Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypoxia-Inducible Factor 1 and Dysregulated c-Myc Cooperatively Induce Vascular Endothelial Growth Factor and Metabolic Switches Hexokinase 2 and Pyruvate Dehydrogenase Kinase 1

ABSTRACT Hypoxia is a pervasive microenvironmental factor that affects normal development as well as tumor progression. In most normal cells, hypoxia stabilizes hypoxia-inducible transcription factors (HIFs), particularly HIF-1, which activates genes involved in anaerobic metabolism and angiogenesis. As hypoxia signals a cellular deprivation state, HIF-1 has also been reported to counter the activity of MYC, which encodes a transcription factor that drives cell growth and proliferation. Since many human cancers express dysregulated MYC, we sought to determine whether HIF-1 would in fact collaborate with dysregulated MYC rather countering its function. Here, using the P493-6 Burkitt's lymphoma model with an inducible MYC, we demonstrate that HIF-1 cooperates with dysregulated c-Myc to promote glycolysis by induction of hexokinase 2, which catalyzes the first step of glycolysis, and pyruvate dehydrogenase kinase 1, which inactivates pyruvate dehydrogenase and diminishes mitochondrial respiration. We also found the collaborative induction of vascular endothelial growth factor (VEGF) by HIF-1 and dysregulated c-Myc. This study reports the previously unsuspected collaboration between HIF-1 and dysregulated MYC and thereby provides additional insights into the regulation of VEGF and the Warburg effect, which describes the propensity for cancer cells to convert glucose to lactate.
Has Approximate Machine Unlearning been evaluated properly? From Auditing to Side Effects

The growing concerns surrounding data privacy and security have underscored the critical necessity for machine unlearning--aimed at fully removing data lineage from machine learning models. MLaaS providers expect this to be their ultimate safeguard for regulatory compliance. Despite its critical importance, the pace at which privacy communities have been developing and implementing strong methods to verify the effectiveness of machine unlearning has been disappointingly slow, with this vital area often receiving insufficient focus. This paper seeks to address this shortfall by introducing well-defined and effective metrics for black-box unlearning auditing tasks. We transform the auditing challenge into a question of non-membership inference and develop efficient metrics for auditing. By relying exclusively on the original and unlearned models--eliminating the need to train additional shadow models--our approach simplifies the evaluation of unlearning at the individual data point level. Utilizing these metrics, we conduct an in-depth analysis of current approximate machine unlearning algorithms, identifying three key directions where these approaches fall short: utility, resilience, and equity. Our aim is that this work will greatly improve our understanding of approximate machine unlearning methods, taking a significant stride towards converting the theoretical right to data erasure into a auditable reality.
NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment

This article describes one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover, and irrigation intensity environmental data records (EDRs) from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), Advanced Scatterometer (ASCAT), Moderate-Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Soil Moisture Ocean Salinity (SMOS) mission, and Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of the NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing it to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and evapotranspiration. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g., SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g., SMMR, SSM/I, AMSR-E). The evaluation also indicates the high skill of NCA-LDAS when compared with other LSM products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of the western United States during 1979–2015, particularly in the southwestern United States, consistent with the trends from the U.S. Drought Monitor, albeit for a shorter 2000–15 time period.
Variable heavy tailed durations in Internet traffic. Part I. Understanding heavy tails

The paper is part of a larger paper (see Hernandez-Campos, F. et al., http://www.cs.unc.edu/Research/dirt/proj/marron/VarHeavyTails, 2002) that studies the tails of the duration distribution of Internet data flows, and their "heaviness". Data analysis motivates the concepts of moderate, far and extreme tails for understanding the richness of information available in the data. The analysis also motivates a notion of "variable tail index", which leads to a generalization of existing theory for heavy tail durations leading to long range dependence. The emphasis here is on understanding heavy tails.
BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting

While neural rendering has demonstrated impressive capabilities in 3D scene reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and accurate camera poses. Numerous approaches have been proposed to train Neural Radiance Fields (NeRF) with motion-blurred images, commonly encountered in real-world scenarios such as low-light or long-exposure conditions. However, the implicit representation of NeRF struggles to accurately recover intricate details from severely motion-blurred images and cannot achieve real-time rendering. In contrast, recent advancements in 3D Gaussian Splatting achieve high-quality 3D scene reconstruction and real-time rendering by explicitly optimizing point clouds as Gaussian spheres. In this paper, we introduce a novel approach, named BAD-Gaussians (Bundle Adjusted Deblur Gaussian Splatting), which leverages explicit Gaussian representation and handles severe motion-blurred images with inaccurate camera poses to achieve high-quality scene reconstruction. Our method models the physical image formation process of motion-blurred images and jointly learns the parameters of Gaussians while recovering camera motion trajectories during exposure time. In our experiments, we demonstrate that BAD-Gaussians not only achieves superior rendering quality compared to previous state-of-the-art deblur neural rendering methods on both synthetic and real datasets but also enables real-time rendering capabilities. Our project page and source code is available at https://lingzhezhao.github. io/BAD-Gaussians/
Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation

In this paper, we explore the visual representations produced from a pre-trained text-to-video (T2V) diffusion model for video understanding tasks. We hypothesize that the latent representation learned from a pretrained generative T2V model encapsulates rich semantics and coherent temporal correspondences, thereby naturally facilitating video understanding. Our hypothesis is validated through the classic referring video object segmentation (R-VOS) task. We introduce a novel framework, termed ``VD-IT'', tailored with dedicatedly designed components built upon a fixed pretrained T2V model. Specifically, VD-IT uses textual information as a conditional input, ensuring semantic consistency across time for precise temporal instance matching. It further incorporates image tokens as supplementary textual inputs, enriching the feature set to generate detailed and nuanced masks.Besides, instead of using the standard Gaussian noise, we propose to predict the video-specific noise with an extra noise prediction module, which can help preserve the feature fidelity and elevates segmentation quality. Through extensive experiments, we surprisingly observe that fixed generative T2V diffusion models, unlike commonly used video backbones (e.g., Video Swin Transformer) pretrained with discriminative image/video pre-tasks, exhibit better potential to maintain semantic alignment and temporal consistency. On existing standard benchmarks, our VD-IT achieves highly competitive results, surpassing many existing state-of-the-art methods. The code will be available at \url{https://github.com/buxiangzhiren/VD-IT}
Pragmatic Competence Evaluation of Large Language Models for Korean

The current evaluation of Large Language Models (LLMs) predominantly relies on benchmarks focusing on their embedded knowledge by testing through multiple-choice questions (MCQs), a format inherently suited for automated evaluation. Our study extends this evaluation to explore LLMs' pragmatic competence--a facet previously underexamined before the advent of sophisticated LLMs, specifically in the context of Korean. We employ two distinct evaluation setups: the conventional MCQ format, adapted for automatic evaluation, and Open-Ended Questions (OEQs), assessed by human experts, to examine LLMs' narrative response capabilities without predefined options. Our findings reveal that GPT-4 excels, scoring 81.11 and 85.69 in the MCQ and OEQ setups, respectively, with HyperCLOVA X, an LLM optimized for Korean, closely following, especially in the OEQ setup, demonstrating a score of 81.56 with a marginal difference of 4.13 points compared to GPT-4. Furthermore, while few-shot learning strategies generally enhance LLM performance, Chain-of-Thought (CoT) prompting introduces a bias toward literal interpretations, hindering accurate pragmatic inference. Considering the growing expectation for LLMs to understand and produce language that aligns with human communicative norms, our findings emphasize the importance for advancing LLMs' abilities to grasp and convey sophisticated meanings beyond mere literal interpretations.
Uniform boundedness of semistable pure sheaves on projective manifolds

We prove uniform boundedness statements for semistable pure sheaves on projective manifolds. For example, we prove that the set of isomorphism classes of pure sheaves of dimension 2 that are slope semistable with respect to ample classes that vary in a compact set $K$ are bounded. We also prove uniform boundedness for pure sheaves of higher dimension, but with restrictions on the compact set $K$. As applications we get new statements about moduli spaces of semistable sheaves, and the wall-chamber structure that governs their variation.
Effects of Age and Season on in vivo Embryo Production of Friesian Holstein Cows

: Embryo transfer is one of the leading technologies that is now being applied in an effort to increase the population and productivity of dairy cattle in Indonesia. Several factors may affect embryo production including age and season. In this study, the effects of age and season on in vivo embryo production were evaluated on Friesian Holstein (FH) cows. A total of 74 embryo production records from 10 FH cows aged 25-120 months were included in this study. The age of FH cows was divided into four categories, namely 25-48, 49-72, 73-96 and 97-120 months. The factor of season was divided into two categories, namely rainy (October-April) and dry (May-September). The variables observed were the number of corpus luteum, total embryo/ova, transferable embryo, degenerative cells, and nonfertilized oocytes. The results showed that the corpus luteum, total embryo/ova, and degenerative cells were not affected by age. However, the age significantly affected transferable embryos and nonfertilized oocytes, with the optimum range of age 49-72 months for transferable embryos. On the other hand, the season had no significant effect on all embryo production parameters. It could be concluded that age is a significant determinant for in vivo embryo production of FH cows. Meanwhile, the different seasons do not alter embryo production, which indicates that embryo collection can be performed throughout the year.
Enlightening the blind spot of the Michaelis-Menten rate law: The role of relaxation dynamics in molecular complex formation

The century-long Michaelis-Menten rate law and its modifications in the modeling of biochemical rate processes stand on the assumption that the concentration of the complex of interacting molecules, at each moment, rapidly approaches an equilibrium (quasi-steady state) compared to the pace of molecular concentration changes. Yet, in the case of actively time-varying molecular concentrations with transient or oscillatory dynamics, the deviation of the complex profile from the quasi-steady state becomes relevant. A recent theoretical approach, known as the effective time-delay scheme (ETS), suggests that the delay by the relaxation time of molecular complex formation contributes to the substantial breakdown of the quasi-steady state assumption. Here, we systematically expand this ETS and inquire into the comprehensive roles of relaxation dynamics in complex formation. Through the modeling of rhythmic protein-protein and protein-DNA interactions and the mammalian circadian clock, our analysis reveals the effect of the relaxation dynamics beyond the time delay, which extends to the dampening of changes in the complex concentration with a reduction in the oscillation amplitude against the quasi-steady state. Interestingly, the combined effect of the time delay and amplitude reduction shapes both qualitative and quantitative oscillatory patterns such as the emergence and variability of the mammalian circadian rhythms. These findings highlight the drawback of the routine assumption of quasi-steady states and enhance the mechanistic understanding of rich time-varying biomolecular activities.
Multistep Nature of Microvascular Recruitment of Ex Vivo–expanded Embryonic Endothelial Progenitor Cells during Tumor Angiogenesis

Tissue neovascularization involves recruitment of circulating endothelial progenitor cells that originate in the bone marrow. Here, we show that a class of embryonic endothelial progenitor cells (Tie-2+, c-Kit+, Sca-1+, and Flk-1−/low), which were isolated at E7.5 of mouse development at the onset of vasculogenesis, retain their ability to contribute to tumor angiogenesis in the adult. Using intravital fluorescence videomicroscopy, we further defined the multistep process of embryonic endothelial progenitor cell (eEPC) homing and incorporation. Circulating eEPCs are specifically arrested in “hot spots” within the tumor microvasculature, extravasate into the interstitium, form multicellular clusters, and incorporate into functional vascular networks. Expression analysis and in vivo blocking experiments provide evidence that the initial cell arrest of eEPC homing is mediated by E- and P-selectin and P-selectin glycoprotein ligand 1. This paper provides the first in vivo insights into the mechanisms of endothelial progenitor cell recruitment and, thus, indicates novel ways to interfere with pathological neovascularization.
Design, Implementation and Evaluation of Congestion Control for Multipath TCP

Multipath TCP, as proposed by the IETF working group mptcp, allows a single data stream to be split across multiple paths. This has obvious benefits for reliability, and it can also lead to more efficient use of networked resources. We describe the design of a multipath congestion control algorithm, we implement it in Linux, and we evaluate it for multihomed servers, data centers and mobile clients. We show that some 'obvious' solutions for multipath congestion control can be harmful, but that our algorithm improves throughput and fairness compared to single-path TCP. Our algorithmis a drop-in replacement for TCP, and we believe it is safe to deploy.
Fr\'{e}chet Edit Distance

We define and investigate the Fr\'{e}chet edit distance problem. Given two polygonal curves $\pi$ and $\sigma$ and a threshhold value $\delta>0$, we seek the minimum number of edits to $\sigma$ such that the Fr\'{e}chet distance between the edited $\sigma$ and $\pi$ is at most $\delta$. For the edit operations we consider three cases, namely, deletion of vertices, insertion of vertices, or both. For this basic problem we consider a number of variants. Specifically, we provide polynomial time algorithms for both discrete and continuous Fr\'{e}chet edit distance variants, as well as hardness results for weak Fr\'{e}chet edit distance variants.
Investigation of magnetic order influenced phonon and electron dynamics in MnBi$_{2}$Te$_{4}$ and Sb doped MnBi$_{2}$Te$_{4}$ through terahertz time-domain spectroscopy

MnBi$_{2}$Te$_{4}$, the first topological insulator with inherent magnetic ordering, has attracted significant attention recently for providing a platform to realize several exotic quantum phenomena at relatively higher temperatures. In this work, we have carried out an exhaustive investigation of MnBi$_{2}$Te$_{4}$ and Sb doped MnBi$_{2}$Te$_{4}$ thin films using THz time-domain spectroscopy. The extracted real THz conductivity displays a strong IR active E$_u$ phonon absorption peak (at $\sim$1.5 THz) merged on top of the Drude-like contributions from bulk and surface electrons. The extracted parameters from the THz conductivity data fitted to the Drude-Fano-Lorentz model, show significant changes in their temperature dependence around the magnetic ordering N\'eel temperature of $\sim$ 25K, which is suggestive of the coupling between magnetic ordering and electronic band structure. The frequency of the E$_u$ phonon displays an anomalous blue-shift with increasing temperatures by $\sim$ 0.1 THz ($\sim$7 %) for MnBi$_{2}$Te$_{4}$ and $\sim$0.2 THz ($\sim$13 %) for Sb doped MnBi$_{2}$Te$_{4}$ between 7K and 250K. The line-shape of the E$_u$ phonon mode in Sb doped MnBi$_{2}$Te$_{4}$ shows significant Fano asymmetry compared to that of MnBi$_{2}$Te$_{4}$, indicating that Sb doping plays an important role in the Fano interference between the phonons and the electrons, in this system. These results indicate that the anomalous phonon behaviour seen in MBT arise mainly from positive cubic anharmonicity induced self energy parameter, whereas both anharmonicity and the electron phonon coupling are at play in making the relatively higher anomalous blue shift of phonons in MBST. Our studies provide the first comprehensive understanding of the phonon and electron dynamics of MnBi$_{2}$Te$_{4}$ and Sb doped MnBi$_{2}$Te$_{4}$ in the THz range using time-domain THz spectroscopy.
PAON: A New Neuron Model using Pad\'e Approximants

Convolutional neural networks (CNN) are built upon the classical McCulloch-Pitts neuron model, which is essentially a linear model, where the nonlinearity is provided by a separate activation function. Several researchers have proposed enhanced neuron models, including quadratic neurons, generalized operational neurons, generative neurons, and super neurons, with stronger nonlinearity than that provided by the pointwise activation function. There has also been a proposal to use Pade approximation as a generalized activation function. In this paper, we introduce a brand new neuron model called Pade neurons (Paons), inspired by the Pade approximants, which is the best mathematical approximation of a transcendental function as a ratio of polynomials with different orders. We show that Paons are a super set of all other proposed neuron models. Hence, the basic neuron in any known CNN model can be replaced by Paons. In this paper, we extend the well-known ResNet to PadeNet (built by Paons) to demonstrate the concept. Our experiments on the single-image super-resolution task show that PadeNets can obtain better results than competing architectures.
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks

Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging. In this paper we propose a fine-grained validation framework that is not overly reliant on any one facet of these sociotechnical systems, and that recognises their inherent modular structure: technical building blocks, user-facing explanatory artefacts and social communication protocols. While we concur that user studies are invaluable in assessing the quality and effectiveness of explanation presentation and delivery strategies from the explainees' perspective in a particular deployment context, the underlying explanation generation mechanisms require a separate, predominantly algorithmic validation strategy that accounts for the technical and human-centred desiderata of their (numerical) outputs. Such a comprehensive sociotechnical utility-based evaluation framework could allow to systematically reason about the properties and downstream influence of different building blocks from which explainable artificial intelligence systems are composed -- accounting for a diverse range of their engineering and social aspects -- in view of the anticipated use case.
Normal pressure hydrocephalus co-existing with a second dementia disorder

An 87-year-old woman had both Lewy body dementia and normal pressure hydrocephalus simultaneously. One should not automatically exclude the possibility of normal pressure hydrocephalus simply because another neurodegenerative disorder is present, especially if the second disorder masks typical symptoms of normal pressure hydrocephalus. Better identification of dual diagnosis patients could lead to improvements in gait and incontinence following ventriculoperitoneal or lumboperitoneal shunting, even if the natural history of the other neurodegenerative disease remains unchanged.