Miracle-dz/UniFAR-FTUs huggingface dataset:
NBMOD: Find It and Grasp It in Noisy Background

Grasping objects is a fundamental yet important capability of robots, and many tasks such as sorting and picking rely on this skill. The prerequisite for stable grasping is the ability to correctly identify suitable grasping positions. However, finding appropriate grasping points is challenging due to the diverse shapes, varying density distributions, and significant differences between the barycenter of various objects. In the past few years, researchers have proposed many methods to address the above-mentioned issues and achieved very good results on publicly available datasets such as the Cornell dataset and the Jacquard dataset. The problem is that the backgrounds of Cornell and Jacquard datasets are relatively simple - typically just a whiteboard, while in real-world operational environments, the background could be complex and noisy. Moreover, in real-world scenarios, robots usually only need to grasp fixed types of objects. To address the aforementioned issues, we proposed a large-scale grasp detection dataset called NBMOD: Noisy Background Multi-Object Dataset for grasp detection, which consists of 31,500 RGB-D images of 20 different types of fruits. Accurate prediction of angles has always been a challenging problem in the detection task of oriented bounding boxes. This paper presents a Rotation Anchor Mechanism (RAM) to address this issue. Considering the high real-time requirement of robotic systems, we propose a series of lightweight architectures called RA-GraspNet (GraspNet with Rotation Anchor): RARA (network with Rotation Anchor and Region Attention), RAST (network with Rotation Anchor and Semi Transformer), and RAGT (network with Rotation Anchor and Global Transformer) to tackle this problem. Among them, the RAGT-3/3 model achieves an accuracy of 99% on the NBMOD dataset. The NBMOD and our code are available at https://github.com/kmittle/Grasp-Detection-NBMOD.
LM-VC: Zero-Shot Voice Conversion via Speech Generation Based on Language Models

Language model (LM) based audio generation frameworks, e.g., AudioLM, have recently achieved new state-of-the-art performance in zero-shot audio generation. In this paper, we explore the feasibility of LMs for zero-shot voice conversion. An intuitive approach is to follow AudioLM – Tokenizing speech into semantic and acoustic tokens respectively by HuBERT and SoundStream, and converting source semantic tokens to target acoustic tokens conditioned on acoustic tokens of the target speaker. However, such an approach encounters several issues: 1) the linguistic content contained in semantic tokens may get dispersed during multi-layer modeling while the lengthy speech input in the voice conversion task makes contextual learning even harder; 2) the semantic tokens still contain speaker-related information, which may be leaked to the target speech, lowering the target speaker similarity; 3) the generation diversity in the sampling of the LM can lead to unexpected outcomes during inference, leading to unnatural pronunciation and speech quality degradation. To mitigate these problems, we propose LM-VC, a two-stage language modeling approach that generates coarse acoustic tokens for recovering the source linguistic content and target speaker's timbre, and then reconstructs the fine for acoustic details as converted speech. Specifically, to enhance content preservation and facilitates better disentanglement, a masked prefix LM with a mask prediction strategy is used for coarse acoustic modeling. This model is encouraged to recover the masked content from the surrounding context and generate target speech based on the target speaker's utterance and corrupted semantic tokens. Besides, to further alleviate the sampling error in the generation, an external LM, which employs window attention to capture the local acoustic relations, is introduced to participate in the coarse acoustic modeling through shallow fusion. Finally, a prefix LM reconstructs fine acoustic tokens from the coarse and results in the converted speech. Experiments demonstrate that LM-VC outperforms competitive systems in speech naturalness and speaker similarity.
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex system simulation and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems.
Lessons Learned from a Performance Analysis and Optimization of a Multiscale Cellular Simulation

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a locality-aware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field.
Plausibility-Based Heuristics for Latent Space Classical Planning

Recent work on LatPlan has shown that it is possible to learn models for domain-independent classical planners from unlabeled image data. Although PDDL models acquired by LatPlan can be solved using standard PDDL planners, the resulting latent-space plan may be invalid with respect to the underlying, ground-truth domain (e.g., the latent-space plan may include hallucinatory/invalid states). We propose Plausibility-Based Heuristics, which are domain-independent plausibility metrics which can be computed for each state evaluated during search and uses as a heuristic function for best-first search. We show that PBH significantly increases the number of valid found plans on image-based tile puzzle and Towers of Hanoi domains.
The Detection of Possible γ-Ray Quasi-periodic Modulation with ∼600 days from the Blazar S2 0109+22

In this work, we analyzed the long-term γ-ray data by a Fermi Large Area Telescope of blazar S2 0109+22, ranging from 2008 to 2023. The quasi-periodic oscillations (QPOs) of blazars aided in investigating the physical properties of internal supermassive black holes, the nature of variability, and the underlying radiation mechanism. We employed four different methods—Weighted Wavelet Z-transform, Lomb–Scargle periodogram, REDFIT and phase folded light curve analysis, for searching QPO signals. Our analysis identified a possible QPO behavior with a periodicity of ∼600 days in 2013 November–2023 January at a significance level of ∼3. 5σ. This QPO signal sustained ∼9 yr, corresponding to 5.6 cycles, which was in good agreement with the previously observed periodicity of ∼657 days in radio. We explained this phenomenon based on the accretion model and the lighthouse effect, in a binary black hole system.
Quantum Superposition and Entanglement in Spin-Glass Systems

We propose that spin glasses can exist in equally probable superposition states (SSs) comprising potential configurations. Employing the Edward-Anderson (EA) order parameter and magnetization, we establish a classification scheme for these SSs based on their contribution to discerning magnetic order (disorder), such as SG, ferromagnetic (FM), and paramagnetic (PM) phases. We also encompass various system sizes and investigate the entanglement properties of these phase-dependent SSs using the negativity measure. Our analysis reveals that the SG order parameter can be employed to determine the entanglement characteristics of magnetically ordered (disordered) phases, and vice versa, with negativity indicating the presence of magnetic order. Specifically, we examine the relationship between negativity and susceptibility in spin-glass systems. Our findings provide further insight into the role of quantum superposition in spin glasses and quantum magnets.
Composition of nested embeddings with an application to outlier removal

We study the design of embeddings into Euclidean space with outliers. Given a metric space $(X,d)$ and an integer $k$, the goal is to embed all but $k$ points in $X$ (called the"outliers") into $\ell_2$ with the smallest possible distortion $c$. Finding the optimal distortion $c$ for a given outlier set size $k$, or alternately the smallest $k$ for a given target distortion $c$ are both NP-hard problems. In fact, it is UGC-hard to approximate $k$ to within a factor smaller than $2$ even when the metric sans outliers is isometrically embeddable into $\ell_2$. We consider bi-criteria approximations. Our main result is a polynomial time algorithm that approximates the outlier set size to within an $O(\log^4 k)$ factor and the distortion to within a constant factor. The main technical component in our result is an approach for constructing a composition of two given embeddings from subsets of $X$ into $\ell_2$ which inherits the distortions of each to within small multiplicative factors. Specifically, given a low $c_S$ distortion embedding from $S\subset X$ into $\ell_2$ and a high(er) $c_X$ distortion embedding from the entire set $X$ into $\ell_2$, we construct a single embedding that achieves the same distortion $c_S$ over pairs of points in $S$ and an expansion of at most $O(\log k)\cdot c_X$ over the remaining pairs of points, where $k=|X\setminus S|$. Our composition theorem extends to embeddings into arbitrary $\ell_p$ metrics for $p\ge 1$, and may be of independent interest. While unions of embeddings over disjoint sets have been studied previously, to our knowledge, this is the first work to consider compositions of nested embeddings.
GPU-Accelerated Verification of Machine Learning Models for Power Systems

Computational tools for rigorously verifying the performance of large-scale machine learning (ML) models have progressed significantly in recent years. The most successful solvers employ highly specialized, GPU-accelerated branch and bound routines. Such tools are crucial for the successful deployment of machine learning applications in safety-critical systems, such as power systems. Despite their successes, however, barriers prevent out-of-the-box application of these routines to power system problems. This paper addresses this issue in two key ways. First, for the first time to our knowledge, we enable the simultaneous verification of multiple verification problems (e.g., checking for the violation of all line flow constraints simultaneously and not by solving individual verification problems). For that, we introduce an exact transformation that converts the"worst-case"violation across a set of potential violations to a series of ReLU-based layers that augment the original neural network. This allows verifiers to interpret them directly. Second, power system ML models often must be verified to satisfy power flow constraints. We propose a dualization procedure which encodes linear equality and inequality constraints directly into the verification problem; and in a manner which is mathematically consistent with the specialized verification tools. To demonstrate these innovations, we verify problems associated with data-driven security constrained DC-OPF solvers. We build and test our first set of innovations using the $\alpha,\beta$-CROWN solver, and we benchmark against Gurobi 10.0. Our contributions achieve a speedup that can exceed 100x and allow higher degrees of verification flexibility.
Clustered Federated Learning via Generalized Total Variation Minimization

We study optimization methods to train local (or personalized) models for decentralized collections of local datasets with an intrinsic network structure. This network structure arises from domain-specific notions of similarity between local datasets. Examples of such notions include spatio-temporal proximity, statistical dependencies or functional relations. Our main conceptual contribution is to formulate federated learning as generalized total variation (GTV) minimization. This formulation unifies and considerably extends existing federated learning methods. It is highly flexible and can be combined with a broad range of parametric models, including generalized linear models or deep neural networks. Our main algorithmic contribution is a fully decentralized federated learning algorithm. This algorithm is obtained by applying an established primal-dual method to solve GTV minimization. It can be implemented as message passing and is robust against inexact computations that arise from limited computational resources, including processing time or bandwidth. Our main analytic contribution is an upper bound on the deviation between the local model parameters learnt by our algorithm and an oracle-based clustered federated learning method. This upper bound reveals conditions on the local models and the network structure of local datasets such that GTV minimization is able to pool (nearly) homogeneous local datasets.
New Characterizations and Efficient Local Search for General Integer Linear Programming

Integer linear programming (ILP) models a wide range of practical combinatorial optimization problems and significantly impacts industry and management sectors. This work proposes new characterizations of ILP with the concept of boundary solutions. Motivated by the new characterizations, we develop a new local search algorithm Local-ILP, which is efficient for solving general ILP validated on a large heterogeneous problem dataset. We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes. Two new operators are proposed, namely the tight move and the lift move operators, which are associated with appropriate scoring functions. Different modes apply different operators to realize different search strategies and the algorithm switches between three modes according to the current search state. Putting these together, we develop a local search ILP solver called Local-ILP. Experiments conducted on the MIPLIB dataset show the effectiveness of our algorithm in solving large-scale hard ILP problems. In the aspect of finding a good feasible solution quickly, Local-ILP is competitive and complementary to the state-of-the-art commercial solver Gurobi and significantly outperforms the state-of-the-art non-commercial solver SCIP. Moreover, our algorithm establishes new records for 6 MIPLIB open instances. The theoretical analysis of our algorithm is also presented, which shows our algorithm could avoid visiting unnecessary regions.
Reliability Analysis of a Network Server System with DoS Attacks

Abstract As the Internet has been greatly developed, the demand for improvement of the reliability of the Internet has increased. Recently, there exists a problem in the illegal access which attacks a server intentionally. In particular, DoS (Denial of Service) attack which sends a huge number of packets has been a serious problem. In order to cope with this problem, IDS (Intrusion Detection System) has been widely used. This paper formulates a stochastic model for a server system with DoS attacks. A server has the function of IDS. The mean time and the expected monitoring number until a server system becomes faulty are derived. Further, an optimal policy which minimizes the expected cost is discussed. Finally, a numerical example is given.
Equilibrium structure of differentially rotating and tidally distorted polytropic gaseous spheres

In this paper we propose a method for computing the equilibrium structure and various physical parameters of differentially rotating and tidally distorted polytropic stars. The law of differential rotation has been assumed to be in the form of ! (s) = ( P3=1 aie bis 2 ) 1 2. The proposed method utilizes the averaging approach of Kippenhahn and Thomas and concepts of Roche- Equipotential in a manner earlier used by Mohan et al. to incorporate the effects of differential rotation and tidal distortion on the equilibrium structure of polytropic stellar models. The inner structure and various physical parameters of differentially rotating and tidally distorted polytropic models with various polytropic indices have been computed with suitable combination of the parameters. The local stability criterion has also been used for different stellar models with suitable combination of parameters.
LEAD: Min-Max Optimization from a Physical Perspective

Adversarial formulations such as generative adversarial networks (GANs) have rekindled interest in two-player min-max games. A central obstacle in the optimization of such games is the rotational dynamics that hinder their convergence. In this paper, we show that game optimization shares dynamic properties with particle systems subject to multiple forces, and one can leverage tools from physics to improve optimization dynamics. Inspired by the physical framework, we propose LEAD, an optimizer for min-max games. Next, using Lyapunov stability theory and spectral analysis, we study LEAD's convergence properties in continuous and discrete time settings for a class of quadratic min-max games to demonstrate linear convergence to the Nash equilibrium. Finally, we empirically evaluate our method on synthetic setups and CIFAR-10 image generation to demonstrate improvements in GAN training.
Mechanical performance of concrete made with sewage sludge ash: A review (Part Ⅰ)

Abstract Sewage sludge is frequently stable and safe when used as construction materials since it bypasses several of the costly and energy-intensive phases of usage. This is supported by numerous studies, particularly when the proportion of sewage sludge is at ideal levels. The primary goal of this article is to demonstrate the use of sewage sludge in building and construction materials. Novel properties such as slump flow and setting time as well as strength properties such as compressive strength, tensile strength, flexural strength, elastic modulus, impact energy, and absorbed energy are the main aspects studied in this review (Part Ⅰ). Part II describes the physical and chemical properties, durability, and morphological structure of the sewage sludge ash. The findings in this review (Part Ⅰ) indicate that the flow of concrete decreased with the addition of sewage sludge ash due to its porous nature, but the strength properties improved with sewage sludge ash due to micro-filling voids and pozzolanic activity, which improved the binding properties of cement paste. However, optimum substitution is critical as a greater proportion decreased the strength due to a deficiency of flowability, which boosted compaction energy leading to more cavities. Different studies recommend different optimum doses. However, the typical range for the optimum dose of sewage sludge ash is 5–10% (by wt) of cement.
Cognitive functioning in a cohort of high-grade glioma patients

Abstract Background High grade gliomas are associated with cognitive problems. The aim of the study was to investigate cognitive functioning in a cohort of patients with high grade glioma, according to isocitrate dehydrogenase (IDH) and methyl guanine methyl transferase (MGMT) status and other clinical characteristics. Patients and methods The patients with the high-grade glioma treated in Slovenia in given period of time were included in study. Postoperatively they completed neuropsychological assessment consisting of Slovenian Verbal Learning Test, Slovenian Controlled Oral Word Association Test, Trail Making Test Part A and B and self-evaluation questionnaire. We analysed results (z-scores and dichotomized results) also according to IDH mutation and MGMT methylation. We examined differences between groups using T-test, Mann-Whitney U, χ2 and Kendall's Tau tests. Results Out of 275 patients in the cohort, we included 90. Forty-six percent of patients were unable to participate due to poor performance status and other conditions related to tumour. Patients with the IDH mutation were younger, with better performance status, larger proportions of grade III tumours and MGMT methylation. In this group cognitive functioning is significantly better in the domains of immediate recall, short delayed recall and delayed recall, and in the fields of executive functioning and recognition. There were no differences in cognitive functioning in regard to MGMT status. Grade III tumours were associated with more frequent MGMT methylation. Self-assessment proved week tool, associated only with immediate recall. Conclusions We found no differences in cognitive functioning according to MGMT status, but cognition was better when IDH mutation was present. In a cohort study of patients with high-grade glioma, almost half were unable to participate in a study, which points to an overrepresentation of patients with better cognitive functioning in the research.
Sprachdatenbasierte Modellierung von Wissensnetzen in der mittelalterlichen Romania (ALMA): Projektskizze

Abstract We present ALMA, a new research project aimed at investigating the interaction between language and knowledge practices from AD 1100 to 1500. Our primary question is how Medieval Italian, French, Occitan, Catalan, and Spanish developed into languages of knowledge and scholarship (German Wissen(schafts)sprachen) in permanent opposition to and exchange with the predominant Latin (but also with Arab, Greek, and Hebrew). Focusing on two domains, medicine and law, the project combines linguistics, text philology, and the history of science with the Digital Humanities and ontology engineering. ALMA will create two multi-lingual, domain-specific text corpora by integrating text editions of hitherto unedited manuscripts and incunabula, and digitized printed editions. Our corpus-linguistic exploration of the ALMA corpora will provide the basis for lexical-semantic studies that analyze emerging knowledge networks and the depth of their linguistic representations. We hypothesize that language evolution and the development of more complex linguistic structures will allow for measuring the impact of knowledge practices on medieval vernacular languages. We will trace the dissemination of lexical material across languages, language varieties, cultural spaces, and periods. This will enable us to follow specific vernacular communication channels. We will use cutting-edge technologies to compile, publish, and share our findings, and to model them in the form of historicized ontologies and Linked Data. Our onomasiological, ontology-driven approach will result in the creation of domain models that can be re-used within the Semantic Web. This has great potential to be relevant for researchers from different disciplines.