Miracle-dz/UniFAR-FTUs huggingface dataset:
The Efficacy of Retrograde Infusion with LY231617 in a Rat Middle Cerebral Artery Occlusion Model

Abstract Background and Purpose : We examined the efficacy of the antioxidant LY231617 administered five hours following middle cerebral artery (MCA) occlusion in rats. Methods : The treatment was contrived for a two hour interval. Group A (n=16) was left untreated. Group B (n=16) received an intravenous infusion of LY231617. Group C (n=6) received saline (86 μI/min) by retrograde infusion of the cerebral vein (RICV). Group D (n=22) was administered LY231617 (10mg/kg/2hr) in saline (86μI/min) by RICV. Local cerebral blood flow with [14C]-iodoantipyrine and blood-brain transfer constant with 14C-α-amino-isobutyric acid were examined. Early ischemic damage was histologically examined with cresyl violet and Luxol fast blue and with triphenyl-tetrazolium chloride. Results : The results revealed a marked increase in local cerebral blood flow (over 600%, p < 0.01) after RICV with LY231617, with a significant improvement of BBB permeability in rats from group D. Ischemic brain damage measured with Luxol fast blue and triphenyl-tetrazolium chloride methods showed a significant improvement (50-91 %) of ischemic damage in group D, as compared to groups B and C. Conclusion : Retrograde infusion of the cerebral vein with LY231617 resulted in a significant amelioration at seven hours post MCA occlusion.
Entangling Machine Learning with Quantum Tensor Networks

This paper examines the use of tensor networks, which can efficiently represent high-dimensional quantum states, in language modeling. It is a distillation and continuation of the work done in (van der Poel, 2023). To do so, we will abstract the problem down to modeling Motzkin spin chains, which exhibit long-range correlations reminiscent of those found in language. The Matrix Product State (MPS), also known as the tensor train, has a bond dimension which scales as the length of the sequence it models. To combat this, we use the factored core MPS, whose bond dimension scales sub-linearly. We find that the tensor models reach near perfect classifying ability, and maintain a stable level of performance as the number of valid training examples is decreased.
Towards Unsupervised Question Answering System with Multi-level Summarization for Legal Text

This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet novel similarity and distance-based unsupervised approach to generate labels. Further, we explore the Multi-level fusion of Legal-Bert embeddings using ensemble features, including CNN, GRU, and LSTM. To address the lengthy nature of Legal explanation in the dataset, we introduce T5-based segment-wise summarization, which successfully retained crucial information, enhancing the model's performance. Our unsupervised system witnessed a 20-point increase in macro F1-score on the development set and a 10-point increase on the test set, which is promising given its uncomplicated architecture.
Fission of pancreatic islets during postnatal growth of the mouse

A cell composition analysis was made of the pancreatic islets in postnatal H253 mice. This line has a lacZ insertion on the X chromosome so that in female hemizygotes 50% of cells should be positive for β‐galactosidase and 50% negative. Immediately after birth, the islets were of a heterogeneous cell composition. However, by 4 weeks some islets have become homogeneous. This suggests that islets progress towards monoclonality in a similar way to the intestinal crypts and stomach gastric glands. Pancreatic islets may therefore represent ‘structural proliferative units’ in the overall histological organization of the pancreas. Reduction of genetic heterogeneity might arise from cell turnover, fission of islets or both. Analysis of the cell composition of the X‐inactivation mosaic mice also provides the first clear evidence for islet fission in pancreatic development. Irregularly shaped islets resembling dumb‐bells, with a characteristic neck of α‐cells, were observed with decreasing frequency with increasing age. Three‐dimensional reconstruction confirmed their resemblance to conjoined islets. The cell composition analysis showed: (1) the relatedness of the two sides of a dumb‐bell islet is significantly higher than between two non‐dumb‐bell islets and (2) the relatedness of two randomly selected islets decreases as the distance between them increases. This suggests that dumb‐bell islets are in a state of fission rather than fusion, and that islet fission is a mode of islet production in the postnatal pancreas.
On Designing Consistent Covariance Recovery from a Deep Learning Visual Odometry Engine

Deep learning techniques have significantly advanced in providing accurate visual odometry solutions by leveraging large datasets. However, generating uncertainty estimates for these methods remains a challenge. Traditional sensor fusion approaches in a Bayesian framework are well-established, but deep learning techniques with millions of parameters lack efficient methods for uncertainty estimation. This paper addresses the issue of uncertainty estimation for pre-trained deep-learning models in monocular visual odometry. We propose formulating a factor graph on an implicit layer of the deep learning network to recover relative covariance estimates, which allows us to determine the covariance of the Visual Odometry (VO) solution. We showcase the consistency of the deep learning engine's covariance approximation with an empirical analysis of the covariance model on the EUROC datasets to demonstrate the correctness of our formulation.
Crude Existence: Environment and the Politics of Oil in Northern Angola

After decades of civil war and instability, the African country of Angola is experiencing a spectacular economic boom thanks to its most valuable natural resource: oil. But oil extraction--both onand offshore--is a toxic remedy for the country's economic ills, with devastating effects on both the environment and traditional livelihoods. Focusing on the everyday realities of people living in the extraction zones, Kristin Reed explores the exclusion, degradation, and violence that are the fruits of petrocapitalism in Angola. Copyright Information: All rights reserved unless otherwise indicated. Contact the author or original publisher for any necessary permissions. eScholarship is not the copyright owner for deposited works. Learn more at http://www.escholarship. org/help_copyright. html#reuse Crude Existence: Environment and the Politics of Oil in Northern Angola
Elevating Software Quality in Agile Environments: The Role of Testing Professionals in Unit Testing

Testing is an essential quality activity in the software development process. Usually, a software system is tested on several levels, starting with unit testing that checks the smallest parts of the code until acceptance testing, which is focused on the validations with the end-user. Historically, unit testing has been the domain of developers, who are responsible for ensuring the accuracy of their code. However, in agile environments, testing professionals play an integral role in various quality improvement initiatives throughout each development cycle. This paper explores the participation of test engineers in unit testing within an industrial context, employing a survey-based research methodology. Our findings demonstrate that testing professionals have the potential to strengthen unit testing by collaborating with developers to craft thorough test cases and fostering a culture of mutual learning and cooperation, ultimately contributing to increasing the overall quality of software projects.
A Machine Learning Approach for Multiscale Modeling of the Facet Capsular Ligament

We develop a new neural network architecture that strictly enforces constitutive constraints such as polyconvexity, frame-indifference, zero strain energy with zero deformations, and the symmetry of the stress and material stiffness. Additionally, we show that for this neural network, the accuracy is significantly improved by using a Sobolev minimization strategy that includes derivative terms. Using our network and Sobolev minimization, we obtain a NMSE of 0.15% for the energy, 0.815% averaged across the components of the stress, and 5.4% averaged across the components of the stiffness. This machine learned constitutive model was deployed in a finite element simulation of a facet capsular ligament. The displacement fields and stress-strain curves where compared to a multiscale simulation that required running on a GPU based supercomputer. At 70% strain, the model using the neural network had less than 10% relative error in the mean stress value.
Local Approximation of Secrecy Capacity

This paper uses Euclidean Information Theory (EIT) to analyze the wiretap channel. We investigate a scenario of efficiently transmitting a small amount of information subject to compression rate and secrecy constraints. We transform the information-theoretic problem into a linear algebra problem and obtain the perturbed probability distributions such that secrecy is achievable. Local approximations are being used in order to obtain an estimate of the secrecy capacity by solving a generalized eigenvalue problem.
A Unified and General Framework for Continual Learning

Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. However, these methods lack a unified framework and common terminology for describing their approaches. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies. Notably, this new framework is capable of encompassing established CL approaches as special instances within a unified and general optimization objective. An intriguing finding is that despite their diverse origins, these methods share common mathematical structures. This observation highlights the compatibility of these seemingly distinct techniques, revealing their interconnectedness through a shared underlying optimization objective. Moreover, the proposed general framework introduces an innovative concept called refresh learning, specifically designed to enhance the CL performance. This novel approach draws inspiration from neuroscience, where the human brain often sheds outdated information to improve the retention of crucial knowledge and facilitate the acquisition of new information. In essence, refresh learning operates by initially unlearning current data and subsequently relearning it. It serves as a versatile plug-in that seamlessly integrates with existing CL methods, offering an adaptable and effective enhancement to the learning process. Extensive experiments on CL benchmarks and theoretical analysis demonstrate the effectiveness of the proposed refresh learning. Code is available at \url{https://github.com/joey-wang123/CL-refresh-learning}.
STAT3 but Not STAT1 Is Required for Astrocyte Differentiation

The JAK-STAT signaling pathway has been implicated in astrocyte differentiation. Both STAT1 and STAT3 are expressed in the central nervous system and are thought to be important for glial differentiation, as mainly demonstrated in vitro; however direct in vivo evidence is missing. We investigated whether STAT1 and STAT3 are essential for astrocyte development by testing the STAT responsiveness of astrocyte progenitors. STAT3 was absent in the ventricular zone where glial progenitors are born but begins to appear at the marginal zone at E16.5. At E18.5, both phospho-STAT1 and phospho-STAT3 were present in glial fibrillary acidic protein (GFAP)-expressing white matter astrocytes. Overexpression of STAT3 by electroporation of chicks in ovo induced increased numbers of astrocyte progenitors in the spinal cord. Likewise, elimination of STAT3 in Stat3 conditional knockout (cKO) mice resulted in depletion of white matter astrocytes. Interestingly, elimination of STAT1 in Stat1 null mice did not inhibit astrocyte differentiation and deletion of Stat1 failed to aggravate the glial defects in Stat3 cKO mice. Measuring the activity of STAT binding elements and the gfap promoter in the presence of various STAT mutants revealed that transactivation depended on the activity of STAT3 not STAT1. No synergistic interaction between STAT1 and STAT3 was observed. Cortical progenitors of Stat1 null; Stat3 cKO mice generated astrocytes when STAT3 or the splice variant Stat3β was supplied, but not when STAT1 was introduced. Together, our results suggest that STAT3 is necessary and sufficient for astrocyte differentiation whereas STAT1 is dispensable.
Hierarchical NeuroSymbolic Approach for Action Quality Assessment

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. We will open-source all of our annotated training data and code for ease of reproducibility.
Train Ego-Path Detection on Railway Tracks Using End-to-End Deep Learning

This paper introduces the task of"train ego-path detection", a refined approach to railway track detection designed for intelligent onboard vision systems. Whereas existing research lacks precision and often considers all tracks within the visual field uniformly, our proposed task specifically aims to identify the train's immediate path, or"ego-path", within potentially complex and dynamic railway environments. Building on this, we extend the RailSem19 dataset with ego-path annotations, facilitating further research in this direction. At the heart of our study lies TEP-Net, an end-to-end deep learning framework tailored for ego-path detection, featuring a configurable model architecture, a dynamic data augmentation strategy, and a domain-specific loss function. Leveraging a regression-based approach, TEP-Net outperforms SOTA: while addressing the track detection problem in a more nuanced way than previously, our model achieves 97.5% IoU on the test set and is faster than all existing methods. Further comparative analysis highlights the relevance of the conceptual choices behind TEP-Net, demonstrating its inherent propensity for robustness across diverse environmental conditions and operational dynamics. This work opens promising avenues for the development of intelligent driver assistance systems and autonomous train operations, paving the way toward safer and more efficient railway transportation.
LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images

Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored vision-for-science research area. It seeks to distinguish the worked material, which is critical for understanding archaeological artifacts, material interactions, tool functionalities, and dental records. However, this challenging task goes beyond the well-studied image classification problem for common objects. It is affected by many confounders owing to the complex wear mechanism and microscopic imaging, which makes it difficult even for human experts to identify the worked material successfully. In this paper, we investigate the following three questions on this unique vision task for the first time:(i) How well can state-of-the-art pre-trained models (like DINOv2) generalize to the rarely seen domain? (ii) How can few-shot learning be exploited for scarce microscopic images? (iii) How do the ambiguous magnification and sensing modality influence the classification accuracy? To study these, we collaborated with archaeologists and built the first open-source and the largest LUWA dataset containing 23,130 microscopic images with different magnifications and sensing modalities. Extensive experiments show that existing pre-trained models notably outperform human experts but still leave a large gap for improvements. Most importantly, the LUWA dataset provides an underexplored opportunity for vision and learning communities and complements existing image classification problems on common objects.
A scaling law for a model of epitaxially strained elastic films with dislocations

A static variational model for shape formation in heteroepitaxial crystal growth is considered. The energy functional takes into account surface energy, elastic misfit-energy and nucleation energy of dislocations. A scaling law for the infimal energy is proven. The results quantify the expectation that in certain parameter regimes, island formation or topological defects are favorable. This generalizes results in the purely elastic setting from [Goldman and Zwicknagl (2014)]. To handle dislocations in the lower bound, a new variant of a ball-construction combined with thorough local estimates is presented.
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation

Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance. Significant efforts have been devoted to promote evaluation accuracy under limited compression ratio while overlooked the robustness of distilled dataset. In this work, we introduce a comprehensive benchmark that, to the best of our knowledge, is the most extensive to date for evaluating the adversarial robustness of distilled datasets in a unified way. Our benchmark significantly expands upon prior efforts by incorporating a wider range of dataset distillation methods, including the latest advancements such as TESLA and SRe2L, a diverse array of adversarial attack methods, and evaluations across a broader and more extensive collection of datasets such as ImageNet-1K. Moreover, we assessed the robustness of these distilled datasets against representative adversarial attack algorithms like PGD and AutoAttack, while exploring their resilience from a frequency perspective. We also discovered that incorporating distilled data into the training batches of the original dataset can yield to improvement of robustness.
Parametric shape analysis via 3-valued logic

We present a family of abstract-interpretation algorithms that are capable of determining "shape invariants" of programs that perform destructive updating on dynamically allocated storage. The main idea is to represent the stores that can passibly arise during execution using three-valued logical structures.Questions about properties of stores can be answered by evaluating predicate-logic formulae using Kleene's semantics of three-valued logic: ¿If a formula evaluates to true, then the formula holds in every store represented by the three-valued structure. ¿If a formula evaluates to false, then the formula does not hold in any store represented by the three-valued structure. ¿If a formula evaluates to unknown, then we do not know if this formula always holds, never holds, or sometimes holds and sometimes does not hold in the stores represented by the three-valued structure. Three-valued logical structures are thus a conservative representation of memory stores.The approach described is a parametric framework: It provides the basis for generating a family of shape-analysis algorithms by varying the vocabulary used in the three-valued logic.
kDGLM: a R package for Bayesian analysis of Generalized Dynamic Linear Models

This paper introduces kDGLM, an R package designed for Bayesian analysis of Generalized Dynamic Linear Models (GDLM), with a primary focus on both uni- and multivariate exponential families. Emphasizing sequential inference for time series data, the kDGLM package provides comprehensive support for fitting, smoothing, monitoring, and feed-forward interventions. The methodology employed by kDGLM, as proposed in Alves et al. (2024), seamlessly integrates with well-established techniques from the literature, particularly those used in (Gaussian) Dynamic Models. These include discount strategies, autoregressive components, transfer functions, and more. Leveraging key properties of the Kalman filter and smoothing, kDGLM exhibits remarkable computational efficiency, enabling virtually instantaneous fitting times that scale linearly with the length of the time series. This characteristic makes it an exceptionally powerful tool for the analysis of extended time series. For example, when modeling monthly hospital admissions in Brazil due to gastroenteritis from 2010 to 2022, the fitting process took a mere 0.11s. Even in a spatial-time variant of the model (27 outcomes, 110 latent states, and 156 months, yielding 17,160 parameters), the fitting time was only 4.24s. Currently, the kDGLM package supports a range of distributions, including univariate Normal (unknown mean and observational variance), bivariate Normal (unknown means, observational variances, and correlation), Poisson, Gamma (known shape and unknown mean), and Multinomial (known number of trials and unknown event probabilities). Additionally, kDGLM allows the joint modeling of multiple time series, provided each series follows one of the supported distributions. Ongoing efforts aim to continuously expand the supported distributions.
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation

In online video platforms, reading or writing comments on interesting videos has become an essential part of the video watching experience. However, existing video recommender systems mainly model users' interaction behaviors with videos, lacking consideration of comments in user behavior modeling. In this paper, we propose a novel recommendation approach called LSVCR by leveraging user interaction histories with both videos and comments, so as to jointly conduct personalized video and comment recommendation. Specifically, our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. The SR model serves as the primary recommendation backbone (retained in deployment) of our approach, allowing for efficient user preference modeling. Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors. In order to integrate the merits of the SR model and the supplemental LLM recommender, we design a twostage training paradigm. The first stage is personalized preference alignment, which aims to align the preference representations from both components, thereby enhancing the semantics of the SR model. The second stage is recommendation-oriented fine-tuning, in which the alignment-enhanced SR model is fine-tuned according to specific objectives. Extensive experiments in both video and comment recommendation tasks demonstrate the effectiveness of LSVCR. Additionally, online A/B testing on the KuaiShou platform verifies the actual benefits brought by our approach. In particular, we achieve a significant overall gain of 4.13% in comment watch time.