Miracle-dz/UniFAR-FTUs huggingface dataset:
Tight dynamic problem lower bounds from generalized BMM and OMv

Popular fine-grained hypotheses have been successful in proving conditional lower bounds for many dynamic problems. Two of the most widely applicable hypotheses in this context are the combinatorial Boolean Matrix Multiplication (BMM) hypothesis and the closely-related Online Matrix Vector Multiplication (OMv) hypothesis. The main theme of this paper is using k-dimensional generalizations of these two hypotheses to prove new tight conditional lower bounds for dynamic problems. The combinatorial k-Clique hypothesis, which is a standard hypothesis in the literature, naturally generalizes the combinatorial BMM hypothesis. In this paper, we prove tight lower bounds for several dynamic problems under the combinatorial k-Clique hypothesis. For instance, we show that the Dynamic Range Mode problem has no combinatorial algorithms with poly(n) pre-processing time, O(n2/3−є) update time and O(n2/3−є) query time for any є > 0, matching the known upper bounds for this problem. Previous lower bounds only ruled out algorithms with O(n1/2−є) update and query time under the OMv hypothesis. We also show that the Dynamic Subgraph Connectivity problem on undirected graphs with m edges has no combinatorial algorithms with poly(m) pre-processing time, O(m2/3−є) update time and O(m1−є) query time for є > 0, matching the upper bound given by Chan, Pătraşcu, and Roditty [SICOMP’11], and improving the previous update time lower bound (based on OMv) with exponent 1/2. Other examples include tight combinatorial lower bounds for Dynamic 2D Orthogonal Range Color Counting, Dynamic 2-Pattern Document Retrieval, and Dynamic Range Mode in higher dimensions. Furthermore, we propose the OuMvk hypothesis as a natural generalization of the OMv hypothesis. Under this hypothesis, we prove tight lower bounds for various dynamic problems. For instance, we show that the Dynamic Skyline Points Counting problem in (2k−1)-dimensional space has no algorithm with poly(n) pre-processing time and O(n1−1/k−є) update and query time for є > 0, even if the updates are semi-online. Other examples include tight conditional lower bounds for (semi-online) Dynamic Klee’s measure for unit cubes, and high-dimensional generalizations of Erickson’s problem and Langerman’s problem.
Margin-distancing for safe model explanation

The growing use of machine learning models in consequential settings has highlighted an important and seemingly irreconcilable tension between transparency and vulnerability to gaming. While this has sparked sizable debate in legal literature, there has been comparatively less technical study of this contention. In this work, we propose a clean-cut formulation of this tension and a way to make the tradeoff between transparency and gaming. We identify the source of gaming as being points close to the \emph{decision boundary} of the model. And we initiate an investigation on how to provide example-based explanations that are expansive and yet consistent with a version space that is sufficiently uncertain with respect to the boundary points' labels. Finally, we furnish our theoretical results with empirical investigations of this tradeoff on real-world datasets.
On the Stability of Unconditionally Positive and Linear Invariants Preserving Time Integration Schemes

Higher-order time integration methods that unconditionally preserve the positivity and linear invariants of the underlying differential equation system cannot belong to the class of general linear methods. This poses a major challenge for the stability analysis of such methods since the new iterate depends nonlinearly on the current iterate. Moreover, for linear systems, the existence of linear invariants is always associated with zero eigenvalues, so that steady states of the continuous problem become non-hyperbolic fixed points of the numerical time integration scheme. Altogether, the stability analysis of such methods requires the investigation of non-hyperbolic fixed points for general nonlinear iterations. Based on the center manifold theory for maps we present a theorem for the analysis of the stability of non-hyperbolic fixed points of time integration schemes applied to problems whose steady states form a subspace. This theorem provides sufficient conditions for both the stability of the method and the local convergence of the iterates to the steady state of the underlying initial value problem. This theorem is then used to prove the unconditional stability of the MPRK22($\alpha$)-family of modified Patankar-Runge-Kutta schemes when applied to arbitrary positive and conservative linear systems of differential equations. The theoretical results are confirmed by numerical experiments.
Asymptotic expansion of the nonlocal heat content

Let $\mathbf{X}=\{X_t\}_{t\geq 0}$ be a L\'evy process in $\mathbb{R}^d$ and $\Omega$ be an open subset of $\mathbb{R}^d$ with finite Lebesgue measure. In this article we consider the quantity $H(t)=\int_{\Omega} \mathbb{P}^x (X_t\in\Omega^c) \, \mathrm{d}x$ which is called the heat content. We study its asymptotic expansion for isotropic $\alpha$-stable L\'evy processes and more general L\'evy processes, under mild assumptions on the characteristic exponent.
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problem by penalizing the Q-values of OOD actions or constraining the trained policy to be close to the behavior policy. Nevertheless, such methods typically prevent the generalization of value functions beyond the offline data and also lack precise characterization of OOD data. In this paper, we propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints. Specifically, PBRL conducts uncertainty quantification via the disagreement of bootstrapped Q-functions, and performs pessimistic updates by penalizing the value function based on the estimated uncertainty. To tackle the extrapolating error, we further propose a novel OOD sampling method. We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL. Extensive experiments on D4RL benchmark show that PBRL has better performance compared to the state-of-the-art algorithms.
FastRPB: a Scalable Relative Positional Encoding for Long Sequence Tasks

Transformers achieve remarkable performance in various domains, including NLP, CV, audio processing, and graph analysis. However, they do not scale well on long sequence tasks due to their quadratic complexity w.r. t. the inputs length. Linear Transformers were proposed to address this limitation. However, these models have shown weaker performance on the long sequence tasks comparing to the original one. In this paper, we explore Linear Transformer models, rethinking their two core components. Firstly, we improved Linear Transformer with Shift-Invariant Kernel Function SIKF, which achieve higher accuracy without loss in speed. Secondly, we introduce FastRPB which stands for Fast Relative Positional Bias, which efficiently adds positional information to self-attention using Fast Fourier Transformation. FastRPB is independent of the self-attention mechanism and can be combined with an original self-attention and all its efficient variants. FastRPB has O(N log(N)) computational complexity, requiring O(N) memory w.r. t. input sequence length N.
ML-Aided Collision Recovery for UHF-RFID Systems

We propose a collision recovery algorithm with the aid of machine learning (ML-aided) for passive Ultra High Fre-quency (UHF) Radio Frequency Identification (RFID) systems. The proposed method aims at recovering the tags under collision to improve the system performance. We first estimate the number of tags from the collided signal by utilizing machine learning tools and show that the number of colliding tags can be estimated with high accuracy. Second, we employ a simple yet effective deep learning model to find the experienced channel coefficients. The proposed method allows the reader to separate each tag's signal from the received one by applying maximum likelihood decoding. We perform simulations to illustrate that the use of deep learning is highly beneficial and demonstrate that the proposed approach boosts the throughput performance of the standard framed slotted ALOHA (FSA) protocol from 0.368 to 1.837, where the receiver is equipped with a single antenna and capable of decoding up to 4 tags.
Extending the Baseline for SMC X-1's Spin and Orbital Behavior with NuSTAR Stray Light

StrayCats, the catalog of NuSTAR stray light observations, contains data from bright X-ray sources that fall within crowded source regions. These observations offer unique additional data with which to monitor sources such as X-ray binaries that show variable timing behavior. In this work, we present a timing analysis of stray light data of the high-mass X-ray binary SMC X-1, the first scientific analysis of a single source from the StrayCats project. We describe the process of screening stray light data for scientific analysis, verify the orbital ephemeris, and create both time- and energy-resolved pulse profiles. We find that the orbital ephemeris of SMC X-1 is unchanged and confirm a long-term spin-up rate of ν̇=(2.52±0.03)×10−11 Hz s−1. We also note that the shape of SMC X-1's pulse profile, while remaining double peaked, varies significantly with time and only slightly with energy.
Learning Fast and Slow for Online Time Series Forecasting

The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting. Successful solutions require handling changes to new and recurring patterns. However, training deep neural forecaster on the fly is notoriously challenging because of their limited ability to adapt to non-stationary environments and the catastrophic forgetting of old knowledge. In this work, inspired by the Complementary Learning Systems (CLS) theory, we propose Fast and Slow learning Networks (FSNet), a holistic framework for online time-series forecasting to simultaneously deal with abrupt changing and repeating patterns. Particularly, FSNet improves the slowly-learned backbone by dynamically balancing fast adaptation to recent changes and retrieving similar old knowledge. FSNet achieves this mechanism via an interaction between two complementary components of an adapter to monitor each layer's contribution to the lost, and an associative memory to support remembering, updating, and recalling repeating events. Extensive experiments on real and synthetic datasets validate FSNet's efficacy and robustness to both new and recurring patterns. Our code is available at \url{https://github.com/salesforce/fsnet}.
Augmentation based unsupervised domain adaptation

The insertion of deep learning in medical image analysis had lead to the development of state-of-the art strategies in several applications such a disease classification, as well as abnormality detection and segmentation. However, even the most advanced methods require a huge and diverse amount of data to generalize. Because in realistic clinical scenarios, data acquisition and annotation is expensive, deep learning models trained on small and unrepresentative data tend to outperform when deployed in data that differs from the one used for training (e.g data from different scanners). In this work, we proposed a domain adaptation methodology to alleviate this problem in segmentation models. Our approach takes advantage of the properties of adversarial domain adaptation and consistency training to achieve more robust adaptation. Using two datasets with white matter hyperintensities (WMH) annotations, we demonstrated that the proposed method improves model generalization even in corner cases where individual strategies tend to fail.
IOTLB-SC: An Accelerator-Independent Leakage Source in Modern Cloud Systems

Hardware peripherals such as GPUs and FPGAs are commonly available in server-grade computing to accelerate specific compute tasks, from database queries to machine learning. CSPs have integrated these accelerators into their infrastructure and let tenants combine and configure these components flexibly, based on their needs. Securing I/O interfaces is critical to ensure proper isolation between tenants in these highly complex, heterogeneous, yet shared server systems, especially in the cloud, where some peripherals may be under control of a malicious tenant. In this work, we investigate the interfaces that connect peripheral hardware components to each other and the rest of the system. We show that the I/O memory management units (IOMMUs) — intended to ensure proper isolation of peripherals — are the source of a new attack surface: the I/O translation look-aside buffer (IOTLB). We show that by using an FPGA accelerator card one can gain precise information over IOTLB activity. That information can be used for covert communication between peripherals without bothering CPU or to directly extract leakage from neighboring accelerated compute jobs such as GPU-accelerated databases. We present the first qualitative and quantitative analysis of this newly uncovered attack surface before fine-grained channels become widely viable with the introduction of CXL and PCIe 5.0. In addition, we propose possible countermeasures that software developers, hardware designers, and system administrators can use to suppress the observed side-channel leakages and analyze their implicit costs.
Effects of cutting speeds and moisture content on grass chopping (pulverizing)

A study was conducted at the Malaysian Agricultural Research and Development Institute, MARDI, Serdang, Selangor, Malaysia, to investigate the effects of Vertical Mower with double sided knife at four different speeds of 2000, 2200, 2500 and 2700 rpm, with two grass moisture contents at 20.37% and 57.07% wet base (wb%) during grass chopping (pulverizing). The results obtained showed that all the treatments were significant at 99% significance level for the percentage of grass leave area and the percentage of length of stem. The best results obtained were at a mower speed of 2500 rpm and 20.37% of grass moisture content which resulted in average values of 81.03% for the percentage of the leave area and 82.08% for the percentage of the length of the stem.
Photophysical and electrochemical properties of alternating copolymers consisting of 3,4-ethylenedioxythiophene and 4,4′-diphenylamine/3,6-carbazole

ABSTRACT In the past decades, π-conjugated polymers including polyaniline (PANI), polycarbazole (PCz) and poly(3,4-ethylenedioxythiophene) (PEDOT), have roused much attention and been widely applied in high-performance organic semiconductors on account of their unique electronic, optoelectronic, electrochemical, and mechanical properties. Herein, two kinds of alternating copolymers, poly[(N-(2-ethylhexyl)carbazole-3,6-diyl)-(3,4-ethylenedioxythiophene-2,5-diyl)] (P(3,6-Cz-EDOT)) and poly[(N-(2-ethylhexyl)diphenylamine-4,4′-diyl)-(3,4-ethylenedioxythiophene-2,5-diyl)] (P(4,4′-DPA-EDOT)) were subtly designed and synthesized via direct (hetero)arene polymerization (DHAP) in good yields (up to 88% and 90%, respectively) and high molecular weights (up to 8400 and 24100 Da, respectively). The photophysical, electrochemical, thermal and morphological properties of P(3,6-Cz-EDOT) and P(4,4′-DPA-EDOT) were systematically investigated based on experimental analysis and theoretical simulations. As compared to P(4,4′-DPA-EDOT), P(3,6-Cz-EDOT) exhibited shorter π-conjugation, a higher glass transition temperature (T g), better thermal stability, more porous morphology and a preferably curved or spiral conformation. Moreover, both P(3,6-Cz-EDOT) and P(4,4′-DPA-EDOT) possessed high specific discharge capacities, indicating their potential applications in solution-processed charge-storage devices. Graphical abstract
The Lipoteichoic Acid-Related Proteins YqgS and LafA Contribute to the Resistance of Listeria monocytogenes to Nisin

Listeria monocytogenes is an opportunistic Gram-positive pathogen responsible for listeriosis, and is widely present in a variety of foods including ready-to-eat foods, meat, and dairy products. Nisin is the only licensed lantibiotic by the FDA for use as a food-grade inhibitor in over 50 countries. ABSTRACT Listeria monocytogenes is a major pathogen contributing to foodborne outbreaks with high mortality. Nisin, a natural antimicrobial, has been widely used as a food preservative. However, the mechanisms of L. monocytogenes involved in nisin resistance have not yet to be fully defined. A mariner transposon library was constructed in L. monocytogenes, leading to the identification of 99 genes associated with the innate resistance to nisin via Transposon sequencing (Tn-seq) analysis. To validate the accuracy of the Tn-seq results, we constructed five mutants (ΔyqgS, ΔlafA, ΔvirR, ΔgtcA, and Δlmo1464) in L. monocytogenes. The results revealed that yqgS and lafA, the lipoteichoic acid-related genes, were essential for resistance to nisin, while the gtcA and lmo1464 mutants showed substantially enhanced nisin resistance. Densely wrinkled, collapsed surface and membrane breakdown were shown on ΔyqgS and ΔlafA mutants under nisin treatment. Deletion of yqgS and lafA altered the surface charge, and decreased the resistance to general stress conditions and cell envelope-acting antimicrobials. Furthermore, YqgS and LafA are required for biofilm formation and cell invasion of L. monocytogenes. Collectively, these results reveal novel mechanisms of nisin resistance in L. monocytogenes and may provide unique targets for the development of food-grade inhibitors for nisin-resistant foodborne pathogens. IMPORTANCE Listeria monocytogenes is an opportunistic Gram-positive pathogen responsible for listeriosis, and is widely present in a variety of foods including ready-to-eat foods, meat, and dairy products. Nisin is the only licensed lantibiotic by the FDA for use as a food-grade inhibitor in over 50 countries. A prior study suggests that L. monocytogenes are more resistant than other Gram-positive pathogens in nisin-mediated bactericidal effects. However, the mechanisms of L. monocytogenes involved in nisin resistance have not yet to be fully defined. Here, we used a mariner transposon library to identify nisin-resistance-related genes on a genome-wide scale via transposon sequencing. We found, for the first time, that YqgS and LafA (Lipoteichoic acid-related proteins) are required for resistance to nisin. Subsequently, we investigated the roles of YqgS and LafA in L. monocytogenes stress resistance, antimicrobial resistance, biofilm formation, and virulence in mammalian cells.
Evaluation of the Levels of Peripheral CD3+, CD4+, and CD8+ T Cells and IgG and IgM Antibodies in COVID-19 Patients at Different Stages of Infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China. This virus is a serious threat to people not only in China but also worldwide, where it has been detected in over 222 countries. ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the stimulatory levels of cellular-mediated immunity, which plays an essential role in controlling SARS-CoV-2 infection. In fact, several studies have shown the association of lymphopenia with severe COVID-19 in patients. The aim of this study is to investigate the response of the immune system, including cell-mediated immunity and antibody production, during different stages of SARS-CoV-2 infection. Peripheral blood and serum samples were collected from patients with moderate infection, patients under medication (hospitalized), patients who had recovered, and healthy individuals (n = 80). Flow cytometry analysis was performed on peripheral blood samples to determine the cellular immunity profile of each patient. The data showed a significant reduction in the levels of CD3+, CD4+, and CD8+ T cells and CD45+ cells in the moderate and under-medication groups, suggesting lymphopenia in those patients. Also, enzyme-linked immunosorbent assay (ELISA) was conducted on the serum samples to measure the levels of antibodies, including IgM and IgG, in each patient. The results revealed a significant increase in the levels of IgM in the moderate infection and under-medication patients, thus indicating the production of IgM during the first week of infection. Furthermore, changes in the levels of IgG were significantly detected among recovered patients, indicating therefore a remarkable increase during the recovery stage of SARS-CoV-2 infection and thus a strong humoral-mediated immunity. In summary, the results of this study may help us to understand the main role of the cellular immune responses, including CD3+, CD4+, and CD8+ T cells, against SARS-CoV-2 infection. This understanding might support the development of SARS-CoV-2 treatments and vaccines in the near future. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 in China. This virus is a serious threat to people not only in China but also worldwide, where it has been detected in over 222 countries. It has been reported that ∼3.4% of SARS-CoV-2-infected patients have died. The significance of our study relies on the fact that an enzyme-linked immunosorbent assay and flow cytometry were used to measure the levels of antibodies and cellular immune response, respectively, from clinical samples of patients infected with SARS-CoV-2.