Miracle-dz/UniFAR-FTUs huggingface dataset:
Matrix Completion with Hypergraphs: Sharp Thresholds and Efficient Algorithms

This paper considers the problem of completing a rating matrix based on sub-sampled matrix entries as well as observed social graphs and hypergraphs. We show that there exists a \emph{sharp threshold} on the sample probability for the task of exactly completing the rating matrix -- the task is achievable when the sample probability is above the threshold, and is impossible otherwise -- demonstrating a phase transition phenomenon. The threshold can be expressed as a function of the ``quality'' of hypergraphs, enabling us to \emph{quantify} the amount of reduction in sample probability due to the exploitation of hypergraphs. This also highlights the usefulness of hypergraphs in the matrix completion problem. En route to discovering the sharp threshold, we develop a computationally efficient matrix completion algorithm that effectively exploits the observed graphs and hypergraphs. Theoretical analyses show that our algorithm succeeds with high probability as long as the sample probability exceeds the aforementioned threshold, and this theoretical result is further validated by synthetic experiments. Moreover, our experiments on a real social network dataset (with both graphs and hypergraphs) show that our algorithm outperforms other state-of-the-art matrix completion algorithms.
Tropical modeling of battery swapping and charging station

We propose and investigate a queueing model of a battery swapping and charging station (BSCS) for electric vehicles (EVs). A new approach to the analysis of the queueing model is developed, which combines the representation of the model as a stochastic dynamic system with the use of the methods and results of tropical algebra, which deals with the theory and applications of algebraic systems with idempotent operations. We describe the dynamics of the queueing model by a system of recurrence equations that involve random variables (RVs) to represent the interarrival time of incoming EVs. A performance measure for the model is defined as the mean operation cycle time of the station. Furthermore, the system of equations is represented in terms of the tropical algebra in vector form as an implicit linear state dynamic equation. The performance measure takes on the meaning of the mean growth rate of the state vector (the Lyapunov exponent) of the dynamic system. By applying a solution technique of vector equations in tropical algebra, the implicit equation is transformed into an explicit one with a state transition matrix with random entries. The evaluation of the Lyapunov exponent reduces to finding the limit of the expected value of norms of tropical matrix products. This limit is then obtained using results from the tropical spectral theory of deterministic and random matrices. With this approach, we derive a new exact formula for the mean cycle time of the BSCS, which is given in terms of the expected value of the RVs involved. We present the results of the Monte Carlo simulation of the BSCS’s operation, which show a good agreement with the exact solution. The application of the obtained solution to evaluate the performance of one BSCS and to find the optimal distribution of battery packs between stations in a network of BSCSs is discussed. The solution may be of interest in the case when the details of the underlying probability distributions are difficult to determine and, thus, serves to complement and supplement other modeling techniques with the need to fix a distribution.
Alternative Gibbs measure for fertile three-state Hard-Core models on a Cayley tree

We consider fertile three-state Hard-Core (HC) models with the activity parameter $\lambda>0$ on a Cayley tree. It is known that there exist four types of such models:"wrench"\,,"wand"\,,"hinge"\, and"pipe"\,. In cases"wand"\, and"hinge"\ on a Cayley tree of arbitrary order a complete description of translation-invariant Gibbs measures is obtained. The conception of alternative Gibbs measure is introduced and in the case"wand"\, translational invariance conditions for alternative Gibbs measures are found. Also, we show that the existence of alternative Gibbs measures which are not translation-invariant
G-Safe: Safe GPU Sharing in Multi-Tenant Environments

Modern GPU applications, such as machine learning (ML) frameworks, can only partially utilize beefy GPUs, leading to GPU underutilization in cloud environments. Sharing GPUs across multiple applications from different users can improve resource utilization and consequently cost, energy, and power efficiency. However, GPU sharing creates memory safety concerns because kernels must share a single GPU address space (GPU context). Previous GPU memory protection approaches have limited deployability because they require specialized hardware extensions or access to source code. This is often unavailable in GPU-accelerated libraries heavily utilized by ML frameworks. In this paper, we present G-Safe, a PTX-level bounds checking approach for GPUs that limits GPU kernels of each application to stay within the memory partition allocated to them. G-Safe relies on three mechanisms: (1) It divides the common GPU address space into separate partitions for different applications. (2) It intercepts and checks data transfers, fencing erroneous operations. (3) It instruments all GPU kernels at the PTX level (available in closed GPU libraries) fencing all kernel memory accesses outside application memory bounds. We implement G-Safe as an external, dynamically linked library that can be pre-loaded at application startup time. G-Safe's approach is transparent to applications and can support real-life, complex frameworks, such as Caffe and PyTorch, that issue billions of GPU kernels. Our evaluation shows that the overhead of G-Safe compared to native (unprotected) for such frameworks is between 4\% - 12\% and on average 9\%.
Numerical GR MHD simulations of the post-merger system with a composition-dependent equation of state

By means of HARM\_COOL\_EOS, which is our code for conservative relativistic magnetohydrodynamics, we developed a new scheme for the simulation of a system formed after compact binary merger. Our code works with a tabulated equation of state of dense matter, accounts for the neutrino leakage, and follows the mass outflows via the tracer particle method. We discuss the numerical scheme, and present the recovery method included in our code. We also show results of a numerical simulation, addressed to the post-merger system after the coalescence of binary neutron stars, or a neutron star with a stellar mass black hole. The plasma is very neutron-rich, so the r-process nucleosynthesis in the ejected material may lead to unstable heavy isotopes creation. They are responsible for an electromagnetic signal, observed as a kilonova. In addition, the magnetized, neutrino-driven wind can act as a collimating mechanism for the relativistic jet.
Conditional Flood Fill Method in Logic Synthesis

In the field of Electronic Design Automation (EDA), logic synthesis plays a pivotal role in optimizing hardware resources. Traditional logic synthesis algorithms, such as the Quine-McCluskey method, face challenges in scalability and efficiency, particularly for higher-dimension problems. This paper introduces a novel heuristic algorithm based on Conditional Flood Fill Method aimed at addressing these limitations. Our method employs count-based adjacent element handling and introduces nine new theorems to guide the logic synthesis process. Experimental results validate the efficacy of our approach, showing significant improvements in computational efficiency and scalability compared to existing algorithms. The algorithm holds potential for future advancements in circuit development and Boolean function optimization.
Quantum singular value transformation for an arbitrary bounded operator embedded in a unitary operator

This research extends quantum singular value transformation (QSVT) for general bounded operators embedded in unitary operators on possibly infinite-dimensional Hilbert spaces. Through in-depth mathematical exploration, we have achieved a refined operator-theoretic understanding of QSVT, leading to a more streamlined approach. One of the key discoveries is that polynomial transformations in QSVT inherently apply to the entire operator, rather than being contingent on the selection of a specific basis. We expect that this research will pave the way for applying these insights to a broader range of problems in quantum information processing and provide analytical tools for quantum dynamics, such as quantum walks.
Modeling Spoof Noise by De-spoofing Diffusion and its Application in Face Anti-spoofing

Face anti-spoofing is crucial for ensuring the security and reliability of face recognition systems. Several existing face anti-spoofing methods utilize GAN-like networks to detect presentation attacks by estimating the noise pattern of a spoof image and recovering the corresponding genuine image. But GAN’s limited face appearance space results in the denoised faces cannot cover the full data distribution of genuine faces, thereby undermining the generalization performance of such methods. In this work, we present a pioneering attempt to employ diffusion models to denoise a spoof image and restore the genuine image. The difference between these two images is considered as the spoof noise, which can serve as a discriminative cue for face anti-spoofing. We evaluate our proposed method on several intra-testing and inter-testing protocols, where the experimental results showcase the effectiveness of our method in achieving competitive performance in terms of both accuracy and generalization.
F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning

Recent studies have shown that video-level representation learning is crucial to the capture and understanding of the long-range temporal structure for video action recognition. Most existing 3D convolutional neural network (CNN)-based methods for video-level representation learning are clip-based and focus only on short-term motion and appearances. These CNN-based methods lack the capacity to incorporate and model the long-range spatiotemporal representation of the underlying video and ignore the long-range video-level context during training. In this study, we propose a factorized 4D CNN architecture with attention (F4D) that is capable of learning more effective, finer-grained, long-term spatiotemporal video representations. We demonstrate that the proposed F4D architecture yields significant performance improvements over the conventional 2D, and 3D CNN architectures proposed in the literature. Experiment evaluation on five action recognition benchmark datasets, i.e., Something-Something-v1, SomethingSomething-v2, Kinetics-400, UCF101, and HMDB51 demonstrate the effectiveness of the proposed F4D network architecture for video-level action recognition.
Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling

Deep neural network models for image segmentation can be a powerful tool for the automation of motor claims handling processes in the insurance industry. A crucial aspect is the reliability of the model outputs when facing adverse conditions, such as low quality photos taken by claimants to document damages. We explore the use of a meta-classification model to assess the precision of segments predicted by a model trained for the semantic segmentation of car body parts. Different sets of features correlated with the quality of a segment are compared, and an AUROC score of 0.915 is achieved for distinguishing between high- and low-quality segments. By removing low-quality segments, the average mIoU of the segmentation output is improved by 16 percentage points and the number of wrongly predicted segments is reduced by 77%.
PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency

Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation. Taking range measurements as input, our approach alternates between incremental learning of the local implicit signed distance field and the pose estimation given the current local map using a correspondence-free, point-to-implicit model registration. Our implicit map is based on sparse optimizable neural points, which are inherently elastic and deformable with the global pose adjustment when closing a loop. Loops are also detected using the neural point features. Extensive experiments validate that PIN-SLAM is robust to various environments and versatile to different range sensors such as LiDAR and RGB-D cameras. PIN-SLAM achieves pose estimation accuracy better or on par with the state-of-the-art LiDAR odometry or SLAM systems and outperforms the recent neural implicit SLAM approaches while maintaining a more consistent, and highly compact implicit map that can be reconstructed as accurate and complete meshes. Finally, thanks to the voxel hashing for efficient neural points indexing and the fast implicit map-based registration without closest point association, PIN-SLAM can run at the sensor frame rate on a moderate GPU. Codes will be available at: https://github.com/PRBonn/PIN_SLAM.
Remote sensing of a levitated superconductor with a flux-tunable microwave cavity

We present a cavity-electromechanical system comprising a superconducting quantum interference device which is embedded in a microwave resonator and coupled via a pick-up loop to a 6 $\mu$g magnetically-levitated superconducting sphere. The motion of the sphere in the magnetic trap induces a frequency shift in the SQUID-cavity system. We use microwave spectroscopy to characterize the system, and we demonstrate that the electromechanical interaction is tunable. The measured displacement sensitivity of $10^{-7} \, \mathrm{m} / \sqrt{\mathrm{Hz}}$, defines a path towards ground-state cooling of levitated particles with Planck-scale masses at millikelvin environment temperatures.
Generative Multi-Modal Knowledge Retrieval with Large Language Models

Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data. We retrieve knowledge via a two-step process: 1) generating knowledge clues related to the queries, and 2) obtaining the relevant document by searching databases using the knowledge clue. In particular, we first introduce an object-aware prefix-tuning technique to guide multi-grained visual learning. Then, we align multi-grained visual features into the textual feature space of the LLM, employing the LLM to capture cross-modal interactions. Subsequently, we construct instruction data with a unified format for model training. Finally, we propose the knowledge-guided generation strategy to impose prior constraints in the decoding steps, thereby promoting the generation of distinctive knowledge clues. Through experiments conducted on three benchmarks, we demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
SARRIGUREN: a polynomial-time complete algorithm for random k-SAT with relatively dense clauses

SARRIGUREN, a new complete algorithm for SAT based on counting clauses (which is valid also for Unique-SAT and #SAT) is described, analyzed and tested. Although existing complete algorithms for SAT perform slower with clauses with many literals, that is an advantage for SARRIGUREN, because the more literals are in the clauses the bigger is the probability of overlapping among clauses, a property that makes the clause counting process more efficient. Actually, it provides a $O(m^2 \times n/k)$ time complexity for random $k$-SAT instances of $n$ variables and $m$ relatively dense clauses, where that density level is relative to the number of variables $n$, that is, clauses are relatively dense when $k\geq7\sqrt{n}$. Although theoretically there could be worst-cases with exponential complexity, the probability of those cases to happen in random $k$-SAT with relatively dense clauses is practically zero. The algorithm has been empirically tested and that polynomial time complexity maintains also for $k$-SAT instances with less dense clauses ($k\geq5\sqrt{n}$). That density could, for example, be of only 0.049 working with $n=20000$ variables and $k=989$ literals. In addition, they are presented two more complementary algorithms that provide the solutions to $k$-SAT instances and valuable information about number of solutions for each literal. Although this algorithm does not solve the NP=P problem (it is not a polynomial algorithm for 3-SAT), it broads the knowledge about that subject, because $k$-SAT with $k>3$ and dense clauses is not harder than 3-SAT. Moreover, the Python implementation of the algorithms, and all the input datasets and obtained results in the experiments are made available.
Binaural Angular Separation Network

We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones. The model is trained with simulated room impulse responses (RIRs) using omni-directional microphones without needing to collect real RIRs. By relying on specific angular regions and multiple room simulations, the model utilizes consistent time difference of arrival (TDOA) cues, or what we call delay contrast, to separate target and interference sources while remaining robust in various reverberation environments. We demonstrate the model is not only generalizable to a commercially available device with a slightly different microphone geometry, but also outperforms our previous work which uses one additional microphone on the same device. The model runs in real-time on-device and is suitable for low-latency streaming applications such as telephony and video conferencing.
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests. To enable more human-centric modeling of user preferences, this work proposes a novel explainable recommendation framework, i.e., LLMHG, synergizing the reasoning capabilities of large language models (LLMs) and the structural advantages of hypergraph neural networks. By effectively profiling and interpreting the nuances of individual user interests, our framework pioneers enhancements to recommendation systems with increased explainability. We validate that explicitly accounting for the intricacies of human preferences allows our human-centric and explainable LLMHG approach to consistently outperform conventional models across diverse real-world datasets. The proposed plug-and-play enhancement framework delivers immediate gains in recommendation performance while offering a pathway to apply advanced LLMs for better capturing the complexity of human interests across machine learning applications.
Moral Values Underpinning COVID-19 Online Communication Patterns

The COVID-19 pandemic has triggered profound societal changes, extending beyond its health impacts to the moralization of behaviors. Leveraging insights from moral psychology, this study delves into the moral fabric shaping online discussions surrounding COVID-19 over a span of nearly two years. Our investigation identifies four distinct user groups characterized by differences in morality, political ideology, and communication styles. We underscore the intricate relationship between moral differences and political ideologies, revealing a nuanced picture where moral orientations do not rigidly separate users politically. Furthermore, we uncover patterns of moral homophily within the social network, highlighting the existence of one potential moral echo chamber. Analyzing the moral themes embedded in messages, we observe that messages featuring moral foundations not typically favored by their authors, as well as those incorporating multiple moral foundations, resonate more effectively with out-group members. This research contributes valuable insights into the complex interplay between moral foundations, communication dynamics, and network structures on Twitter.
Determinants of Household Drinking Water Quality in Rural Ethiopia

Safe and adequate water supply is a vital element to preserve human health; however, access to clean water is limited in many developing countries. Furthermore, improved water sources are often contaminated with fecal matters and consumption of unsafe water poses a great public health risk. This study seeks to identify determinants of microbial contamination of household drinking water under multiple-use water systems in rural areas of Fogera and Mecha districts of Ethiopia. In this analysis, a random sample of 454 households was surveyed from February to March 2014, and water samples from community sources and storage containers were collected and tested for fecal contamination. The number of Escherichia coli (E.coli) colony forming units per 100ml (cfu/100ml) water was used as an indicator of fecal contamination. The results show that 50% of households used protected water sources, 38% used unprotected wells/ spring and 12% used surface water sources. However, water microbiological tests demonstrated that 58% of household storage water samples and 74% of water sources were contaminated with E.coli. After controlling for household sanitary factors, high level of E.coli bacteria colonies were observed in unprotected water compared to surface water and protected wells/springs sources. To ensure the quality and safety of water stored in the household, our findings suggest that point-of-use water treatment, safe water handling and storage, proper hygiene practices such as washing hands after critical times and proper disposal of household garbage should be promoted. On-site water wells should be properly designed to prevent seepage from unhygienic household pit latrine. Furthermore, community water sources should be adequately protected and sanitary measures should be undertaken regularly to reduce contamination from human and animal waste.