronger stimulation augmented the mean amplitude and distribution of TBS responses , leading to greater proportion of recording sites demonstrating short-term plasticity. TBS responses were stimulation site-specific and propagated according to the underlying functional brain architecture, as stronger responses were observed in regions with strong baseline effective (cortico-cortical evoked potentials) and functional (low frequency phase locking) connectivity. Further, our findings enabled the predictions of locations where both TBS responses and change in these responses (e.g. short-term plasticity) were observed. Future work may focus on using pre-treatment connectivity alongside other biophysical factors to personalize stimulation parameters, thereby optimizing induction of neuroplasticity within disease-relevant brain networks.
A Macroscopic Quantum Heat Engine: Nested Resonant Structures and the Entropic Stability of Life

In search of the life spirit, or energy, that flows through the animal heart to make it beat, theologians, philosophers and scientists have for over two millennia been dissecting the heart down to its most basic structures. Recently, researchers have reached the pinnacle of the anatomists’ dream by determining the structure of a cardiac myosin thick filament at roughly twenty angstroms resolution. Yet, dissected down to individual atoms, the energy that flows through the heart is still nowhere to be found. The reason is that there exists in the whole of muscle a form of energy that does not exist in its constituent parts. To identify this energy, we must determine how the billions of randomly moving atoms that make up contractile proteins in muscle scale up to generate the steady unidirectional contraction of muscle. Here, I show that degrees of freedom of small structural elements physically, irreversibly, and discretely collapse to create larger structures with energy landscapes defined by entropic wells quantized by discrete degrees of freedom. The collapse of degrees of freedom across multiple thermal scales results in nested resonant structures from atoms on up to the filamentous structure of muscle, demonstrating that muscle is a quantum heat engine.
PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments

Summary Structural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software that challenge reproducibility and limit their implementation by highly specialised researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F performance.
Tuning a mechanical model to biological reality: A case study in the LaMSA system of the trap-jaw ant Strumigenys

Understanding the relationship between morphology and movement in biomechanical systems, particularly those composed of multiple complex elements, presents challenges due to the nonlinear nature of the interaction between components. This study focuses on the mandibular closing mechanisms in ants, specifically comparing muscle-driven actuation (MDA) and latch-mediated spring actuation (LaMSA) in the genus Strumigenys. Analyzing 3D structural data from diverse Strumigenys species, we employ mathematical models for both LaMSA and MDA systems. Our findings reveal distinct patterns of mechanical sensitivity between the two models, with sensitivity varying across kinematic output metrics. We explore the performance transition between MDA and LaMSA systems by incorporating biological data and correlations between morphological parameters into the models. In these models tuned specifically to Strumigenys, we find the LaMSA mechanism outperforms MDA at small relative mandible mass. Notably, the location and abruptness of the performance transition differs among various kinematic performance metrics. Overall, this work contributes a novel approach to understanding form-function relationships in complex biomechanical systems. By using morphological data to calibrate a general biomechanical model for a particular group, it strikes a balance between simplicity and specificity and allows for conclusions that are uniquely tuned to the morphological characteristics of the group.