Miracle-dz/UniFAR-FTUs huggingface dataset:
Deletion of Jazf1 gene causes early growth retardation and insulin resistance in mice

Significance Despite iterative associations between common gene variants at zinc finger protein 1 (JAZF1) and type 2 diabetes (T2D) as well as height, whether variants in JAZF1 cause these traits has not been established. We show that Jazf1 deletion leads to early growth retardation and late onset insulin resistance in Jazf1 knockout (KO) mice by regulating insulin-like growth factor 1 (IGF-1). Aging and high-fat diet increase the susceptibility of KO mice to T2D, as shown by increased fat mass and insulin resistance. Growth hormone-induced IGF-1 expression is inhibited by JAZF1 knockdown. Therefore, this study provides a functional basis in vivo by which genetic alteration in a single gene, JAZF1, affects stature and T2D.
Reliability of novel centre of pressure measures of quiet standing balance in people with chronic stroke

Background: People with stroke often have asymmetric motor impairment. Investigating asymmetries in, and dynamic properties of, centre of pressure movement during quiet standing can inform how well balance is controlled. Research question: What are the test-retest reliabilities of novel measures of quiet standing balance control in people with chronic stroke? Methods: Twenty people with chronic stroke (>6 months post-stroke), who were able to stand for at least 30 seconds without support, were recruited. Participants completed two 30-second quiet standing trials in a standardized position. Novel measures of quiet standing balance control included: symmetry of variability in centre of pressure displacement and velocity, between-limb synchronization, and sample entropy. Root mean square of centre of pressure displacement and velocity in the antero-posterior and medio-lateral directions were also calculated. Intraclass correlation coefficients (ICCs) were used to determine test-retest reliability, and Bland-Altman plots were created to examine proportional biases. Results: ICC(3,2) were between 0.79 and 0.95 for all variables, indicating 'good' to 'excellent' reliability (>0.75). However, ICC(3,1) for symmetry indices and between-limb synchronization were <0.75. Bland-Altman plots revealed possible proportional biases for root mean square of medio-lateral centre of pressure displacement and velocity and between-limb synchronization, with larger between-trial differences for participants with worse values. Significance: These findings suggest that centre of pressure measures extracted from a single 30-second quiet standing trial may have sufficient reliability for some research studies in chronic stroke. However, for clinical applications, the average of at least two trials may be required.
Opinions of former jail residents about self-collection of SARS-CoV-2 specimens, paired with wastewater surveillance: A qualitative study rapidly examining acceptability of COVID-19 mitigation measures

In year one of the COVID-19 epidemic, the incidence of infection for US carceral populations was 5.5-fold higher than that in the community. Prior to the rapid roll out of a comprehensive jail surveillance program of Wastewater-Based Surveillance (WBS) and individual testing for SARS-CoV-2, we sought the perspectives of formerly incarcerated individuals regarding mitigation strategies against COVID-19 to inform acceptability of the new program. In focus groups, participants discussed barriers to their receiving COVID-19 testing and vaccination. We introduced WBS and individual nasal self-testing, then queried if wastewater testing to improve surveillance of emerging outbreaks before case numbers surged, and specimen self-collection, would be valued. The participants' input gives insight into ways to improve the delivery of COVID-19 interventions. Hearing the voices of those with lived experiences of incarceration is critical to understanding their views on infection control strategies and supports including justice-involved individuals in decision-making processes regarding jail-based interventions.
IFN-γR/STAT1 signaling in recipient hematopoietic antigen-presenting cells suppresses graft-versus-host disease

The absence of IFN-γ receptor (IFN-γR) or STAT1 signaling in donor cells has been shown to result in reduced induction of acute graft-versus-host disease (GVHD). In this study, we unexpectedly observed increased activation and expansion of donor lymphocytes in both lymphohematopoietic organs and GVHD target tissues of IFN-γR/STAT1–deficient recipient mice, leading to rapid mortality following the induction of GVHD. LPS-matured, BM-derived Ifngr1–/– Stat1–/– DCs (BMDCs) were more potent allogeneic stimulators and expressed increased levels of MHC II and costimulatory molecules. Similar effects were observed in human antigen-presenting cells (APCs) with knockdown of Stat1 by CRISPR/Cas9 and treatment with a JAK1/2 inhibitor. Furthermore, we demonstrated that the absence of IFN-γR/STAT1 signaling in hematopoietic APCs impaired the presentation of exogenous antigens, while promoting the presentation of endogenous antigens. Thus, the indirect presentation of host antigens to donor lymphocytes was defective in IFN-γR/STAT1–deficient, donor-derived APCs in fully donor chimeric mice. The differential effects of IFN-γR/STAT1 signaling on endogenous and exogenous antigen presentation could provide further insight into the roles of the IFN-γ/STAT1 signaling pathway in the pathogenesis of GVHD, organ rejection, and autoimmune diseases.
Landau Singularities of the 7-Point Ziggurat I

The (first-type Landau) singularities of any $n$-point massless planar Feynman integral, at any loop order, are encoded in a certain ''ziggurat graph''. We compute the leading Landau singularities of the 7-point ziggurat graph and find perfect agreement with a subset of the ''heptagon symbol alphabet'' that has appeared in the context of planar $\mathcal{N}=4$ super-Yang-Mills theory. The remaining heptagon symbol letters are found in subleading Landau singularities, which we address in a companion paper.
SimCS: Simulation for Domain Incremental Online Continual Segmentation

Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods.
The Mid-infrared Molecular Inventory toward Orion IRc2

We present the first high spectral resolution mid-infrared survey in the Orion BN/KL region, covering 7.2–28.3 μm. With SOFIA/EXES, we target the enigmatic source Orion IRc2. While this is in the most prolifically studied massive star-forming region, longer wavelengths and molecular emission lines dominated previous spectral surveys. The mid-infrared observations in this work access different components and molecular species in unprecedented detail. We unambiguously identify two new kinematic components, both chemically rich with multiple molecular absorption lines. The “blue clump” has v LSR = −7.1 ± 0.7 km s−1, and the “red clump” has 1.4 ± 0.5 km s−1. While the blue and red clumps have similar temperatures and line widths, molecular species in the blue clump have higher column densities. They are both likely linked to pure rotational H2 emission also covered by this survey. This work provides evidence for the scenario that the blue and red clumps are distinct components unrelated to the classic components in the Orion BN/KL region. Comparison to spectroscopic surveys toward other infrared targets in the region show that the blue clump is clearly extended. We analyze, compare, and present in-depth findings on the physical conditions of C2H2, 13CCH2, CH4, CS, H2O, HCN, H13CN , HNC, NH3, and SO2 absorption lines and an H2 emission line associated with the blue and red clumps. We also provide limited analysis of H2O and SiO molecular emission lines toward Orion IRc2 and the atomic forbidden transitions [Fe ii], [S i], [S iii], and [Ne ii].
Novelty Detection for Election Fraud: A Case Study with Agent-Based Simulation Data

In this paper, we propose a robust election simulation model and independently developed election anomaly detection algorithm that demonstrates the simulation's utility. The simulation generates artificial elections with similar properties and trends as elections from the real world, while giving users control and knowledge over all the important components of the elections. We generate a clean election results dataset without fraud as well as datasets with varying degrees of fraud. We then measure how well the algorithm is able to successfully detect the level of fraud present. The algorithm determines how similar actual election results are as compared to the predicted results from polling and a regression model of other regions that have similar demographics. We use k-means to partition electoral regions into clusters such that demographic homogeneity is maximized among clusters. We then use a novelty detection algorithm implemented as a one-class Support Vector Machine where the clean data is provided in the form of polling predictions and regression predictions. The regression predictions are built from the actual data in such a way that the data supervises itself. We show both the effectiveness of the simulation technique and the machine learning model in its success in identifying fraudulent regions.
Direct Statistical Constraints on the Natal Kick Velocity of a Black Hole in an X-Ray Quiet Binary

In recent years, a handful of “dark” binaries have been discovered with a nonluminous compact object. Astrometry and radial velocity measurements of the bright companion allow us to measure the post-supernova orbital elements of such a binary. In this paper, we develop a statistical formalism to use such measurements to infer the pre-supernova orbital elements, and the natal kick imparted by the supernova (SN). We apply this formalism to the recent discovery of an X-ray quiet binary with a black hole, VFTS 243, in the Large Magellanic Cloud. Assuming an isotropic, Maxwellian distribution on natal kicks and using broad agnostic mass priors, we find that kick velocity can be constrained to V k < 72 km s−1 at 90% confidence. We find that a Blaauw kick cannot be ruled out, and that at least about 0.6M ⊙ was lost during the supernova with 90% confidence. The pre-SN orbital separation is found to be robustly constrained to be around 0.3 au.
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding

Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However, this success cannot be directly transferred to 3D scenarios due to the inaccessibility of large-scale 3D-text pairs. To this end, we propose to distill knowledge encoded in pretrained vision-language (VL) foundation models through captioning multi-view images from 3D, which allows explicitly associating 3D and semantic-rich captions. Further, to foster coarse-to-fine visual-semantic representation learning from captions, we design hierarchical 3D-caption pairs, leveraging geometric constraints between 3D scenes and multi-view images. Finally, by employing contrastive learning, the model learns language-aware embeddings that connect 3D and text for open-vocabulary tasks. Our method not only remarkably outperforms baseline methods by 25.8% ~ 44.7% hIoU and 14.5% ~ 50.4% hAP50 in open-vocabulary semantic and instance segmentation, but also shows robust transferability on challenging zero-shot domain transfer tasks. See the project website at https://dingry.github. io/projects/PLA.
Towards Generalized Open Information Extraction

Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside from the training corpus, which certainly violates the initial task principle of domain-independence. In this paper, we propose to advance OpenIE towards a more realistic scenario: generalizing over unseen target domains with different data distributions from the source training domains, termed Generalized OpenIE. For this purpose, we first introduce GLOBE, a large-scale human-annotated multi-domain OpenIE benchmark, to examine the robustness of recent OpenIE models to domain shifts, and the relative performance degradation of up to 70% implies the challenges of generalized OpenIE. Then, we propose DragonIE, which explores a minimalist graph expression of textual fact: directed acyclic graph, to improve the OpenIE generalization. Extensive experiments demonstrate that DragonIE beats the previous methods in both in-domain and out-of-domain settings by as much as 6.0% in F1 score absolutely, but there is still ample room for improvement.
A ductility-strength metric for chemically complex alloys

Local lattice distortions (LLD) – relative to average (x-ray) lattice positions – are expected in multi-principal element alloys (MPEAs) due to the varying complex chemical environments. We suggest a dimensionless metric to characterize ductility in MPEAs using LLD. With its innate quantum-mechanical origins, ductility is clearly affected by LLD arising from increased atomic charge transfer due to electronegativity differences in each local environment (often designated by valence electron count (VEC)). To test this metric, we chose body-centered cubic (bcc) refractory alloys that exhibit a range of ductility-brittleness. The metric was established by combining the weighted-average VEC ( ∆𝑤 #$% ) and intrinsic parameters ( ∆𝑢, √[∆𝑢] + ) related to distortion from the average lattice. The electronic-structure results were calculated using density-functional theory (DFT) methods. The quantitative rank ordering of ductile-to-brittle MPEAs shows good agreement with recent mean-field estimates and experiments. Our results permit a quick ductility assessment to guide the design of more ductile high-temperature MPEAs, and potentially accelerated further if combined with machine-learning.
TF-Net: Deep Learning Empowered Tiny Feature Network for Night-time UAV Detection

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is required to that could efficiently tackle this problem. Infrared cameras are increasingly used for nighttime surveillance due to their wide applications in night vision equipment. This paper uses a deep learning-based TinyFeatureNet (TF-Net), which is an improved version of YOLOv5s, to accurately detect UAVs during the night using infrared (IR) images. In the proposed TF-Net, we introduce architectural changes in the neck and backbone of the YOLOv5s. We also simulated four different YOLOv5 models (s,m,n,l) and proposed TF-Net for a fair comparison. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s. TF-Net yielded the best results with 95.7\% precision, 84\% mAp, and 44.8\% $IoU$.
Online Unrelated-Machine Load Balancing and Generalized Flow with Recourse

We consider the recourse version of the classical online load balancing problem on unrelated machines, where the algorithm is allowed to re-assign prior jobs. We give a (2+є)-competitive algorithm for the problem with Oє(logn) amortized recourse per job. This is the first O(1)-competitive algorithm for the problem with non-trivial recourse, and the competitive ratio nearly matches the long-standing best-known offline approximation guarantee. We also show an O(loglogn/logloglogn)-competitive algorithm for the problem with O(1) amortized recourse. The best-known bounds from prior work are O(loglogn)-competitive algorithms with O(1) amortized recourse due to Gupta et al., for the special case of the restricted assignment model. Along the way, we design an algorithm for the recourse version of the online generalized network flow problem (also known as network flow problem with gains). We have a graph with costs and capacities on the edges, and sources arrive online. Upon arrival of a source, we need to send unit flow from the source. In contrast to standard network flow, every edge uv in the network has a gain parameter γuv > 0, meaning that θ-units of flow sent from u across uv becomes γuv θ units of flow when it reaches v. In the recourse version, the algorithm can undo prior flow sent on an edge by incurring a linear cost. We present an online algorithm for the problem with recourse at most O(1/є) times the offline optimum cost flow for the instance when edge capacities are scaled by a factor 1/1+є. This marks an improvement over prior work in two ways: the known algorithms only apply to standard network flow (i.e., unit gains), and secondly, the guarantees held against an offline flow when edge capacities are scaled by a factor of (2+є). As an immediate corollary of this, we also obtain an improved algorithm for the online b-matching problem with reassignment costs.
BARTSmiles: Generative Masked Language Models for Molecular Representations

We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, a BART-like model with an order of magnitude more compute than previous self-supervised molecular representations. In-depth evaluations show that BARTSmiles consistently outperforms other self-supervised representations across classification, regression, and generation tasks setting a new state-of-the-art on 11 tasks. We then quantitatively show that when applied to the molecular domain, the BART objective learns representations that implicitly encode our downstream tasks of interest. For example, by selecting seven neurons from a frozen BARTSmiles, we can obtain a model having performance within two percentage points of the full fine-tuned model on task Clintox. Lastly, we show that standard attribution interpretability methods, when applied to BARTSmiles, highlight certain substructures that chemists use to explain specific properties of molecules. The code and the pretrained model are publicly available.
Quantum Mechanics and Human Decision Making

In physics, at the beginning of the twentieth century it was recognized that some experiments could not be explained by the conventional classical mechanics but the same could be explained by the newly discovered quantum theory. It resulted into a new mechanics called quantum mechanics that revolutionized the scientific and technological developments. Again at the beginning of the twenty-first century, it is being recognized that some experiments related with the human decision making processes could not be explained by the conventional classical decision theory but the same could be explained by the models based on quantum mechanics. It is now recognized that we need quantum mechanics in psychology as well as in economics and finance. In this paper we attempt to advance and explain the present understanding of applicability of quantum mechanics to the human decision making processes. Using the postulates analogous to the postulates of quantum mechanics, we show the derivation of the quantum interference equation to illustrate the quantum approach. The explanation of disjunction effect experiments of Tversky and Shafir (1992) has been chosen to demonstrate the necessity of a quantum model. Further to suggest the possibility of application of the quantum theory to the business related decisions, some terms such as price operator, state of mind of the acquiring firm, etc. are introduced and discussed in context of the merger/acquisition of business firms. The possibility of the development in the areas such as quantum finance, quantum management, application of quantum mechanics to the human dynamics related with health care management, etc. is also indicated.
Homogenization of some periodic Hamilton-Jacobi equations with defects

Abstract We study homogenization for a class of stationary Hamilton-Jacobi equations in which the Hamiltonian is obtained by perturbing near the origin an otherwise periodic Hamiltonian. We prove that the limiting problem consists of a Hamilton-Jacobi equation outside the origin, with the same effective Hamiltonian as in periodic homogenization, supplemented at the origin with an effective Dirichlet condition that keeps track of the perturbation. Various comments and extensions are discussed.
The human digital twin brain in the resting state and in action

We simulate the human brain at the scale of up to 86 billion neurons, i.e., digital twin brain (DTB), which mimics certain aspects of its biological counterpart both in the resting state and in action. A novel routing communication layout between 10,000 GPUs to implement simulations and a hierarchical mesoscale data assimilation method to be capable to achieve more than trillions of parameters from the estimated hyperparameters are developed. The constructed DTB is able to track the its resting-state biological counterpart with a very high correlation (0.9). The DTB provides a testbed for various ‘dry’ experiments in neuroscience and medicine, and illustrated in two examples: exploring the information flow in our brain, and testing deep brain stimulation mechanisms. Finally, we enable the DTB to interact with environments by demonstrating some possible applications in vision and auditory tasks, and validate the power of DTB with achieving significant correlation with the experimental counterparts. One-Sentence Summary: We develop a human digital twin brain with novel routing communication and hierarchical mesoscale data assimilation methods.
Some congruences involving generalized Bernoulli numbers and Bernoulli polynomials

Let $[x]$ be the integral part of $x$, $n>1$ be a positive integer and $\chi_n$ denote the trivial Dirichlet character modulo $n$. In this paper, we use an identity established by Z. H. Sun to get congruences of $T_{m,k}(n)=\sum_{x=1}^{[n/m]}\frac{\chi_n(x)}{x^k}\left(\bmod n^{r+1}\right)$ for $r\in \{1,2\}$, any positive integer $m $ with $n \equiv \pm 1 \left(\bmod m \right)$ in terms of Bernoulli polynomials. As its an application, we also obtain some new congruences involving binomial coefficients modulo $n^4$ in terms of generalized Bernoulli numbers.