Miracle-dz/UniFAR-FTUs huggingface dataset:
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning

Reinforcement learning (RL) has drawn increasing interests in recent years due to its tremendous success in various applications. However, standard RL algorithms can only be applied for single reward function, and cannot adapt to an unseen reward function quickly. In this paper, we advocate a general operator view of reinforcement learning, which enables us to directly approximate the operator that maps from reward function to value function. The benefit of learning the operator is that we can incorporate any new reward function as input and attain its corresponding value function in a zero-shot manner. To approximate this special type of operator, we design a number of novel operator neural network architectures based on its theoretical properties. Our design of operator networks outperform the existing methods and the standard design of general purpose operator network, and we demonstrate the benefit of our operator deep Q-learning framework in several tasks including reward transferring for offline policy evaluation (OPE) and reward transferring for offline policy optimization in a range of tasks.
PatchTrack: Multiple Object Tracking Using Frame Patches

Object motion and object appearance are commonly used information in multiple object tracking (MOT) applications, either for associating detections across frames in tracking-by-detection methods or direct track predictions for joint-detection-and-tracking methods. However, not only are these two types of information often considered separately, but also they do not help optimize the usage of visual information from the current frame of interest directly. In this paper, we present PatchTrack, a Transformer-based joint-detection-and-tracking system that predicts tracks using patches of the current frame of interest. We use the Kalman filter to predict the locations of existing tracks in the current frame from the previous frame. Patches cropped from the predicted bounding boxes are sent to the Transformer decoder to infer new tracks. By utilizing both object motion and object appearance information encoded in patches, the proposed method pays more attention to where new tracks are more likely to occur. We show the effectiveness of PatchTrack on recent MOT benchmarks, including MOT16 (MOTA 73.71%, IDF1 65.77%) and MOT17 (MOTA 73.59%, IDF1 65.23%). The results are published on https://motchallenge.net/method/MOT=4725&chl=10.
BDG-Net: boundary distribution guided network for accurate polyp segmentation

Colorectal cancer (CRC) is one of the most common fatal cancer in the world. Polypectomy can effectively interrupt the progression of adenoma to adenocarcinoma. Colonoscopy is the primary method to find colonic polyps. However, due to the different sizes and the unclear boundary of polyps, it is challenging to segment polyps accurately. To this end, we design a Boundary Distribution Guided Network (BDG-Net) for accurate polyp segmentation. Specifically, Boundary Distribution Generate Module (BDGM) aggregates high-level features to generate Boundary Distribution Map (BDM), which is sent to the Boundary Distribution Guided Decoder (BDGD) as complementary spatial information to guide the polyp segmentation. Moreover, a multi-scale feature interaction strategy is adopted in BDGD to improve the polyp segmentation of different sizes. Extensive experiments demonstrate that BDG-Net outperforms state-of-the-art models remarkably and maintains low computational complexity.
Stability of multi-population traffic flows

Traffic waves, known also as stop-and-go waves or phantom jams, appear naturally as traffic instabilities, also in confined environments as a ring-road. A multi-population traffic is studied on a ring-road, comprised of drivers with stable and unstable behavior. There exists a critical penetration rate of stable vehicles above which the system is stable, and under which the system is unstable. In the latter case, stop-and-go waves appear, provided enough cars are on the road. The critical penetration rate is explicitly computable, and, in reasonable situations, a small minority of aggressive drivers is enough to destabilize an otherwise very stable flow. This is a source of instability that a single population model would not be able to explain. Also, the multi-population system can be stable below the critical penetration rate if the number of cars is sufficiently small. Instability emerges as the number of cars increases, even if the traffic density remains the same (i.e., number of cars and road size increase similarly). This shows that small experiments could lead to deducing imprecise stability conditions.
Effect of Kinematics and Fluency in Adversarial Synthetic Data Generation for ASL Recognition With RF Sensors

RF sensors have been recently proposed as a new modality for sign language processing technology. They are noncontact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth comparative examination of the kinematic properties of signing as measured by RF sensors for both fluent ASL users and hearing imitation signers. Second, as ASL recognition techniques utilizing deep learning requires a large amount of training data, this work examines the effect of signing kinematics and subject fluency on adversarial learning techniques for data synthesis. The following two different approaches for the synthetic training data generation are proposed: 1) adversarial domain adaptation to minimize the differences between imitation signing and fluent signing data and 2) kinematically-constrained generative adversarial networks for accurate synthesis of RF signing signatures. The results show that the kinematic discrepancies between imitation signing and fluent signing are so significant that training on data directly synthesized from fluent RF signers offers greater performance (93% top-5 accuracy) than that produced by adaptation of imitation signing (88% top-5 accuracy) when classifying 100 ASL signs.
iCaps: Iterative Category-level Object Pose and Shape Estimation

This paper proposes a category-level 6D object pose and shape estimation approach iCaps, which allows tracking 6D poses of unseen objects in a category and estimating their 3D shapes. We develop a category-level auto-encoder network using depth images as input, where feature embeddings from the auto-encoder encode poses of objects in a category. The auto-encoder can be used in a particle filter framework to estimate and track 6D poses of objects in a category. By exploiting an implicit shape representation based on signed distance functions, we build a LatentNet to estimate a latent representation of the 3D shape given the estimated pose of an object. Then the estimated pose and shape can be used to update each other in an iterative way. Our category-level 6D object pose and shape estimation pipeline only requires 2D detection and segmentation for initialization. We evaluate our approach on a publicly available dataset and demonstrate its effectiveness. In particular, our method achieves comparably high accuracy on shape estimation.
ournal of C osmology and A stroparticle hysics Deciphering signatures of Bardeen black holes from the observed quasi-periodic oscillations

Quasi-periodic oscillations (QPOs) observed in the power spectrum of black holes are unique observational probes to the background spacetime since they can be directly related to the timescales associated with the motion of matter orbiting in the vicinity of the black hole horizon. In this regard, the high frequency QPOs (HFQPOs) are particularly interesting as they occur in commensurable pairs, the most common ratio being the 3:2 twin peak QPOs. The theoretical models which aim to explain these QPOs express the observed frequencies in terms of the epicyclic motion of test particles in a given background spacetime. In this work, we study the signatures of Bardeen spacetime from the observed QPOs in the black hole power spectrum. Bardeen black holes are rotating, regular black holes with a magnetic monopole charge. Such regular backgrounds are theoretically interesting as they can potentially evade the curvature singularity, otherwise unavoidable in general relativistic black holes. We perform a χ 2 analysis by comparing the available observations of the quasiperiodic oscillations from black hole sources with the relevant theoretical models and note that the Kerr black holes in general relativity are observationally more favored compared to black holes with a monopole charge. Our analysis reveals that black holes with very high monopole charges are disfavored from QPO related observations.
Optimizing Machine Learning Inference Queries with Correlative Proxy Models

We consider accelerating machine learning (ML) inference queries on unstructured datasets. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable with classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) assume independence among the query predicates, build a proxy model for each predicate offline, and rewrite a new query by injecting these cheap proxy models in the front of the expensive ML UDFs. In such a manner, unlikely inputs that do not satisfy query predicates are filtered early to bypass the ML UDFs. We show that enforcing the independence assumption in this context may result in sub-optimal plans. In this paper, we propose CORE, a query optimizer that better exploits the predicate correlations and accelerates ML inference queries. Our solution builds the proxy models online for a new query and leverages a branch-and-bound search process to reduce the building costs. Results on three real-world text, image and video datasets show that CORE improves the query throughput by up to 63% compared to PP and up to 80% compared to running the queries as it is.
BARACK: Partially Supervised Group Robustness With Guarantees

While neural networks have shown remarkable success on classification tasks in terms of average-case performance, they often fail to perform well on certain groups of the data. Such group information may be expensive to obtain; thus, recent works in robustness and fairness have proposed ways to improve worst-group performance even when group labels are unavailable for the training data. However, these methods generally underperform methods that utilize group information at training time. In this work, we assume access to a small number of group labels alongside a larger dataset without group labels. We propose BARACK, a simple two-step framework to utilize this partial group information to improve worst-group performance: train a model to predict the missing group labels for the training data, and then use these predicted group labels in a robust optimization objective. Theoretically, we provide generalization bounds for our approach in terms of the worst-group performance, which scale with respect to both the total number of training points and the number of training points with group labels. Empirically, our method outperforms the baselines that do not use group information, even when only 1-33% of points have group labels. We provide ablation studies to support the robustness and extensibility of our framework.
Layer-Wise Multi-View Decoding for Improved Natural Language Generation

—In sequence-to-sequence learning, e.g., natural language generation, the decoder relies on the attention mechanism to efﬁciently extract information from the encoder. While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversiﬁed levels of information. Nonetheless, the decoder still obtains only a single view of the source sequences, which might lead to insufﬁcient training of the encoder layer stack due to the hierarchy bypassing problem. In this work, we propose layer-wise multi-view decoding , where for each decoder layer, together with the representations from the last encoder layer, which serve as a global view, those from other encoder layers are supplemented for a stereoscopic view of the source sequences. Systematic experiments and analyses show that we successfully address the hierarchy bypassing problem, require almost negligible parameter increase, and substantially improve the performance of sequence-to-sequence learning with deep representations on ﬁve diverse tasks, i.e., machine translation, abstractive summarization, image captioning, video captioning, and medical report generation. In particular, our approach achieves new state-of-the-art results on eight benchmark datasets, including a low-resource machine translation dataset and two low-resource medical report generation datasets.
Emergent localization phenomena in the synthetic space of zero-dimensional open quantum systems with exceptional points

Phase transitions in non-Hermitian systems are at the focus of cutting edge theoretical and experimental research. On the one hand, parity-time(PT -) and anti-PT -symmetric physics have gained ever-growing interest, due to the existence of non-Hermitian spectral singularities called exceptional points (EPs). On the other, topological and localization transitions in non-Hermitian systems reveal new phenomena, e.g., the non-Hermitian skin effect and the absence of conventional bulk-boundary correspondence. The great majority of previous studies exclusively focus on non-Hermitian Hamiltonians, whose realization requires an a priori fine-tuned extended lattices to exhibit topological and localization transition phenomena. For instance, the PT -symmetry in a lattice can be easily broken by perturbations in any of the lattice sites. As such, this system fragility poses serious challenges for the experimental observation of non-Hermitian topological and localization effects. In this work, we show how the lattice non-Hermitian localization phenomena can naturally emerge in the synthetic space of zero-dimensional open quantum systems. This offers an opportunity to simulate localization transitions in low-dimensional systems, without the need to construct complex arrays of coupled, e.g., cavities or waveguides. Namely, by taking into consideration the full quantum properties of a zero-dimensional quadratic bosonic system, we demonstrate the emergence of spectral, topological, and localization properties in the synthetic vector space, spanned by its higher-order field moments. Indeed, the field moment equations of motion can describe an equivalent (quasi-)particle moving in a one-dimensional (1D) synthetic lattice. This synthetic field moments space can exhibit a nontrivial, even anomalous, localization transition induced by the presence of highly-degenerate EPs. As an example, we study an anti-PT -symmetric bosonic dimmer, and reveal the intriguing interplay between spectral, topological and localization transition, and the emergence of non-Hermitian skin effect in the synthetic 1D space of its high-order field moments. We also generalize our findings to arbitrary low-dimensional open bosonic systems exhibiting spectral singularities (exceptional points, lines, surfaces). Our results can be directly verified in state-of-the-art optical setups, such as superconducting circuits and toroidal resonators, by measuring photon moments or correlation functions, thus allowing the observation of rich spectral and localization non-Hermitian phenomena emerging in synthetic space of zero-dimensional open quantum systems.
Educational Differences in Self-Rated Health Trends Among Middle-Aged and Older Adults Living Alone, 1972–2018

Objectives: This study examines educational differences in living alone and in self-rated health trends among middle-aged and older adults. Methods: We used logistic regression to analyze data from the 1972–2018 National Health Interview Survey (n = 795,239 aged 40–64; n = 357,974 aged 65–84). Results: Between 1972–1974 and 2015–2018, living alone became more prevalent, particularly among men and at lower levels of education. Self-rated health trends varied by living arrangement and education. We found self-rated health declines among middle-aged adults having no college degree and living alone, but trends in self-rated health were mostly stable or even improved among middle-aged adults living with others. Among older adults, self-rated health improved over time, but for the least-educated older Americans living alone, the probability of reporting fair or poor health increased between 1972–1974 and 2015–2018. Discussion: The findings suggest growing disparities by social class, in living arrangements and in self-rated health.
Anesthesia for Advanced Endoscopic Procedures

The gastrointestinal endoscopy paradigm is rapidly changing, and technological advancements are largely responsible. In tandem, anesthesia providers are adapting to the changing needs and demands. The challenges are unique. Complications arising from the procedures are both routine, such as aspiration and hypoxia, and procedure specific, such as bleeding, pneumothorax, pneumopericardium, and pneumoperitoneum. It is crucial for the anesthesia provider to have a good understanding of the techniques employed by the endoscopist. A higher index of suspicion is also essential to diagnose and appropriately manage many of the complications. In this review, an effort is made to discuss both procedural aspects and anesthesia challenges. We hope that both endoscopists and anesthesia providers will benefit from this review.
Circulating proteomic panels for risk stratification of intracranial aneurysm and its rupture

The prevalence of intracranial aneurysm (IA) is increasing, and the consequences of its rupture are severe. This study aimed to reveal specific, sensitive, and non‐invasive biomarkers for diagnosis and classification of ruptured and unruptured IA, to benefit the development of novel treatment strategies and therapeutics altering the course of the disease. We first assembled an extensive candidate biomarker bank of IA, comprising up to 717 proteins, based on altered proteins discovered in the current tissue and serum proteomic analysis, as well as from previous studies. Mass spectrometry assays for hundreds of biomarkers were efficiently designed using our proposed deep learning‐based method, termed DeepPRM. A total of 113 potential markers were further quantitated in serum cohort I (n = 212) & II (n = 32). Combined with a machine‐learning‐based pipeline, we built two sets of biomarker combinations (P6 & P8) to accurately distinguish IA from healthy controls (accuracy: 87.50%) or classify IA rupture patients (accuracy: 91.67%) upon evaluation in the external validation set (n = 32). This extensive circulating biomarker development study provides valuable knowledge about IA biomarkers.
Pediatric Osteoarticular Infections Caused by Mycobacteria Tuberculosis Complex: A 26-Year Review of Cases in San Diego, CA

Background: Osteoarticular infections (OAIs) account for 10%–20% of extrapulmonary Mycobacteria tuberculosis (MTB) complex infections in children and 1%–2% of all pediatric tuberculosis infections. Treatment regimens and durations typically mirror recommendations for other types of extrapulmonary MTB, but there are significant variations in practice, with some experts suggesting a treatment course of 12 months or longer. Methods: We conducted a retrospective review of children diagnosed with MTB complex OAI and cared for between December 31, 1992, and December 31, 2018, at a tertiary care pediatric hospital near the United States–Mexico border. Results: We identified 21 children with MTB complex OAI during the study period. Concurrent pulmonary disease (9.5%), meningitis (9.5%), and intra-abdominal involvement (14.3%) were all observed. MTB complex was identified by culture from operative samples in 15/21 children (71.4%); 8/15 (53.3%) cultures were positive for Mycobacterium bovis. Open bone biopsy was the most common procedure for procurement of a tissue sample and had the highest culture yield. The median duration of antimicrobial therapy was 52 weeks (interquartile range, 46–58). Successful completion of therapy was documented in 15 children (71.4%). Nine children (42.9%) experienced long-term sequelae related to their infection. Conclusion: Among the 21 children with MTB complex OAI assessed, 8 of 15 (53.3%) children with a positive tissue culture had M. bovis, representing a higher percentage than in previous reports and potentially reflecting its presence in unpasteurized dairy products in the California-Baja region. Bone biopsy produced the highest culture yield in this study. Given the rarity of this disease, multicenter collaborative studies are needed to improve our understanding of the presentation and management of pediatric MTB complex OAI.
Development of a sensitive chicken IgY-based enzyme-linked immunosorbent assay for detection of mebendazole in pork and mutton

Abstract Chicken egg yolk IgY has proven to be qualified for analysis of targets in immunoassays. In order to explore the feasibility of chicken IgY-based ELISA for detection of mebendazole (MEB), the chicken IgY against MEB was generated in the laying hens. An enzyme-linked immunosorbent assay (ELISA) based on chicken IgY was developed for detection of MEB with a half-maximum signal inhibition concentration (IC50) of 3.65 ng mL−1 and a limit of detection of 0.25 ng mL−1. The assay showed a lower cross reactivity (less than 1%) with other structures analogues (except amino-MEB with the values of 70.7%). The average recoveries of MEB spiked in pork and mutton muscle samples ranged from 93.6% to 106.3% with relative standard deviation less than 8.78% and 10.85% for intra-assay and inter-assay, respectively, and agreed well with those of high-performance liquid chromatography. Our results indicate that generated IgY could be used as a robust reagent for routine screening analysis of small molecular compounds residues in food samples.
Seroprevalence of herpes simplex virus types 1 and 2 and correlates of infection in Jordan

Background Herpes infections are common infections among populations. Herein, a cross-sectional study was used to determine the seroprevalence of herpes simplex virus type 1 (HSV-1) and herpes simplex virus type 2 (HSV-2) IgG antibodies and their association with potential infection risk factors among Jordanians. Methods A total of 759 serum samples were collected (January to February 2020) and analyzed for HSV-1 and HSV-2 IgG antibodies by enzyme-linked immunosorbent assay. Estimates for population seropositivity were determined by weighting the age-specific seroprevalence by the size of the population in each age stratum. Results The population estimate for HSV-1 seroprevalence was 75.3%. After adjustment for possible confounders, regression analysis revealed higher seroprevalence with increase in age (p < 0.005) and low household income (p = 0.002). The population estimate for HSV-2 seroprevalence was 2.9%. No significant differences in HSV-2 seroprevalence were observed in association with age, gender, family size, educational level, and socioeconomic status, likely due to low seropositivity. Conclusions Jordanians have high HSV-1 and low HSV-2 seroprevalence. Periodical studies might be needed to evaluate changes in HSV-1 and HSV-2 seroprevalence over time. This study provides essential epidemiological data for Jordan and the Middle East and North Africa region.