Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Pattern of Cortical Hypoxia in Multiple Sclerosis and Its Significance on Neuropsychological and Clinical Measures of Disability

Multiple sclerosis (MS) is a degenerative disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal damage. It has been hypothesized that hypoxia plays a role in the pathogenesis of MS. This study was undertaken to investigate the reproducibility of non‐invasively measured cortical microvascular hemoglobin oxygenation (StO2) using frequency domain near‐infrared spectroscopy (fdNIRS), investigate its temporal pattern of hypoxia in people with MS (pwMS), and its relationship with neurocognitive function and mood.
Comprehensive profiling of DNA methylation in Korean patients with colorectal cancer

Alterations in DNA methylation play an important pathophysiological role in the development and progression of colorectal cancer. We comprehensively profiled DNA methylation alterations in 165 Korean patients with colorectal cancer (CRC), and conducted an in-depth investigation of cancer-specific methylation patterns. Our analysis of the tumor samples revealed a significant presence of hypomethylated probes, primarily within the gene body regions; few hypermethylated sites were observed, which were mostly enriched in promoter-like and CpG island regions. The CpG Island Methylator Phenotype-High (CIMP-H) exhibited notable enrichment of microsatellite instability-high (MSI-H). Additionally, our findings indicated a significant correlation between methylation of the MLH1 gene and MSI-H status. Furthermore, we found that the CIMP-H had a higher tendency to affect the right-side of the colon tissues and was slightly more prevalent among older patients. Through our methylome profile analysis, we successfully verified the methylation patterns and clinical characteristics of Korean patients with CRC. This valuable dataset lays a strong foundation for exploring novel molecular insights and potential therapeutic targets for the treatment of CRC.
Protective effects of zingerone against sodium arsenite-induced lung toxicity: A multi-biomarker approach

Objective(s): Sodium arsenite (SA) exposure is toxic to the body. Zingerone (ZNG) is a flavonoid with many biological properties found naturally in honey and plants. This study aimed to determine the effects of ZNG on SA-induced rat lung toxicity. Materials and Methods: Thirty-five male Sprague rats were divided into Control, SA, ZNG, SA+ZNG25, and SA+ZNG50 groups (n=7). SA 10 mg/kg and ZNG were administered at two doses (25 and 50 mg/kg) (orally, 14 days). Analysis of oxidative stress, inflammation damage, apoptosis damage, and autophagic damage markers in lung tissue were determined by biochemical and histological methods. Results: The administration of ZNG reduced oxidative stress by increasing SA-induced decreased antioxidant enzyme activities, increasing Nrf-2, HO-1, and NQO1, and decreasing MDA level. ZNG administration reduced inflammation marker levels. Anti-apoptotic Bcl-2 increased and apoptotic Bax and Caspase-3 decreased with ZNG. ZNG promoted the regression of autophagy by reducing Beclin-1, LC3A, and LC3B levels. Conclusion: Evaluating all data showed that SA caused toxic damage to lung tissue by increasing inflammation, apoptosis, autophagy, and oxidant levels, whereas ZNG had a protective effect by reducing this damage.
DALNet: A Rail Detection Network Based on Dynamic Anchor Line

Rail detection is one of the key factors for intelligent train. In the paper, motivated by the anchor line-based lane detection methods, we propose a rail detection network called DALNet based on dynamic anchor line. Aiming to solve the problem that the predefined anchor line is image agnostic, we design a novel dynamic anchor line mechanism. It utilizes a dynamic anchor line generator to dynamically generate an appropriate anchor line for each rail instance based on the position and shape of the rails in the input image. These dynamically generated anchor lines can be considered as better position references to accurately localize the rails than the predefined anchor lines. In addition, we present a challenging urban rail detection dataset DL-Rail with high-quality annotations and scenario diversity. DL-Rail contains 7000 pairs of images and annotations along with scene tags, and it is expected to encourage the development of rail detection. We extensively compare DALNet with many competitive lane methods. The results show that our DALNet achieves state-of-the-art performance on our DL-Rail rail detection dataset and the popular Tusimple and LLAMAS lane detection benchmarks. The code will be released at https://github.com/Yzichen/mmLaneDet.
Refugee status determination: how cooperation with machine learning tools can lead to more justice

Previous research on refugee status adjudications has shown that prediction of the outcome of an application can be derived from very few features with satisfactory accuracy. Recent research work has achieved between 70 and 90% accuracy using text analytics on various legal fields among which refugee status determination. Some studies report predictions derived from the judge identity only. Additionally most features used for prediction are non-substantive and external features ranging from news reports, date and time of the hearing or weather. On the other hand, literature shows that noise is ubiquitous in human judgments and significantly affects the outcome of decisions. It has been demonstrated that noise is a significant factor impacting legal decisions. We use the term"noise"in the sense described by D. Kahneman, as a measure of how human beings are unavoidably influenced by external factors when making a decision. In the context of refugee status determination, it means for instance that two judges would take different decisions when presented with the same application. This article explores ways that machine learning can help reduce noise in refugee law decision making. We are not suggesting that this proposed methodology should be exclusive from other approaches to improve decisions such as training of decision makers, skills acquisition or judgment aggregation, but rather that it is a path worth exploring. We investigate how artificial intelligence and specifically data-driven applications can be used to benefit all parties involved in refugee status adjudications. We specifically look at decisions taken in Canada and in the United States. Our research aims at reducing arbitrariness and unfairness that derive from noisy decisions, based on the assumption that if two cases or applications are alike they should be treated in the same way and induce the same outcome.
Hey That's Mine Imperceptible Watermarks are Preserved in Diffusion Generated Outputs

Generative models have seen an explosion in popularity with the release of huge generative Diffusion models like Midjourney and Stable Diffusion to the public. Because of this new ease of access, questions surrounding the automated collection of data and issues regarding content ownership have started to build. In this paper we present new work which aims to provide ways of protecting content when shared to the public. We show that a generative Diffusion model trained on data that has been imperceptibly watermarked will generate new images with these watermarks present. We further show that if a given watermark is correlated with a certain feature of the training data, the generated images will also have this correlation. Using statistical tests we show that we are able to determine whether a model has been trained on marked data, and what data was marked. As a result our system offers a solution to protect intellectual property when sharing content online.
Test-Time Embedding Normalization for Popularity Bias Mitigation

Popularity bias is a widespread problem in the field of recommender systems, where popular items tend to dominate recommendation results. In this work, we propose 'Test Time Embedding Normalization' as a simple yet effective strategy for mitigating popularity bias, which surpasses the performance of the previous mitigation approaches by a significant margin. Our approach utilizes the normalized item embedding during the inference stage to control the influence of embedding magnitude, which is highly correlated with item popularity. Through extensive experiments, we show that our method combined with the sampled softmax loss effectively reduces popularity bias compare to previous approaches for bias mitigation. We further investigate the relationship between user and item embeddings and find that the angular similarity between embeddings distinguishes preferable and non-preferable items regardless of their popularity. The analysis explains the mechanism behind the success of our approach in eliminating the impact of popularity bias. Our code is available at https://github.com/ml-postech/TTEN.
L2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations

First-stage retrieval is a critical task that aims to retrieve relevant document candidates from a large-scale collection. While existing retrieval models have achieved impressive performance, they are mostly studied on static data sets, ignoring that in the real-world, the data on the Web is continuously growing with potential distribution drift. Consequently, retrievers trained on static old data may not suit new-coming data well and inevitably produce sub-optimal results. In this work, we study lifelong learning for first-stage retrieval, especially focusing on the setting where the emerging documents are unlabeled since relevance annotation is expensive and may not keep up with data emergence. Under this setting, we aim to develop model updating with two goals: (1) to effectively adapt to the evolving distribution with the unlabeled new-coming data, and (2) to avoid re-inferring all embeddings of old documents to efficiently update the index each time the model is updated. We first formalize the task and then propose a novel Lifelong Learning method for the first-stage Retrieval, namely L2R. L2R adopts the typical memory mechanism for lifelong learning, and incorporates two crucial components: (1) selecting diverse support negatives for model training and memory updating for effective model adaptation, and (2) a ranking alignment objective to ensure the backward-compatibility of representations to save the cost of index rebuilding without hurting the model performance. For evaluation, we construct two new benchmarks from LoTTE and Multi-CPR datasets to simulate the document distribution drift in realistic retrieval scenarios. Extensive experiments show that L^2R significantly outperforms competitive lifelong learning baselines.
Effect of Electron-Phonon Scattering on the Anomalous Hall Conductivity of Fe$_3$Sn: A Kagome Ferromagnetic Metal

We report on magnetic and magnetotransport studies of a Kagome ferromagnetic metal, Fe$_3$Sn. Our studies reveal a large anomalous Hall conductivity ($\sigma_{zx}$) in this system, mainly contributed by temperature independent intrinsic Hall conductivity ($\sigma^{int}_{zx}$=485$\pm$60 S/cm) and temperature dependent extrinsic Hall conductivity ($\sigma^{ext}_{zx}$) due to skew-scattering. Although $\sigma^{ext}_{zx}$ value is large and almost equivalent to the intrinsic Hall conductivity at low temperatures, it drastically decreases with increasing temperature, following the relation $\sigma^{ext}_{zx}=\frac{\sigma_{zx0}^{ext}}{(aT+1)^2}$, under the influence of electron-phonon scattering. The presence of electron-phonon scattering in this system is also confirmed by the linear dependence of longitudinal electrical resistivity at higher temperatures [$\rho(T)\propto T$]. We further find that Fe$_3$Sn is a soft ferromagnet with an easy-axis of magnetization lying in the $\it{ab}$ plane of the crystal with magnetocrystalline anisotropy energy density as large as 1.02 $\times$ 10$^6$
UNCOVER: A NIRSpec Identification of a Broad-line AGN at z = 8.50

Deep observations with the James Webb Space Telescope (JWST) have revealed an emerging population of red pointlike sources that could provide a link between the postulated supermassive black hole seeds and observed quasars. In this work, we present a JWST/NIRSpec spectrum from the JWST Cycle 1 UNCOVER Treasury survey of a massive accreting black hole at z = 8.50 displaying a clear broad-line component as inferred from the Hβ line with FWHM = 3439 ± 413 km s−1, typical of the broad-line region of an active galactic nucleus (AGN). The AGN nature of this object is further supported by high ionization, as inferred from emission lines, and a point-source morphology. We compute a black hole mass of log10(MBH/M⊙)=8.17±0.42 and a bolometric luminosity of L bol ∼ 6.6 × 1045 erg s−1. These values imply that our object is accreting at ∼40% of the Eddington limit. Detailed modeling of the spectral energy distribution in the optical and near-infrared, together with constraints from ALMA, indicate an upper limit on the stellar mass of log10(M*/M⊙)<8.7 , which would lead to an unprecedented ratio of black hole to host mass of at least ∼30%. This is orders of magnitude higher compared to the local QSOs but consistent with recent AGN studies at high redshift with JWST. This finding suggests that a nonnegligible fraction of supermassive black holes either started out from massive seeds and/or grew at a super-Eddington rate at high redshift. Given the predicted number densities of high-z faint AGN, future NIRSpec observations of larger samples will allow us to further investigate galaxy–black hole coevolution in the early Universe.
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation

Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However, this quality comes at significant computational cost: nearly all of these models are iterative and require running sampling multiple times with large models. This iterative process is needed to ensure that different regions of the image are not only aligned with the text prompt, but also compatible with each other. In this work, we propose a light-weight approach to achieving this compatibility between different regions of an image, using a Markov Random Field (MRF) model. We demonstrate the effectiveness of this method on top of the latent token-based Muse text-to-image model. The MRF richly encodes the compatibility among image tokens at different spatial locations to improve quality and significantly reduce the required number of Muse sampling steps. Inference with the MRF is significantly cheaper, and its parameters can be quickly learned through back-propagation by modeling MRF inference as a differentiable neural-network layer. Our full model, MarkovGen, uses this proposed MRF model to both speed up Muse by 1.5X and produce higher quality images by decreasing undesirable image artifacts.
Learning Representations on Logs for AIOps

AI for IT Operations (AIOps) is a powerful platform that Site Reliability Engineers (SREs) use to automate and streamline operational workflows with minimal human intervention. Automated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults. Tasks such as log format detection, log classification, and log parsing are key components of automated log analysis. Most of these tasks require supervised learning; however, there are multiple challenges due to limited labeled log data and the diverse nature of log data. Large Language Models (LLMs) such as BERT and GPT3 are trained using self-supervision on a vast amount of unlabeled data. These models provide generalized representations that can be effectively used for various downstream tasks with limited labeled data. Motivated by the success of LLMs in specific domains like science and biology, this paper introduces a LLM for log data which is trained on public and proprietary log data. Results of our experiments demonstrate that the proposed LLM outperforms existing models on multiple downstream tasks. In summary, AIOps powered by LLMs offers an efficient and effective solution for automating log analysis tasks and enabling SREs to focus on higher-level tasks. Our proposed LLM, trained on public and proprietary log data, offers superior performance on multiple downstream tasks, making it a valuable addition to the AIOps platform.
Constructive Equivariant Observer Design for Inertial Navigation

Inertial Navigation Systems (INS) are algorithms that fuse inertial measurements of angular velocity and specific acceleration with supplementary sensors including GNSS and magnetometers to estimate the position, velocity and attitude, or extended pose, of a vehicle. The industry-standard extended Kalman filter (EKF) does not come with strong stability or robustness guarantees and can be subject to catastrophic failure. This paper exploits a Lie group symmetry of the INS dynamics to propose the first nonlinear observer for INS with error dynamics that are almost-globally asymptotically and locally exponentially stable, independently of the chosen gains. The observer is aided only by a GNSS measurement of position. As expected, the convergence guarantee depends on persistence of excitation of the vehicle's specific acceleration in the inertial frame. Simulation results demonstrate the observer's performance and its ability to converge from extreme errors in the initial state estimates.
PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation

Existing kinematic skeleton-based 3D human pose estimation methods only predict joint positions. Although this is sufficient to compute the yaw and pitch of the bone rotations, the roll around the axis of the bones remains unresolved by these methods. In this paper, we propose a novel 2D-to-3D lifting Graph Convolution Network named PoseGraphNet++ to predict the complete human pose including the joint positions and the bone orientations. We employ node and edge convolutions to utilize the joint and bone features. Our model is evaluated on multiple benchmark datasets, and its performance is either on par with or better than the state-of-the-art in terms of both position and rotation metrics. Through extensive ablation studies, we show that PoseGraphNet++ benefits from exploiting the mutual relationship between the joints and the bones.
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models

The introduction of the transformer architecture and the self-attention mechanism has led to an explosive production of language models trained on specific downstream tasks and data domains. With over 200, 000 models in the Hugging Face ecosystem, users grapple with selecting and optimizing models to suit multifaceted workflows and data domains while addressing computational, security, and recency concerns. There is an urgent need for machine learning frameworks that can eliminate the burden of model selection and customization and unleash the incredible power of the vast emerging model library for end users. Here, we propose a context-aware routing system, Tryage, that leverages a language model router for optimal selection of expert models from a model library based on analysis of individual input prompts. Inspired by the thalamic router in the brain, Tryage employs a perceptive router to predict down-stream model performance on prompts and, then, makes a routing decision using an objective function that integrates performance predictions with user goals and constraints that are incorporated through flags (e.g., model size, model recency). Tryage allows users to explore a Pareto front and automatically trade-off between task accuracy and secondary goals including minimization of model size, recency, security, verbosity, and readability. Across heterogeneous data sets that include code, text, clinical data, and patents, the Tryage framework surpasses Gorilla and GPT3.5 turbo in dynamic model selection identifying the optimal model with an accuracy of 50.9% , compared to 23.6% by GPT 3.5 Turbo and 10.8% by Gorilla. Conceptually, Tryage demonstrates how routing models can be applied to program and control the behavior of multi-model LLM systems to maximize efficient use of the expanding and evolving language model ecosystem.
Supply Function Equilibrium in Networked Electricity Markets

We study deregulated power markets with strategic power suppliers. In deregulated markets, each supplier submits its supply function (i.e., the amount of electricity it is willing to produce at various prices) to the independent system operator (ISO), who based on the submitted supply functions, dispatches the suppliers to clear the market with minimal total generation cost. If all suppliers reported their true marginal cost functions as supply functions, the market outcome would be efficient (i.e., the total generation cost is minimized). However, when suppliers are strategic and aim to maximize their own profits, the reported supply functions are not necessarily the true marginal cost functions, and the resulting market outcome may be inefficient. The efficiency loss depends crucially on the topology of the underlying transmission network. This paper provides an analytical upper bound of the efficiency loss due to strategic suppliers, and proves that the bound is tight under a large class of transmission networks (i.e., weakly cyclic networks). Our upper bound sheds light on how the efficiency loss depends on the transmission network topology (e.g., the degrees of nodes, the admittances and flow limits of transmission lines).
Systematically Detecting Packet Validation Vulnerabilities in Embedded Network Stacks

Embedded Network Stacks (ENS) enable low-resource devices to communicate with the outside world, facilitating the development of Internet of Things and Cyber-Physical Systems. Some defects in ENS are thus high-severity cybersecurity vulnerabilities: they are remotely triggerable and can impact the physical world. While prior research has shed light on the characteristics of defects in many classes of software systems, no study has described the properties of ENS defects nor identified a systematic technique to expose them. The most common automated approach to detecting ENS defects is feedback-driven randomized dynamic analysis (“fuzzing”), a costly and unpredictable technique. This paper provides the first systematic characterization of cybersecurity vulnerabilities in ENS. We analyzed 61 vulnerabilities across 6 open-source ENS. Most of these ENS defects are concentrated in the transport and network layers of the network stack, require reaching different states in the network protocol, and can be triggered by only 1–2 modifications to a single packet. We therefore propose a novel systematic testing framework that focuses on the transport and network layers, uses seeds that cover a network protocol's states, and systematically modifies packet fields. We evaluate this framework on 4 ENS and replicated 12 of the 14 reported IP/TCP/UDP vulnerabilities. On recent versions of these ENSs, it discovered 7 novel defects (6 assigned CVES) during a bounded systematic test that covered all protocol states and made up to 3 modifications per packet. We found defects in 3 of the 4 ENS we tested that had not been found by prior fuzzing research. Our results suggest that fuzzing should be deferred until after systematic testing is employed.
Carroll black holes

Despite the absence of a lightcone structure, some solutions of Carroll gravity show black hole-like behaviour. We define Carroll black holes as solutions of Carroll gravity that exhibit Carroll thermal properties and have a Carroll extremal surface, notions introduced in our work. The latter is a Carroll analogue of a Lorentzian extremal surface. As examples, we discuss the Carroll versions of Schwarzschild, Reissner-Nordström, and BTZ black holes and black hole solutions of generic 1+1 dimensional Carroll dilaton gravity, including Carroll JT and Carroll Witten black holes.