Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-accelerated Discovery of Altermagnetic Materials

Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although altermagnets have been found to possess many exotic physical properties, the very limited availability of known altermagnetic materials (e.g., 14 confirmed materials) hinders the study of such properties. Hence, discovering more types of altermagnetic materials is crucial for a comprehensive understanding of altermagnetism and thus facilitating new applications in the next-generation information technologies, e.g., storage devices and high-sensitivity sensors. Here, we report 25 new altermagnetic materials that cover metals, semiconductors, and insulators, discovered by an AI search engine unifying symmetry analysis, graph neural network pre-training, optimal transport theory, and first-principles electronic structure calculation. The wide range of electronic structural characteristics reveals that various novel physical properties manifest in these newly discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr effect, and topological property. Noteworthy, we discovered 8 i-wave altermagnetic materials for the first time. Overall, the AI search engine performs much better than human experts and suggests a set of new altermagnetic materials with unique properties, outlining its potential for accelerated discovery of the materials with targeting properties.
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either apply a penalty term along the z-axis to remove inconsistencies or reconstruct the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise levels in PET images are affected by several other factors in clinical settings, e.g. scan time, medical history, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a Dose-aware Diffusion model for 3D low-dose PET imaging (DDPET-3D) to address these challenges. We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models. The code is available at: https://github.com/xxx/xxx.
Blind Federated Learning via Over-the-Air q-QAM

In this work, we investigate federated edge learning over a fading multiple access channel. To alleviate the communication burden between the edge devices and the access point, we introduce a pioneering digital over-the-air computation strategy employing q-ary quadrature amplitude modulation, culminating in a low latency communication scheme. Indeed, we propose a new federated edge learning framework in which edge devices use digital modulation for over-the-air uplink transmission to the edge server while they have no access to the channel state information. Furthermore, we incorporate multiple antennas at the edge server to overcome the fading inherent in wireless communication. We analyze the number of antennas required to mitigate the fading impact effectively. We prove a non-asymptotic upper bound for the mean squared error for the proposed federated learning with digital over-the-air uplink transmissions under both noisy and fading conditions. Leveraging the derived upper bound, we characterize the convergence rate of the learning process of a non-convex loss function in terms of the mean square error of gradients due to the fading channel. Furthermore, we substantiate the theoretical assurances through numerical experiments concerning mean square error and the convergence efficacy of the digital federated edge learning framework. Notably, the results demonstrate that augmenting the number of antennas at the edge server and adopting higher-order modulations improve the model accuracy up to 60\%.
Data Factors for Better Compositional Generalization

Recent diagnostic datasets on compositional generalization, such as SCAN (Lake and Baroni, 2018) and COGS (Kim and Linzen, 2020), expose severe problems in models trained from scratch on these datasets. However, in contrast to this poor performance, state-of-the-art models trained on larger and more general datasets show better generalization ability. In this work, to reconcile this inconsistency, we conduct an empirical analysis by training Transformer models on a variety of training sets with different data factors, including dataset scale, pattern complexity, example difficulty, etc. First, we show that increased dataset complexity can lead to better generalization behavior on multiple different generalization challenges. To further understand this improvement, we show two axes of the benefit from more complex datasets: they provide more diverse examples so compositional understanding becomes more effective, and they also prevent ungeneralizable memorization of the examples due to reduced example repetition frequency. Finally, we explore how training examples of different difficulty levels influence generalization differently. On synthetic datasets, simple examples invoke stronger compositionality than hard examples do. On larger-scale real language datasets, while hard examples become more important potentially to ensure decent data coverage, a balanced mixture of simple and hard examples manages to induce the strongest generalizability. The code and data for this work are available at https://github.com/owenzx/data4comp
Can LLMs Follow Simple Rules?

As Large Language Models (LLMs) are deployed with increasing real-world responsibilities, it is important to be able to specify and constrain the behavior of these systems in a reliable manner. Model developers may wish to set explicit rules for the model, such as"do not generate abusive content", but these may be circumvented by jailbreaking techniques. Existing evaluations of adversarial attacks and defenses on LLMs generally require either expensive manual review or unreliable heuristic checks. To address this issue, we propose Rule-following Language Evaluation Scenarios (RuLES), a programmatic framework for measuring rule-following ability in LLMs. RuLES consists of 14 simple text scenarios in which the model is instructed to obey various rules while interacting with the user. Each scenario has a programmatic evaluation function to determine whether the model has broken any rules in a conversation. Our evaluations of proprietary and open models show that almost all current models struggle to follow scenario rules, even on straightforward test cases. We also demonstrate that simple optimization attacks suffice to significantly increase failure rates on test cases. We conclude by exploring two potential avenues for improvement: test-time steering and supervised fine-tuning.
Use of stable isotopes and mercury to assess trophic positions of black carp and other large fishes in the Red-Atchafalaya River system, Louisiana, USA

—The black carp Mylopharyngodon piceus is a large (>1 m long) riverine fish from eastern Asia introduced into the United States via the aquaculture industry. A wild population has been present in the lower Mississippi River basin since the early 1990s, but little is known about the ecological effect of black carp in invaded environments. In its native range, black carp feed almost exclusively on mollusks. In U.S. waters, they likely prey on native mussels, but few wild-caught specimens have been examined by biologists and all have had empty gastrointestinal tracts. In lieu of stomach content data, we examined isotopic values (d13C and d15N) and mercury (Hg) concentrations in muscle tissue of black carp and 10 other large nonnative and native fish species captured in the Red–Atchafalaya River system of Louisiana, USA. Trophic position estimates derived from d15N values ranged from 2.0 for grass carp Ctenopharyngodon idella to 4.8 for blue catfish Ictalurus furcatus and flathead catfish Pylodictis olivaris. Adult black carp had a d15N value (13.2‰), indicating a trophic level of 3.5. Mean total Hg concentrations ranged from 0.02 mg/g in grass carp to 0.27 mg/g in bigmouth buffalo Ictiobus cyprinellus, in black carp 0.17 mg/g; Hg increased with increasing d15N, indicating biomagnification. The limited numbers of taxa and small samples sizes, as well as constraints in methods used, do not allow confirmation that wild black carp are consuming native mollusks. However, our stable isotope results do provide evidence that its diet is similar to other large fish species inhabiting the Red–Atchafalaya system considered to be benthic invertivores, including some known to prey on freshwater mollusks (i.e., smallmouth buffalo I. bubalus and nonnative common carp Cyprinus carpio). * Corresponding author: lnico@usgs.gov Introduction The black carp Mylopharyngodon piceus (Richardson, 1846), family Cyprinidae, is one of four major Asian river carps currently present in the Mississippi River basin of North America (Nico et al. 2005). Others include grass carp Ctenopharyngodon idella (Valenciennes, 1844), bighead carp Hypophthalmichthys nobilis (Richardson, 1845), and silver carp H. molitrix (Valenciennes, 1844). Among the four, the black carp was the most recent Asian carp to be discovered in the wild in North America and remains the most enigmatic. In particular, we know little about black carp trophic ecology, geographic distribution, abundance, reproductive status, and ecological effects. Paucity
A Matroidal Framework for Network-Error Correcting Codes

Matroidal networks were introduced by Dougherty et al. and have been well studied in the recent past. It was shown that a network has a scalar linear network coding solution if and only if it is matroidal associated with a representable matroid. A particularly interesting feature of this development is the ability to construct (scalar and vector) linearly solvable networks using certain classes of matroids. Furthermore, it was shown through the connection between network coding and matroid theory that linear network coding is not always sufficient for general network coding scenarios. The current work attempts to establish a connection between matroid theory and network-error correcting and detecting codes. In a similar vein to the theory connecting matroids and network coding, we abstract the essential aspects of linear network-error detecting codes to arrive at the definition of a matroidal error detecting network (and similarly, a matroidal error correcting network abstracting from network-error correcting codes). An acyclic network (with arbitrary sink demands) is then shown to possess a scalar linear error detecting (correcting) network code if and only if it is a matroidal error detecting (correcting) network associated with a representable matroid. Therefore, constructing such network-error correcting and detecting codes implies the construction of certain representable matroids that satisfy some special conditions, and vice versa. We then present algorithms that enable the construction of matroidal error detecting and correcting networks with a specified capability of network-error correction. Using these construction algorithms, a large class of hitherto unknown scalar linearly solvable networks with multisource, multicast, and multiple-unicast network-error correcting codes is made available for theoretical use and practical implementation, with parameters, such as number of information symbols, number of sinks, number of coding nodes, error correcting capability, and so on, being arbitrary but for computing power (for the execution of the algorithms). The complexity of the construction of these networks is shown to be comparable with the complexity of existing algorithms that design multicast scalar linear network-error correcting codes. Finally, we also show that linear network coding is not sufficient for the general network-error correction (detection) problem with arbitrary demands. In particular, for the same number of network errors, we show a network for which there is a nonlinear network-error detecting code satisfying the demands at the sinks, whereas there are no linear network-error detecting codes that do the same.
Multifaceted effects of bycatch mitigation measures on target/non-target species for pelagic longline for bycatch management

The pelagic longline fishery, in an effort to reduce bycatch of sea turtles, have developed and deployed 21 fisheries bycatch mitigation techniques such as replacing J/tuna hooks and squid bait with circle hooks 22 and whole fish bait. However, little emphasis has been placed on the side effects of bycatch mitigation 23 measures on endangered species other than target bycatch species. Several previous studies of the side 24 effects have been marred by lack of control for the covariates. Here, based on long-term data obtained 25 from research cruises by a pelagic longline vessel, we examined the effects of using circle hooks and 26 whole fish bait to replace squid bait on the fishing mortality of target and non-target fishes, and also 27 bycatch species. A quantitative evaluation analysis of our results, based on a Bayesian approach, 28 showed the use of circle hooks to increase mouth hooking in target and bycatch species, and their size to 29 be proportional to the magnitude of the effect. Although deploying circle hooks increased catch per unit 30 effort (CPUE) and fishing mortality per unit effort (MPUE), changing the bait species from squid to fish 31 clearly had a far greater impact on shark mortality than the use of circle hooks. Because the impact of the 32 introduction of bycatch mitigation measures on species other than the focused bycatch species is 33 non-negligible, a quantitative assessment of bycatch mitigation-related fishing mortality is critical before 34 introducing such measures. 35 rather shortfin swordfish, and in these three species, haulback after mouth hooking also lower than that after hook has reported to increase the likelihood of fatal damage to internal Although previous studies to attach satellite tags to white marlin Tetrapturus albidus by recreational fishing using circle hooks and subsequently have reduced the the no data post-release collected in the different of frequent the relative in
Longitudinal Survey of Astrovirus infection in different bat species in Zimbabwe: Evidence of high genetic Astrovirus diversity

Astroviruses (AstVs) have been discovered in over 80 animal species including diverse bat species and avian species. A study on Astrovirus circulation and diversity in different insectivorous and frugivorous chiropteran species roosting in trees, caves and building basements was carried out at 11 different sites across Zimbabwe. Pooled and individual faecal sampling methods were used for this study, with collection dates ranging from June 2016 to July 2021. In two sites, Magweto and Chirundu, sampling was carried out at monthly intervals from August 2020 to July 2021. Astroviruses and bat mitochondrial genes were amplified using pan-AstVs and CytB /12S RNA PCR systems respectively. Phylogenetic analysis of the RdRp Astrovirus sequences revealed a high genetic diversity of astroviruses. All the bat astroviruses tested in this study clustered with the Mamastrovirus genus. Two distinct groups of amplified sequences were identified. One group was comprised of sequences isolated from Hipposideros, Rhinolophus and Eidolon helvum spp. clustered with Human Astrovirus strains: HuAstV types1-6, HuAstV-MLB1-3 and HuAstV-VA-1. The second group comprising the majority of the sequences clustered with different strains of Bat AstVs. Results from the longitudinal study at Magweto and Chirundu showed an overall AstV prevalence of 13.7% and 10.4% respectively. Peaks of AstV infection at Chirundu coincided with the period when juveniles are 4-6 months old. By combining data from our previous work on Coronaviruses, we also anaylzed co-infection of bats with Coronaviruses and Astroviruses at Magweto and Chirundu sites for which the prevalence of co-infection was 2.6% and 3.5% respectively.
Microbial Metamorphosis: Symbiotic bacteria and fungi proliferate during diapause and may enhance overwintering survival in a solitary bee

Host-microbe interactions underlie the development and fitness of many macroorganisms including bees. While many social bees benefit from vertically transmitted gut bacteria, solitary bees, which comprise the vast majority of species diversity within bees, lack a specialized gut microbiome. Here we examine the composition and abundance of bacteria and fungi throughout the complete life cycle of a ground-nesting solitary bee Anthophora bomboides standfordiana. In contrast to expectations, immature bee stages maintain a distinct core microbiome consisting of Actinobacteria and fungi in the genus Moniliella. Diapausing larval bees hosted the most abundant and distinctive bacteria and fungi, attaining 33 and 52 times their initial copy number, respectively. We tested two adaptive hypotheses regarding microbial functions for overwintering bees. First, using isolated bacteria and fungi, we found that Streptomyces from brood cells inhibited the growth of multiple pathogenic filamentous fungi, suggesting a role in pathogen protection during the long period of diapause. Second, sugar alcohol composition changed in tandem with major changes in microbial abundance suggesting links with bee metabolism or overwintering biology. Our results suggest that Anthophora bomboides hosts a conserved core microbiome that may provide key fitness advantages through larval development and overwintering, and raises the question of how this microbiome is transmitted between generations. The present work suggests that focus on adult gut microbiomes in solitary bees may overlook microbial symbionts within brood cells that could play diverse roles in bee fitness, and that exploration of microbes associated with immature bees may uncover novel microbial effects on insect hosts.
Human Endogenous Retrovirus Expression is Dynamically Regulated in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive, debilitating neurodegenerative disease that afflicts approximately every 1000th individual. Recently, activation of genomic transposable elements (TE) has been suggested as a potential driver of PD onset. However, it is unclear where, when, and to what extent TEs are dysregulated in PD. Here, we performed a multi-tissue transcriptional analysis of multiple patient cohorts and identified TE transcriptional activation as a hallmark of PD. We find that PD patients exhibit up-regulation primarily of human endogenous retrovirus (HERV) transcripts in prefrontal cortex tissue, prefrontal neurons as well as in blood, and we demonstrate that TE activation in the blood is highest at the time of PD diagnosis. Supporting a potentially causal association between ERV dysregulation and PD heterogeneity, reduced gene dosage of the TE repressor Trim28 triggers transcriptional changes highly correlated to those measured in animal models of synucleinopathy (PFF-injection), and importantly, to those exhibited by patients themselves. These data identify ERV up-regulation as a common feature of central and peripheral PD etiology, and highlight potential roles for Trim28-dependent TEs in stratifying and monitoring PD and treatment compliance.
Loss of the brain-liver axis prevents hepatic steatosis in mice

Hepatic lipid metabolism is regulated by the autonomic nervous system of the liver, with the sympathetic innervation being extensively studied, while the parasympathetic efferent innervation is less understood despite its potential importance. In this study, we investigate the consequences of disrupted brain-liver communication on hepatic lipid metabolism in mice exposed to obesogenic conditions. We found that a subset of hepatocytes and the bile duct are innervated by parasympathetic nerves originating from the dorsal motor nucleus of the vagus. The elimination of the brain-liver axis by deleting parasympathetic cholinergic neurons innervating the liver prevents hepatic steatosis and promots browning of inguinal white adipose tissue (ingWAT). The loss of the brain-liver axis also raises hepatic Cyp7b1 expression and fasting serum bile acid levels. Furthermore, knockdown of the G protein-coupled bile acid receptor 1 gene in ingWAT reverses the beneficial effects of the loss of the brain-liver axis, leading to the reappearance of hepatic steatosis in the experimental groups. However, deleting the brain-liver axis has a small but significant effect on body weight, which is accompanied by an increase in energy expenditure. Therefore, altering parasympathetic cholinergic innervation of the liver could offer a potential therapeutic approach for enhancing hepatic lipid metabolism in obesity and diabetes.
Limitations of line-scan MRI for directly measuring neural activity

Several groups have reported using 2D line-scan MRI sequences in humans and mice to directly measure neural responses to stimuli (the “DIANA response”). Other groups have been unable to replicate the DIANA response, even with higher field strength and more repetitions. Part of this discrepancy is likely due to a limited understanding of the noise profile of the line-scan MRI sequence: specifically, the consequences of deviations from the assumption of stationarity between each line acquisition. Here, we collected data using an MRI line-scan method while human subjects viewed a blank screen with the purpose of studying noise unique to the acquisition sequence. We found temporal fluctuations in the reconstructed time series from localized groups of voxels that could easily be confused with neural responses to stimuli. These fluctuations were present both in the head and in the surrounding empty volume along the span of the phase-encoding direction from the head. The timing of these fluctuations varied systematically and smoothly along the phase-encoding direction. These features can be explained by a model that accounts for the acquisition sequence and incorporates time-varying contrast fluctuations in the imaging substrate. Using the model, we quantify the amount of cortical- and scan-averaging one might need to reliably distinguish a DIANA response from noise.