Miracle-dz/UniFAR-FTUs huggingface dataset:
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences

Digital subtraction angiography (DSA) is universally acknowledged as the gold standard for examining lesion angioarchitecture, elucidating arterial blood supply dynamics, and guiding endovascular interventions. The automatic segmentation of intracranial arteries (IA) in DSA, which is pivotal for quantifying vascular morphology, plays an essential role in computer-assisted stroke research and clinical practices. Nevertheless, research in this specific domain remains constrained, primarily owing to the unavailability of publicly datasets for IA segmentation within the research community. Currently, the predominant focus of methodologies lies in the segmentation of single-frame DSA using in-house datasets. These methods, limited by the partial inclusion of contrast in single-frame DSA, encounters challenges in rendering a precise representation of vascular structures. In this paper, we introduces DIAS, a dataset specifically developed for IA segmentation in DSA sequences. A comprehensive benchmark has been established for evaluating DIAS, covering fully, weakly, and semi-supervised segmentation methods. Specifically, we propose a vessel sequence segmentation network that captures the spatiotemporal representation of intravascular contrast for segmenting vessels in DSA sequences. For weakly-supervised learning, we propose a novel scribble learning-based image segmentation framework, incorporating both scribble supervision and consistency regularization. Furthermore, we introduce a random patch-based self-training framework that harnesses unlabeled DSA sequences to improve segmentation performance. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications.
Focusing on Relevant Responses for Multi-modal Rumor Detection

In the absence of an authoritative statement about a rumor, people may expose the truth behind such rumor through their responses on social media. Most rumor detection methods aggregate the information of all the responses and have made great progress. However, due to the different backgrounds of users, the responses have different relevance for discovering th suspicious points hidden in a rumor claim. The methods that focus on all the responding tweets would dilute the effect of the critical ones. Moreover, for a multi-modal rumor claim, the focus of a user may be on several words in the text or an object in the image, so the different modalities should be considered to select the relevant responses and verify the claim. In this paper, we propose a novel multi-modal rumor detection model, termed Focal Reasoning Model (FoRM), to filter out the irrelevant responses and further conduct fine-grained reasoning with the multi-modal claim and corresponding responses. Concretely, there are two main components in our FoRM: the coarse-grained selection and the fine-grained reasoning. The coarse-grained selection component leverages the post-level features of the responses to verify the claim and learns a relevant score of each response. Based on the relevant scores, the most relevant responses are reserved as the critical ones to the further reasoning. In the fine-grained reasoning component, we design a relation attention module to explore the fine-grained relations, i.e., token-to-token and token-to-object relations, between the reserved responses and the multi-modal claim for finding out the valuable clues. Extensive experiments have been conducted on two real-world datasets, and the results demonstrate that our proposed model outperforms all the baselines.
Attention Hybrid Variational Net for Accelerated MRI Reconstruction

The application of compressed sensing (CS)-enabled data reconstruction for accelerating magnetic resonance imaging (MRI) remains a challenging problem. This is due to the fact that the information lost in k-space from the acceleration mask makes it difficult to reconstruct an image similar to the quality of a fully sampled image. Multiple deep learning-based structures have been proposed for MRI reconstruction using CS, in both the k-space and image domains, and using unrolled optimization methods. However, the drawback of these structures is that they are not fully utilizing the information from both domains (k-space and image). Herein, we propose a deep learning-based attention hybrid variational network that performs learning in both the k-space and image domains. We evaluate our method on a well-known open-source MRI dataset (652 brain cases and 1172 knee cases) and a clinical MRI dataset of 243 patients diagnosed with strokes from our institution to demonstrate the performance of our network. Our model achieves an overall peak signal-to-noise ratio/structural similarity of 40.92 ± 0.29/0.9577 ± 0.0025 (fourfold) and 37.03 ± 0.25/0.9365 ± 0.0029 (eightfold) for the brain dataset, 31.09 ± 0.25/0.6901 ± 0.0094 (fourfold) and 29.49 ± 0.22/0.6197 ± 0.0106 (eightfold) for the knee dataset, and 36.32 ± 0.16/0.9199 ± 0.0029 (20-fold) and 33.70 ± 0.15/0.8882 ± 0.0035 (30-fold) for the stroke dataset. In addition to quantitative evaluation, we undertook a blinded comparison of image quality across networks performed by a subspecialty trained radiologist. Overall, we demonstrate that our network achieves a superior performance among others under multiple reconstruction tasks.
Averaging symmetric positive-definite matrices on the space of eigen-decompositions

We study extensions of Fr\'{e}chet means for random objects in the space ${\rm Sym}^+(p)$ of $p \times p$ symmetric positive-definite matrices using the scaling-rotation geometric framework introduced by Jung et al. [\textit{SIAM J. Matrix. Anal. Appl. } \textbf{36} (2015) 1180-1201]. The scaling-rotation framework is designed to enjoy a clearer interpretation of the changes in random ellipsoids in terms of scaling and rotation. In this work, we formally define the \emph{scaling-rotation (SR) mean set} to be the set of Fr\'{e}chet means in ${\rm Sym}^+(p)$ with respect to the scaling-rotation distance. Since computing such means requires a difficult optimization, we also define the \emph{partial scaling-rotation (PSR) mean set} lying on the space of eigen-decompositions as a proxy for the SR mean set. The PSR mean set is easier to compute and its projection to ${\rm Sym}^+(p)$ often coincides with SR mean set. Minimal conditions are required to ensure that the mean sets are non-empty. Because eigen-decompositions are never unique, neither are PSR means, but we give sufficient conditions for the sample PSR mean to be unique up to the action of a certain finite group. We also establish strong consistency of the sample PSR means as estimators of the population PSR mean set, and a central limit theorem. In an application to multivariate tensor-based morphometry, we demonstrate that a two-group test using the proposed PSR means can have greater power than the two-group test using the usual affine-invariant geometric framework for symmetric positive-definite matrices.
Probing the hot circumgalactic medium of external galaxies in X-ray absorption II: a luminous spiral galaxy at z ≈ 0.225

The circumgalactic medium (CGM) is the most massive baryonic component of a spiral galaxy, shock heated to about 106 K for an $\rm L^{\star }$ galaxy. The CGM of the Milky Way has been well-characterized through X-ray absorption line spectroscopy. However, the paucity of bright background sources makes it challenging to probe the CGM of external galaxies. Previously, using broad O vi absorption as a signpost, we successfully detected the CGM of one galaxy in X-rays. Here we report on the detection of the O vii K α absorption line at the redshift of a spiral galaxy at z ≈ 0.225, using 1.2 Ms of Chandra observations. This is a robust detection, clearly showing the presence of the hot gas. The mass in the hot phase is at least an order of magnitude larger than that in the cooler phases detected in the UV. The presence of hot gas 116 h−1 kpc from the centre of this galaxy provides credence to the existence of the extended CGM of the Milky Way. There has been a report of the detection of O vii absorption from the warm-hot intergalactic medium in this sightline, using stacking analysis on older data set. We argue that the absorption line is from the CGM of the z ≈ 0.225 galaxy instead.
An Overview of Catastrophic AI Risks

Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose catastrophic risks. Although numerous risks have been detailed separately, there is a pressing need for a systematic discussion and illustration of the potential dangers to better inform efforts to mitigate them. This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories: malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs; organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents; and rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans. For each category of risk, we describe specific hazards, present illustrative stories, envision ideal scenarios, and propose practical suggestions for mitigating these dangers. Our goal is to foster a comprehensive understanding of these risks and inspire collective and proactive efforts to ensure that AIs are developed and deployed in a safe manner. Ultimately, we hope this will allow us to realize the benefits of this powerful technology while minimizing the potential for catastrophic outcomes.
One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments

Robot-assisted dressing could benefit the lives of many people such as older adults and individuals with disabilities. Despite such potential, robot-assisted dressing remains a challenging task for robotics as it involves complex manipulation of deformable cloth in 3D space. Many prior works aim to solve the robot-assisted dressing task, but they make certain assumptions such as a fixed garment and a fixed arm pose that limit their ability to generalize. In this work, we develop a robot-assisted dressing system that is able to dress different garments on people with diverse poses from partial point cloud observations, based on a learned policy. We show that with proper design of the policy architecture and Q function, reinforcement learning (RL) can be used to learn effective policies with partial point cloud observations that work well for dressing diverse garments. We further leverage policy distillation to combine multiple policies trained on different ranges of human arm poses into a single policy that works over a wide range of different arm poses. We conduct comprehensive real-world evaluations of our system with 510 dressing trials in a human study with 17 participants with different arm poses and dressed garments. Our system is able to dress 86% of the length of the participants' arms on average. Videos can be found on our project webpage: https://sites.google. com/view/one-policy-dress.
Functional singular value decomposition and multi-resolution anomaly detection

LINGSONG ZHANG: Functional Singular Value Decomposition and Multi-Resolution Anomaly Detection. (Under the direction of J. S. Marron, Zhengyuan Zhu and Haipeng Shen.) This dissertation has two major parts. The first part discusses the connections and differences between the statistical tool of Principal Component Analysis (PCA) and the related numerical method of Singular Value Decomposition (SVD), and related visualization methods. The second part proposes a Multi-Resolution Anomaly Detection (MRAD) method for time series with long range dependence (LRD). PCA is a popular method in multivariate analysis and in Functional Data Analysis (FDA). Compared to PCA, SVD is more general, because it not only provides a direct approach to calculate the principal components (PCs), but also simultaneously yields the PCAs for both the row and the column spaces. SVD has been used directly to explore and analyze data sets, and has been shown to be an insightful analysis tool in many fields. However, the connection and differences between PCA and SVD have seldom been explored from a statistical view point. Here we explore the connections and differences between PCA and SVD, and extend the usual SVD method to variations including different centerings based on various types of means. A generalized scree plot is developed to provide a visual aid for selection of different centerings. Several matrix views of the SVD components are introduced to explore different features in data, including SVD surface plots, image plots, rotation movies, and curve movies. These methods visualize both column and row information of a two-way matrix simultaneously, relate the matrix to relevant curves, and show local variations and interactions between columns and rows. Several toy examples are designed
Learning to Generate Better Than Your LLM

Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT and GPT-4 can engage in fluent conversations with users after finetuning with RL. Capitalizing on key properties of text generation, we seek to investigate RL algorithms beyond general purpose algorithms like Proximal Policy Optimization (PPO). In particular, we extend RL algorithms to allow them to interact with a dynamic black-box guide LLM and propose RL with guided feedback (RLGF), a suite of RL algorithms for LLM fine-tuning. We provide two ways for the guide LLM to interact with the LLM to be optimized for maximizing rewards. The guide LLM can generate text which serves as additional starting states for the RL optimization procedure. The guide LLM can also be used to complete the partial sentences generated by the LLM that is being optimized, treating the guide LLM as an expert to imitate and surpass eventually. We experiment on the IMDB positive sentiment, CommonGen, and TL;DR summarization tasks. We show that our RL algorithms achieve higher performance than supervised learning (SL) and the RL baseline PPO, demonstrating the benefit of interaction with the guide LLM. On both CommonGen and TL;DR, we not only outperform our SL baselines but also improve upon PPO across a variety of metrics beyond the one we optimized for. Our code can be found at https://github.com/Cornell-RL/tril.
Laser noise residuals in LISA from on-board processing and time-delay interferometry

Time-delay interferometry (TDI) is a crucial step in the on-ground data processing pipeline of the Laser Interferometer Space Antenna (LISA), as it reduces otherwise overwhelming laser noise and allows for the detection of gravitational waves. This being said, several laser noise couplings have been identified that limit the performance of TDI. First, on-board processing, which is used to decimate the sampling rate from tens of MHz down to a few Hz, requires careful design of the anti-aliasing filters to mitigate folding of laser noise power into the observation band. Furthermore, the flatness of those filters is important to limit the effect of the flexing-filtering coupling. Secondly, the post-processing delays applied in TDI are subject to ranging and interpolation errors. All of these effects are partially described in the literature. In this paper, we present them in a unified framework and give a more complete description of aliased laser noise and the coupling of interpolation errors. Furthermore, for the first time, we discuss the impact of laser locking on laser noise residuals in the final TDI output. To verify the validity of the analytic PSD models we derive, we run numerical simulations using LISA Instrument and calculate second-generation TDI variables with PyTDI. We consider a setup with six independent lasers and with locked lasers (locking configuration N1-12). We find that laser locking indeed affects the laser noise residual in the TDI combination as it introduces correlations among the six lasers inducing slight modulations of the PSD compared to the case of six independent lasers. This implies further studies on laser noise residuals should consider the various locking configurations to produce accurate results.
Evaluation of multifactorial digestive disorders in a dairy herd at different stages of lactation

Abstract The manager of a large dairy herd (total confinement, total mixed ration feeding, 10130 kg milk sold per cow and year) requested a workup of a digestive disorder problem that had been ongoing for several years. The cows were at all stages of lactation, and the incidence proportion (events/100 cows at risk) of digestive disorders was approximately 5 to 10%. The clinical picture included an abnormal demeanor of varying severity and signs that differed among the cows. The latter included decreased milk production and physical activity, low body condition score, abnormal rumen motility and stratification, small intestinal dilatation, diarrhea, undigested fiber particles in the feces, fever and abdominal pain. The following approach was used to investigate the digestive disorder problem: 1. The herd was assessed for production levels, housing requirements, feeding protocols and animal health management. The latter comprised evaluation of different animal scores, metabolic profile analysis, diagnosis of disease, culling records and slaughter data. The results revealed risk factors concerning the feeding and animal health monitoring, (e. g. in dry matter intake and silage quality management, disease detection and diagnosis in fresh cows). The assessment also identified a high occurrence of digestive disorders of unknown origin. 2. Fifteen cows that represented the ongoing digestive disorder problem were selected to undergo clinical examination, hematological analysis, urinalysis, and ultrasonography of the ventral abdomen. The clinical examinations revealed different digestive disorders, which were mainly inflammatory in nature, in all the cows. Eight cows had localized reticuloperitonitis and 13 had left displaced abomasum with different degrees of displacement and adhesions between the abomasum and reticulum attributable to reticuloperitonitis. Our results revealed a multifactorial problem caused by several risk factors relating to animal health and feeding management protocols that resulted in different types of digestive disorders. The wires from damaged tires used to hold the pit silo tarps in place were identified as a possible traumatic cause of the reticuloperitonitis. Treatment, prevention and follow-up of the different conditions were discussed.
Social Media Data for Firearms Research: Promise and Perils

Firearms research has been hindered by a persistent lack of high-quality, reliable, and timely data. Using social media data in firearms research is, therefore, appealing: these data are large in scale, continuously provided, passively obtained, inexpensive in some cases, and easily combined with data from traditional sources. We consider how social media data have been used in firearms research to date and how they can be best used moving forward. Despite the many advantages of using social media data, there are key analytical and ethical considerations associated with their use, including understanding the underlying population, constructing valid and reliable measures from shared content, the possibility of false data and misinformation through fake accounts, and concerns having to do with the privacy and consent of social media users. Notwithstanding these drawbacks, we show that there is a compelling case for the continued development of these data for firearms research and suggest principles for their responsible use in this context.
Pharmacokinetics and Safety of Sulfamethoxazole-Trimethoprim After Oral Administration of Single and Multiple Doses to Rhode Island Red Chickens (Gallus gallus domesticus)

Abstract: Sulfamethoxazole-trimethoprim (SMZ-TMP), a commonly prescribed antibiotic for backyard hens, is neither Food and Drug Administration approved nor prohibited in laying hens in the United States. The aim of this study was to determine whether plasma concentrations above targeted minimum inhibitory concentration breakpoint values for Enterobacteriaceae could be achieved with oral dosing. Five Rhode Island red hens (Gallus gallus domesticus) were administered a single dose of 96 mg/kg SMZ-TMP (80 mg/kg SMZ and 16 mg/kg TMP) IV followed by the same dose orally after a washout period. Following oral dosing, mean SMZ concentrations exceeded the target breakpoint for approximately 12 hours; however, TMP only briefly exceeded the target breakpoint. Bioavailability was 60.5% for SMZ and 82.0% for TMP. Ten naïve birds were allocated into control (n = 4) and treatment (n = 6) groups for a 7-day multi-dose study. Treatment birds received an oral suspension dosed at 16 mg/kg TMP and 80 mg/kg SMZ every 48 hours (on days 1, 3, 5, and 7); TMP tablets were additionally dosed at 25 mg/bird on days 1, 3, 5, and 7, and 50 mg/bird on days 2, 4, and 6. Plasma SMZ-TMP concentrations were measured on a multiple time interval by ultraperformance liquid chromatography–mass spectrometry, and pharmacokinetic analyses were performed using a noncompartmental model. No accumulation for either drug was noted following repeated dosing, and no statistical differences in biochemical values, packed cell volumes, or weight were found between pre- and posttreatment in either the treatment or control groups. Sulfamethoxazole (80 mg/kg q48h PO) and TMP (24.1–28.0 mg/kg q24h PO) maintained therapeutic plasma concentrations at or exceeding the minimum inhibitory concentration breakpoint of Enterobacteriaceae for 72 and 24 hours for TMP and SMZ, respectively, without evidence of adverse effects or drug accumulation. Further studies are needed to refine this dosage regimen and evaluate adverse effects in ill birds.
Microfiber evanescent-field photothermal gas detection using acoustic-induced mode-dependent frequency shift

Abstract In this study, we experimentally showcase the microfiber evanescent-field photothermal gas detection by exploiting all-fiber MHz-level frequency shift scheme. Based on the acousto-optic interaction effect, the low-frequency shifts of 0.9 MHz and 1.83 MHz can be obtained through the cyclic conversion between the transverse core modes LP01 and LP11 in the few-mode fiber. Our proposed all-fiber frequency shifters show flexible MHz-level up(down) frequency shifts with superior sideband rejection ratio (over 40 dB) and low insertion loss (less than 1 dB). Furthermore, an all-fiber heterodyne interferometric detection system is implemented by leveraging the above low-frequency shifters, in which around 1-μm-diameter microfiber is investigated for photothermal gas detection. A pump-probe configuration is employed to obtain the photothermal effect induced by the gas absorption of the modulated evanescent field. By demodulating the phase of the beat signal output by the interferometer, an equivalent detection limit (1σ) of 32 ppm and a response time of 22 s are achieved for ammonia, as well as 0.24 % instability within 48 pump cycles. Given its compact all-fiber configuration and high sensitivity with fast response, the experimental results can pave the way for widespread applications like heterodyne detection, fiber optical sensors, and interplanetary coherent communications.