Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of KDM6B epigenetically confers resistance to lipotoxicity in nonalcoholic fatty liver disease–related HCC

Background: NAFLD caused by abnormalities in hepatic lipid metabolism is associated with an increased risk of developing HCC. The molecular mechanisms underlying the progression of NAFLD-related HCC are not fully understood. We investigated the molecular mechanism and role of KDM6B downregulation in NAFLD-related HCC after the KDM6B gene was identified using microarray analysis as commonly downregulated in mouse NAFLD-related HCC and human nonhepatitis B and nonhepatitis C viral-HCC. Methods: The 5-hydroxymethylcytosine levels of KDM6B in HCC cells were determined using glycosylated hydroxymethyl-sensitive PCR. Microarray and chromatin immunoprecipitation analyses using KDM6B-knockout (KO) cells were used to identify KDM6B target genes. Lipotoxicity was assessed using a palmitate-treated cell proliferation assay. Immunohistochemistry was used to evaluate KDM6B expression in human HCC tissues. Results: KDM6B expression levels in HCC cells correlated with the 5-hydroxymethylcytosine levels in the KDM6B gene body region. Gene set enrichment analysis revealed that the lipid metabolism pathway was suppressed in KDM6B-KO cells. KDM6B-KO cells acquired resistance to lipotoxicity (p < 0.01) and downregulated the expression of G0S2, an adipose triglyceride lipase/patatin like phospholipase domain containing 2 (ATGL/PNPLA2) inhibitor, through increased histone H3 lysine-27 trimethylation levels. G0S2 knockdown in KDM6B-expressed HCC cells conferred lipotoxicity resistance, whereas ATGL/PNPLA2 inhibition in the KDM6B-KO cells reduced these effects. Immunohistochemistry revealed that KDM6B expression was decreased in human NAFLD-related HCC tissues (p < 0.001), which was significantly associated with decreased G0S2 expression (p = 0.032). Conclusions: KDM6B-disrupted HCC acquires resistance to lipotoxicity via ATGL/PNPLA2 activation caused by epigenetic downregulation of G0S2 expression. Reduced KDM6B and G0S2 expression levels are common in NAFLD-related HCC. Targeting the KDM6B-G0S2-ATGL/PNPLA2 pathway may be a useful therapeutic strategy for NAFLD-related HCC.
River evaporation and corresponding heat fluxes in forested catchments

River water temperature is a very important variable in ecological studies, especially for the management of fisheries and aquatic resources. Temperature can impact on fish distribution, growth, mortality and community dynamics. River evaporation has been identified as an important heat loss and a key process in the thermal regime of rivers. However, its quantification remains a challenge, mainly because of the difficulty of making direct measurements. The objectives of this study were to characterize the evaporative heat flux at different scales (brook vs river) and to improve the estimation of the evaporative heat flux in a stream temperature model at the hourly timescale. Using a mass balance approach with floating minipans, we measured river evaporation at an hourly timescale in a medium‐sized river (Little Southwest Miramichi) and a small brook (Catamaran Brook) in New Brunswick, Canada. With these direct measurements of evaporation, we developed mass transfer equations to estimate hourly evaporation rates from microclimate conditions measured 2 m above the stream. During the summer 2012, river evaporation was more important for the medium‐sized river with a mean daily evaporation rate of 3.0 mm day−1 in the Little Southwest Miramichi River compared with that of 1.0 mm day−1 in Catamaran Brook. Evaporation was the main heat loss mechanism in the two studied streams and was responsible for 42% of heat losses in the Little Southwest Miramichi River and 34% of heat losses in Catamaran Brook during the summer. Copyright © 2013 John Wiley & Sons, Ltd.
Activity budget and seasonal activity shifts in sympatric lemurs: Increased lean season effort in a cathemeral frugivore contrasts with energy conservation in a diurnal folivore

One of the most fundamental aspects of a species’ behavioral strategy is its activity budget; for primates this generally involves the allocation of available time among resting, feeding, traveling, and social behavior. Comparisons between species, populations, or individuals can reveal divergences in adaptive strategies and current stressors, and reflect responses to such diverse pressures as predation, thermoregulation, nutrition, and social needs. Further, variation across seasons is an important part of behavioral strategies to survive food scarcity; this can involve increasing or decreasing effort. We documented activity over the 24‐h cycle for the cathemeral, frugivorous Eulemur fulvus and the diurnal, folivorous Propithecus diadema across 13–18 months at Tsinjoarivo, Madagascar. Their activity budgets were dominated by resting (E. fulvus: 74.1%; P. diadema: 85.2%), followed by feeding (15.8%, 12.4%), traveling (9.31%, 1.74%) and social activities (0.76%, 0.70%), respectively. The lower feeding and higher resting in P. diadema likely reflect slower gastrointestinal transit and higher reliance on microbial fermentation to extract energy from fibrous food. The two species showed opposite lean season strategies. E. fulvus increased activity, with more feeding but less travel time, consistent with a shift to less‐profitable fruits, and some leaves and flowers, while increasing feeding effort to compensate (“energy maximizing”). P. diadema showed less variation across months, but the lean season still evoked reduced effort across the board (feeding, travel, and social behavior), consistent with a “time minimizing” strategy prioritizing energy conservation and microbe‐assisted digestion. Understanding these divergent shifts is key to understanding natural behavior and the extent of behavioral flexibility under stressful conditions. Finally, the complex patterns of fruit availability (intra‐ and interannually) and the species’ behavioral responses across months underscore the need to move beyond simplistic “lean/abundant season” and “fruit/leaf” dichotomies in understanding underlying energetic strategies, and species’ vulnerability to habitat change.
Memorization with neural nets: going beyond the worst case

In practice, deep neural networks are often able to easily interpolate their training data. To understand this phenomenon, many works have aimed to quantify the memorization capacity of a neural network architecture: the largest number of points such that the architecture can interpolate any placement of these points with any assignment of labels. For real-world data, however, one intuitively expects the presence of a benign structure so that interpolation already occurs at a smaller network size than suggested by memorization capacity. In this paper, we investigate interpolation by adopting an instance-specific viewpoint. We introduce a simple randomized algorithm that, given a fixed finite dataset with two classes, with high probability constructs an interpolating three-layer neural network in polynomial time. The required number of parameters is linked to geometric properties of the two classes and their mutual arrangement. As a result, we obtain guarantees that are independent of the number of samples and hence move beyond worst-case memorization capacity bounds. We illustrate the effectiveness of the algorithm in non-pathological situations with extensive numerical experiments and link the insights back to the theoretical results.
Data-Efficient Power Flow Learning for Network Contingencies

This work presents an efficient data-driven method to learn power flows in grids with network contingencies and to estimate corresponding probabilistic voltage envelopes (PVE). First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage-power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50% reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50-250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75% of N-2 contingency network structures, even without historical N-2 outage data. The proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.
A Roadmap towards Intelligent Operations for Reliable Cloud Computing Systems

The increasing complexity and usage of cloud systems have made it challenging for service providers to ensure reliability. This paper highlights two main challenges, namely internal and external factors, that affect the reliability of cloud microservices. Afterward, we discuss the data-driven approach that can resolve these challenges from four key aspects: ticket management, log management, multimodal analysis, and the microservice resilience testing approach. The experiments conducted show that the proposed data-driven AIOps solution significantly enhances system reliability from multiple angles.
A Novel Computational and Modeling Foundation for Automatic Coherence Assessment

Coherence is an essential property of well-written texts, that refers to the way textual units relate to one another. In the era of generative AI, coherence assessment is essential for many NLP tasks; summarization, generation, long-form question-answering, and more. However, in NLP {coherence} is an ill-defined notion, not having a formal definition or evaluation metrics, that would allow for large-scale automatic and systematic coherence assessment. To bridge this gap, in this work we employ the formal linguistic definition of \citet{Reinhart:1980} of what makes a discourse coherent, consisting of three conditions -- {\em cohesion, consistency} and {\em relevance} -- and formalize these conditions as respective computational tasks. We hypothesize that (i) a model trained on all of these tasks will learn the features required for coherence detection, and that (ii) a joint model for all tasks will exceed the performance of models trained on each task individually. On two benchmarks for coherence scoring rated by humans, one containing 500 automatically-generated short stories and another containing 4k real-world texts, our experiments confirm that jointly training on the proposed tasks leads to better performance on each task compared with task-specific models, and to better performance on assessing coherence overall, compared with strong baselines. We conclude that the formal and computational setup of coherence as proposed here provides a solid foundation for advanced methods of large-scale automatic assessment of coherence.
Flat covers and injective hulls of persistence modules

Motivated by recent progress in topological data analysis, we establish a Matlis duality between injective hulls and flat covers of persistence modules. This extends to a duality between minimal flat and minimal injective resolutions. We utilize the theory of flat cotorsion modules and flat covers developed by Enochs and Xu. By means of this theory we can work with persistence modules which are not tame or even pointwise finite-dimensional.
The multiplicity of the zero Laplacian eigenvalue of uniform hypertrees

In this paper, the Laplacian characteristic polynomial of uniform hypergraphs with cut vertices or pendant edges and the Laplacian matching polynomial of uniform hypergraphs are characterized.The multiplicity of the zero Laplacian eigenvalue of uniform hypertrees is given, which proves the conjecture in \cite{zheng2023zero} (The zero eigenvalue of the Laplacian tensor of a uniform hypergraph, Linear and Multilinear Algebra, (2023) Doi:10.1080/03081087.2023. 2172541).
Robust Asset-Liability Management

How should financial institutions hedge their balance sheets against interest rate risk when they have long-term assets and liabilities? Using the perspective of functional and numerical analysis, we propose a model-free bond portfolio selection method that generalizes classical immunization and accommodates arbitrary liability structure, portfolio constraints, and perturbations in interest rates. We prove the generic existence of an immunizing portfolio that maximizes the worst-case equity with a tight error estimate and provide a solution algorithm. Numerical evaluations using empirical and simulated yield curves from a no-arbitrage term structure model support the feasibility and accuracy of our approach relative to existing methods.
MARL: Multi-scale Archetype Representation Learning for Urban Building Energy Modeling

Building archetypes, representative models of building stock, are crucial for precise energy simulations in Urban Building Energy Modeling. The current widely adopted building archetypes are developed on a nationwide scale, potentially neglecting the impact of local buildings’ geometric specificities. We present Multi-scale Archetype Representation Learning (MARL), an approach that leverages representation learning to extract geometric features from a specific building stock. Built upon VQ-AE, MARL encodes building footprints and purifies geometric information into latent vectors constrained by multiple architectural downstream tasks. These tailored representations are proven valuable for further clustering and building energy modeling. The advantages of our algorithm are its adaptability with respect to the different building footprint sizes, the ability for automatic generation across multi-scale regions, and the preservation of geometric features across neighborhoods and local ecologies. In our study spanning five regions in LA County, we show MARL surpasses both conventional and VQ-AE extracted archetypes in performance. Results demonstrate that geometric feature embeddings significantly improve the accuracy and reliability of energy consumption estimates. Code, dataset and trained models are available on the project page: https://github.com/ZixunHuang1997/MARL-BuildingEnergyEstimation.
Dual-Augmented Transformer Network for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS), a fundamental computer vision task, which aims to segment out the object within only class-level labels. The traditional methods adopt the CNN-based network and utilize the class activation map (CAM) strategy to discover the object regions. However, such methods only focus on the most discriminative region of the object, resulting in incomplete segmentation. An alternative is to explore vision transformers (ViT) to encode the image to acquire the global semantic information. Yet, the lack of transductive bias to objects is a flaw of ViT. In this paper, we explore the dual-augmented transformer network with self-regularization constraints for WSSS. Specifically, we propose a dual network with both CNN-based and transformer networks for mutually complementary learning, where both networks augment the final output for enhancement. Massive systemic evaluations on the challenging PASCAL VOC 2012 benchmark demonstrate the effectiveness of our method, outperforming previous state-of-the-art methods.
Mapping of Internet “Coastlines” via Large Scale Anonymized Network Source Correlations

Expanding the scientific tools available to protect computer networks can be aided by a deeper understanding of the underlying statistical distributions of network traffic and their potential geometric interpretations. Analyses of large scale network observations provide a unique window into studying those underlying statistics. Newly developed GraphBLAS hypersparse matrices and D4M associative array technologies enable the efficient anonymized analysis of network traffic on the scale of trillions of events. This work analyzes over 100,000,000,000 anonymized packets from the largest Internet telescope (CAIDA) and over 10,000,000 anonymized sources from the largest commercial honeyfarm (GreyNoise). Neither CAIDA nor GreyNoise actively emit Internet traffic and provide distinct observations of unsolicited Internet traffic (primarily botnets and scanners). Analysis of these observations confirms the previously observed Cauchy-like distributions describing temporal correlations between Internet sources. The Gull light-house problem is a well-known geometric characterization of the standard Cauchy distribution and motivates a potential geometric interpretation for Internet observations. This work generalizes the Gull lighthouse problem to accommodate larger classes of coastlines, deriving a closed-form solution for the resulting probability distributions, stating and examining the inverse problem of identifying an appropriate coastline given a continuous probability distribution, identifying a geometric heuristic for solving this problem computationally, and applying that heuristic to examine the temporal geometry of different subsets of network observations. Application of this method to the CAIDA and GreyNoise data reveals a several orders of magnitude difference between known benign and other traffic which can lead to potentially novel ways to protect networks.
Cook2LTL: Translating Cooking Recipes to LTL Formulae using Large Language Models

Cooking recipes are challenging to translate to robot plans as they feature rich linguistic complexity, temporally-extended interconnected tasks, and an almost infinite space of possible actions. Our key insight is that combining a source of cooking domain knowledge with a formalism that captures the temporal richness of cooking recipes could enable the extraction of unambiguous, robot-executable plans. In this work, we use Linear Temporal Logic (LTL) as a formal language expressive enough to model the temporal nature of cooking recipes. Leveraging a pretrained Large Language Model (LLM), we present Cook2LTL, a system that translates instruction steps from an arbitrary cooking recipe found on the internet to a set of LTL formulae, grounding high-level cooking actions to a set of primitive actions that are executable by a manipulator in a kitchen environment. Cook2LTL makes use of a caching scheme that dynamically builds a queryable action library at runtime. We instantiate Cook2LTL in a realistic simulation environment (AI2-THOR), and evaluate its performance across a series of cooking recipes. We demonstrate that our system significantly decreases LLM API calls (-51%), latency (-59%), and cost (-42%) compared to a baseline that queries the LLM for every newly encountered action at runtime.
A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos

Nuanced understanding and the generation of detailed descriptive content for (bimanual) manipulation actions in videos is important for disciplines such as robotics, human-computer interaction, and video content analysis. This study describes a novel method, integrating graph based modeling with layered hierarchical attention mechanisms, resulting in higher precision and better comprehensiveness of video descriptions. To achieve this, we encode, first, the spatio-temporal inter dependencies between objects and actions with scene graphs and we combine this, in a second step, with a novel 3-level architecture creating a hierarchical attention mechanism using Graph Attention Networks (GATs). The 3-level GAT architecture allows recognizing local, but also global contextual elements. This way several descriptions with different semantic complexity can be generated in parallel for the same video clip, enhancing the discriminative accuracy of action recognition and action description. The performance of our approach is empirically tested using several 2D and 3D datasets. By comparing our method to the state of the art we consistently obtain better performance concerning accuracy, precision, and contextual relevance when evaluating action recognition as well as description generation. In a large set of ablation experiments we also assess the role of the different components of our model. With our multi-level approach the system obtains different semantic description depths, often observed in descriptions made by different people, too. Furthermore, better insight into bimanual hand-object interactions as achieved by our model may portend advancements in the field of robotics, enabling the emulation of intricate human actions with heightened precision.
Automatic Data Repair: Are We Ready to Deploy?

Data quality is paramount in today's data-driven world, especially in the era of generative AI. Dirty data with errors and inconsistencies usually leads to flawed insights, unreliable decision-making, and biased or low-quality outputs from generative models. The study of repairing erroneous data has gained significant importance. Existing data repair algorithms differ in information utilization, problem settings, and are tested in limited scenarios. In this paper, we initially compare and summarize these algorithms using a new guided information-based taxonomy. We then systematically conduct a comprehensive evaluation of 12 mainstream data repair algorithms under the settings of various data error rates, error types, and downstream analysis tasks, assessing their error reduction performance with a novel metric. Also, we develop an effective and unified repair optimization strategy that substantially benefits the state of the arts, as empirically confirmed. We demonstrate that, the pure clean data may not necessarily yield the best performance in data analysis tasks and data is always worth repairing regardless of error rate. Based on the found observations and insights, we provide some practical guidelines for 5 scenarios and 2 main data analysis tasks. We anticipate this paper enabling researchers and users to well understand and deploy data repair algorithms in practice. Finally, we outline research challenges and promising future directions in the data repair field.
On the Equivalence of Graph Convolution and Mixup

This paper investigates the relationship between graph convolution and Mixup techniques. Graph convolution in a graph neural network involves aggregating features from neighboring samples to learn representative features for a specific node or sample. On the other hand, Mixup is a data augmentation technique that generates new examples by averaging features and one-hot labels from multiple samples. One commonality between these techniques is their utilization of information from multiple samples to derive feature representation. This study aims to explore whether a connection exists between these two approaches. Our investigation reveals that, under two mild conditions, graph convolution can be viewed as a specialized form of Mixup that is applied during both the training and testing phases. The two conditions are: 1) \textit{Homophily Relabel} - assigning the target node's label to all its neighbors, and 2) \textit{Test-Time Mixup} - Mixup the feature during the test time. We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup. We also empirically verify the equivalence by training an MLP using the two conditions to achieve comparable performance.