Miracle-dz/UniFAR-FTUs huggingface dataset:
Determined Multi-Label Learning via Similarity-Based Prompt

In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each training instance is time- and labor-consuming for real-world applications. To alleviate this problem, a novel labeling setting termed \textit{Determined Multi-Label Learning} (DMLL) is proposed, aiming to effectively alleviate the labeling cost inherent in multi-label tasks. In this novel labeling setting, each training instance is associated with a \textit{determined label} (either"Yes"or"No"), which indicates whether the training instance contains the provided class label. The provided class label is randomly and uniformly selected from the whole candidate labels set. Besides, each training instance only need to be determined once, which significantly reduce the annotation cost of the labeling task for multi-label datasets. In this paper, we theoretically derive an risk-consistent estimator to learn a multi-label classifier from these determined-labeled training data. Additionally, we introduce a similarity-based prompt learning method for the first time, which minimizes the risk-consistent loss of large-scale pre-trained models to learn a supplemental prompt with richer semantic information. Extensive experimental validation underscores the efficacy of our approach, demonstrating superior performance compared to existing state-of-the-art methods.
DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking

Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations, operational inefficiencies, and process unreliability. This paper introduces a Dynamic Bin-Picking Framework (DBPF) that challenges traditional static assumptions. The DBPF endows the robot with the reactivity to pick multiple moving arbitrary objects while avoiding dynamic obstacles, such as the moving bin. Combined with scene-level pose generation, the proposed pose selection metric leverages the Tendency-Aware Manipulability Network optimizing suction pose determination. Heuristic task-specific designs like velocity-matching, dynamic obstacle avoidance, and the resight policy, enhance the picking success rate and reliability. Empirical experiments demonstrate the importance of these components. Our method achieves an average 84% success rate, surpassing the 60% of the most comparable baseline, crucially, with zero collisions. Further evaluations under diverse dynamic scenarios showcase DBPF's robust performance in dynamic bin-picking. Results suggest that our framework offers a promising solution for efficient and reliable robotic bin-picking under dynamics.
Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning

Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learning methods which have up to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude faster than the state-of-the-art deep learning for planning models. Our novel approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the $h^{\text{FF}}$ heuristic in a fair competition setting. It also outperforms or ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on plan quality. WL-GOOSE is the first learning for planning model which achieves these feats. Furthermore, we study the connections between our novel WL feature generation method, previous theoretically flavoured learning architectures, and Description Logic Features for planning.
Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction

Information Extraction refers to a collection of tasks within Natural Language Processing (NLP) that identifies sub-sequences within text and their labels. These tasks have been used for many years to link extract relevant information and to link free text to structured data. However, the heterogeneity among information extraction tasks impedes progress in this area. We therefore offer a unifying perspective centered on what we define to be spans in text. We then re-orient these seemingly incongruous tasks into this unified perspective and then re-present the wide assortment of information extraction tasks as variants of the same basic Span-Oriented Information Extraction task.
Towards Online Real-Time Memory-based Video Inpainting Transformers

Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers. Although these models show promising reconstruction quality and temporal consistency, they are still unsuitable for live videos, one of the last steps to make them completely convincing and usable. The main limitations are that these state-of-the-art models inpaint using the whole video (offline processing) and show an insufficient frame rate. In our approach, we propose a framework to adapt existing inpainting transformers to these constraints by memorizing and refining redundant computations while maintaining a decent inpainting quality. Using this framework with some of the most recent inpainting models, we show great online results with a consistent throughput above 20 frames per second. The code and pretrained models will be made available upon acceptance.
Anderson Acceleration Without Restart: A Novel Method with $n$-Step Super Quadratic Convergence Rate

In this paper, we propose a novel Anderson's acceleration method to solve nonlinear equations, which does \emph{not} require a restart strategy to achieve numerical stability. We propose the greedy and random versions of our algorithm. Specifically, the greedy version selects the direction to maximize a certain measure of progress for approximating the current Jacobian matrix. In contrast, the random version chooses the random Gaussian vector as the direction to update the approximate Jacobian. Furthermore, our algorithm, including both greedy and random versions, has an $n$-step super quadratic convergence rate, where $n$ is the dimension of the objective problem. For example, the explicit convergence rate of the random version can be presented as $ \norm{\vx_{k+n+1} - \vx_*} / \norm{\vx_k- \vx_*}^2 = \cO\left(\left(1-\frac{1}{n}\right)^{kn}\right)$ for any $k\geq 0$ where $\vx_*$ is the optimum of the objective problem. This kind of convergence rate is new to Anderson's acceleration and quasi-Newton methods. The experiments also validate the fast convergence rate of our algorithm.
Investigating the impact of design debt on software quality

Technical debt is a metaphor describing situations where developers accept sacrifices in one dimension of development (e.g. software quality) in order to optimize another dimension (e.g. implementing necessary features before a deadline). Approaches, such as code smell detection, have been developed to identify particular kinds of debt, e.g. design debt. What has not yet been understood is the impact design debt has on the quality of a software product. Answering this question is important for understanding how growing debt affects a software product and how it slows down development, e.g. though introducing rework such as fixing bugs. In this case study we investigate how design debt, in the form of god classes, affects the maintainability and correctness of software products by studying two sample applications of a small-size software development company. The results show that god classes are changed more often and contain more defects than non-god classes. This result complements findings of earlier research and suggests that technical debt has a negative impact on software quality, and should therefore be identified and managed closely in the development process.
A note on generalization bounds for losses with finite moments

This paper studies the truncation method from Alquier [1] to derive high-probability PAC-Bayes bounds for unbounded losses with heavy tails. Assuming that the $p$-th moment is bounded, the resulting bounds interpolate between a slow rate $1 / \sqrt{n}$ when $p=2$, and a fast rate $1 / n$ when $p \to \infty$ and the loss is essentially bounded. Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance. This bound has an exponentially better dependence on the confidence parameter and the dependency measure than previous bounds in the literature. Finally, the paper extends all results to guarantees in expectation and single-draw PAC-Bayes. In order to so, it obtains analogues of the PAC-Bayes fast rate bound for bounded losses from [2] in these settings.
$\textit{LinkPrompt}$: Natural and Universal Adversarial Attacks on Prompt-based Language Models

Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks. Instead of using a fixed prompt template to fine-tune the model, some research demonstrates the effectiveness of searching for the prompt via optimization. Such prompt optimization process of prompt-based learning on PLMs also gives insight into generating adversarial prompts to mislead the model, raising concerns about the adversarial vulnerability of this paradigm. Recent studies have shown that universal adversarial triggers (UATs) can be generated to alter not only the predictions of the target PLMs but also the prediction of corresponding Prompt-based Fine-tuning Models (PFMs) under the prompt-based learning paradigm. However, UATs found in previous works are often unreadable tokens or characters and can be easily distinguished from natural texts with adaptive defenses. In this work, we consider the naturalness of the UATs and develop $\textit{LinkPrompt}$, an adversarial attack algorithm to generate UATs by a gradient-based beam search algorithm that not only effectively attacks the target PLMs and PFMs but also maintains the naturalness among the trigger tokens. Extensive results demonstrate the effectiveness of $\textit{LinkPrompt}$, as well as the transferability of UATs generated by $\textit{LinkPrompt}$ to open-sourced Large Language Model (LLM) Llama2 and API-accessed LLM GPT-3.5-turbo.
Comparative genomics and evolution of alternative splicing: the pessimists' science.

Group of Bioinformatics, Vavilov Institute of General Genetics, RAS, Gubkina 3, Moscow 119991, Russia, Institute of Bioinformatics, GSF-National Research Center for Environment and Health, Ingostaedter Landstr.1, Neuherberg 85764, Germany, Institute for Information Transmission Problems, RAS, Bolshoi Karetny pereulok 19, Moscow 127994, Russia, and Faculty of Bioengineering and Bioinformatics, Moscow State University, Vorobievy Gory 1-73, Moscow 119992, Russia
The combined effects of reverberation and noise on speech intelligibility by cochlear implant listeners

Objective: The purpose of this study is to assess the individual effect of reverberation and noise, as well as their combined effect, on speech intelligibility by cochlear implant (CI) users. Design: Sentence stimuli corrupted by reverberation, noise, and reverberation + noise are presented to 11 CI listeners for word identification. They are tested in two reverberation conditions (T60 = 0.6 s, 0.8 s), two noise conditions (SNR = 5 dB, 10 dB), and four reverberation + noise conditions. Study sample: Eleven CI users participated. Results: Results indicated that reverberation degrades speech intelligibility to a greater extent than additive noise (speech-shaped noise), at least for the SNR levels tested. The combined effects were greater than those introduced by either reverberation or noise alone. Conclusions: The effect of reverberation on speech intelligibility by CI users was found to be larger than that by noise. The results from the present study highlight the importance of testing CI users in reverberant conditions, since testing in noise-alone conditions might underestimate the difficulties they experience in their daily lives where reverberation and noise often coexist.
Inhibition of the Soluble Epoxide Hydrolase Promotes Albuminuria in Mice with Progressive Renal Disease

Epoxyeicotrienoic acids (EETs) are cytochrome P450-dependent anti-hypertensive and anti-inflammatory derivatives of arachidonic acid, which are highly abundant in the kidney and considered reno-protective. EETs are degraded by the enzyme soluble epoxide hydrolase (sEH) and sEH inhibitors are considered treatment for chronic renal failure (CRF). We determined whether sEH inhibition attenuates the progression of CRF in the 5/6-nephrectomy model (5/6-Nx) in mice. 5/6-Nx mice were treated with a placebo, an ACE-inhibitor (Ramipril, 40 mg/kg), the sEH-inhibitor cAUCB or the CYP-inhibitor fenbendazole for 8 weeks. 5/6-Nx induced hypertension, albuminuria, glomerulosclerosis and tubulo-interstitial damage and these effects were attenuated by Ramipril. In contrast, cAUCB failed to lower the blood pressure and albuminuria was more severe as compared to placebo. Plasma EET-levels were doubled in 5/6 Nx-mice as compared to sham mice receiving placebo. Renal sEH expression was attenuated in 5/6-Nx mice but cAUCB in these animals still further increased the EET-level. cAUCB also increased 5-HETE and 15-HETE, which derive from peroxidation or lipoxygenases. Similar to cAUCB, CYP450 inhibition increased HETEs and promoted albuminuria. Thus, sEH-inhibition failed to elicit protective effects in the 5/6-Nx model and showed a tendency to aggravate the disease. These effects might be consequence of a shift of arachidonic acid metabolism into the lipoxygenase pathway.
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models

Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as patents, papers, and theses, without having domain-specific knowledge. At the same time, ecological experts are searching for a variety of means to preserve biodiversity. To contribute to these efforts, we focused on endangered species and through in-context learning, we distilled knowledge from GPT-4. In effect, we created datasets for both named entity recognition (NER) and relation extraction (RE) via a two-stage process: 1) we generated synthetic data from GPT-4 of four classes of endangered species, 2) humans verified the factual accuracy of the synthetic data, resulting in gold data. Eventually, our novel dataset contains a total of 3.6K sentences, evenly divided between 1.8K NER and 1.8K RE sentences. The constructed dataset was then used to fine-tune both general BERT and domain-specific BERT variants, completing the knowledge distillation process from GPT-4 to BERT, because GPT-4 is resource intensive. Experiments show that our knowledge transfer approach is effective at creating a NER model suitable for detecting endangered species from texts.
Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control

Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehicles remain critical challenges, particularly in the dynamic aquatic environment. In this work, we propose a novel control scheme for creating multi-agent distributed formation control with limited communication between individual agents. In addition, the formation of the multi-agent can be reconfigured in real-time and the network connectivity can be maintained. The proposed use case for this scheme includes creating underwater mobile communication networks that can adapt to environmental or network conditions to maintain the quality of communication links for long-range exploration, seabed monitoring, or underwater infrastructure inspection. This work introduces a novel Distributed Nonlinear Model Predictive Control (DNMPC) strategy, integrating Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) with a relaxed decay rate, specifically tailored for 6-DOF underwater robotics. The effectiveness of our proposed DNMPC scheme was demonstrated through rigorous MATLAB simulations for trajectory tracking and formation reconfiguration in a dynamic environment. Our findings, supported by tests conducted using Software In The Loop (SITL) simulation, confirm the approach's applicability in real-time scenarios.
Hybridization of fuzzy GBML approaches for pattern classification problems

We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we examine the search ability of each approach to efficiently find fuzzy rule-based systems with high classification accuracy. It is clearly demonstrated that each approach has its own advantages and disadvantages. Next, we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is handled as an individual. Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set. Then, we compare our hybrid algorithm with the Michigan and Pittsburgh approaches. Experimental results show that our hybrid algorithm has higher search ability. The necessity of a heuristic specification method of antecedent fuzzy sets is also demonstrated by computational experiments on high-dimensional problems. Finally, we examine the generalization ability of fuzzy rule-based classification systems designed by our hybrid algorithm.
Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People

"Scene description"applications that describe visual content in a photo are useful daily tools for blind and low vision (BLV) people. Researchers have studied their use, but they have only explored those that leverage remote sighted assistants; little is known about applications that use AI to generate their descriptions. Thus, to investigate their use cases, we conducted a two-week diary study where 16 BLV participants used an AI-powered scene description application we designed. Through their diary entries and follow-up interviews, users shared their information goals and assessments of the visual descriptions they received. We analyzed the entries and found frequent use cases, such as identifying visual features of known objects, and surprising ones, such as avoiding contact with dangerous objects. We also found users scored the descriptions relatively low on average, 2.76 out of 5 (SD=1.49) for satisfaction and 2.43 out of 4 (SD=1.16) for trust, showing that descriptions still need significant improvements to deliver satisfying and trustworthy experiences. We discuss future opportunities for AI as it becomes a more powerful accessibility tool for BLV users.
Acoustic and language modeling of human and nonhuman noises for human-to-human spontaneous speech recognition

Several improvements of our speech-to-speech translation system JANUS on spontaneous human-to-human dialogs are presented. Common phenomena in spontaneous speech are described, followed by a classification of different types of noise. To handle the variety of spontaneous effects in human-to-human dialogs, special noise models are introduced representing both human and nonhuman noise, as well as word fragments. It is shown that both the acoustic and the language modeling of the noise increase the recognition performance significantly. In the experiments, a clustering of the noise classes is performed and the resulting cluster variants are compared, thus allowing one to determine the best tradeoff between the sensitivity and trainability of the models.
Improved Methods of Task Assignment and Resource Allocation with Preemption in Edge Computing Systems

Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. In addition, edge cloud servers must make allocation decisions with only limited information available, since the arrival of future client tasks might be impossible to predict, and the states and behavior of neighboring servers might be obscured. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. We follow a two-round bidding approach to assign tasks to edge cloud servers, and servers are allowed to preempt previous tasks to allocate more useful ones. We evaluate the performance of our system using realistic simulations and real-world trace data from a high-performance computing cluster. Results show that our heuristic improves system-wide performance by $20-25\%$ over previous work when accounting for the time taken by each approach. In this way, an ideal trade-off between performance and speed is achieved.
Immunomodulatory Role of Clarithromycin in Acinetobacter baumannii Infection via Formation of Neutrophil Extracellular Traps

ABSTRACT Macrolide antibiotics have been shown to act as immunomodulatory molecules in various immune cells. However, their effect on neutrophils has not been extensively investigated. In this study, we investigated the role of macrolide antibiotics in the generation of neutrophil extracellular traps (NETs). By assessing ex vivo and in vivo NET formation, we demonstrated that clarithromycin is able to induce NET generation both in vitro and in vivo. Clarithromycin utilizes autophagy in order to form NETs, and these NETs are decorated with antimicrobial peptide LL-37. Clarithromycin-induced NETs are able to inhibit Acinetobacter baumannii growth and biofilm formation in an LL-37-dependent manner. Additionally, LL-37 antimicrobial function depends on NET scaffold integrity. Collectively, these data expand the knowledge on the immunomodulatory role of macrolide antibiotics via the generation of LL-37-bearing NETs, which demonstrate LL-37-dependent antimicrobial activity and biofilm inhibition against A. baumannii.
UPSS: A Global, Least-Privileged Storage System with Stronger Security and Better Performance

Strong confidentiality, integrity, user control, reliability and performance are critical requirements in privacy-sensitive applications. Such applications would benefit from a data storage and sharing infrastructure that provides these properties even in decentralized topologies with untrusted storage backends, but users today are forced to choose between systemic security properties and system reliability or performance. As an alternative to this status quo we present UPSS: the user-centric private sharing system, a cryptographic storage system that can be used as a conventional filesystem or as the foundation for security-sensitive applications such as redaction with integrity and private revision control. We demonstrate that both the security and performance properties of UPSS exceed that of existing cryptographic filesystems and that its performance is comparable to mature conventional filesystems - in some cases, even superior. Whether used directly via its Rust API or as a conventional filesystem, UPSS provides strong security and practical performance on untrusted storage.