Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories

This paper presents a retrospective overview of a decade of research in our department towards self-organizing personal knowledge assistants in evolving corporate memories. Our research is typically inspired by real-world problems and often conducted in interdisciplinary collaborations with research and industry partners. We summarize past experiments and results comprising topics like various ways of knowledge graph construction in corporate and personal settings, Managed Forgetting and (Self-organizing) Context Spaces as a novel approach to Personal Information Management (PIM) and knowledge work support. Past results are complemented by an overview of related work and some of our latest findings not published so far. Last, we give an overview of our related industry use cases including a detailed look into CoMem, a Corporate Memory based on our presented research already in productive use and providing challenges for further research. Many contributions are only first steps in new directions with still a lot of untapped potential, especially with regard to further increasing the automation in PIM and knowledge work support.
MRQ: Support Multiple Quantization Schemes through Model Re-Quantization

Despite the proliferation of diverse hardware accelerators (e.g., NPU, TPU, DPU), deploying deep learning models on edge devices with fixed-point hardware is still challenging due to complex model quantization and conversion. Existing model quantization frameworks like Tensorflow QAT [1], TFLite PTQ [2], and Qualcomm AIMET [3] supports only a limited set of quantization schemes (e.g., only asymmetric per-tensor quantization in TF1.x QAT [4]). Accordingly, deep learning models cannot be easily quantized for diverse fixed-point hardwares, mainly due to slightly different quantization requirements. In this paper, we envision a new type of model quantization approach called MRQ (model re-quantization), which takes existing quantized models and quickly transforms the models to meet different quantization requirements (e.g., asymmetric ->symmetric, non-power-of-2 scale ->power-of-2 scale). Re-quantization is much simpler than quantizing from scratch because it avoids costly re-training and provides support for multiple quantization schemes simultaneously. To minimize re-quantization error, we developed a new set of re-quantization algorithms including weight correction and rounding error folding. We have demonstrated that MobileNetV2 QAT model [7] can be quickly re-quantized into two different quantization schemes (i.e., symmetric and symmetric+power-of-2 scale) with less than 0.64 units of accuracy loss. We believe our work is the first to leverage this concept of re-quantization for model quantization and models obtained from the re-quantization process have been successfully deployed on NNA in the Echo Show devices.
Kinematic Afterslip Patterns

Non‐inertial afterslip has been inferred to occur following large earthquakes. An explanation for this slow slip phenomenon is that coseismically generated stresses induce sliding on parts of a fault surface with velocity‐strengthening frictional properties. Here we develop an alternative mesoscale heuristic explanation for afterslip based on the idea that afterslip may occur on any portion of a fault that exhibits positive residual geometric moment following an earthquake, including sections that ruptured coseismically. Following a large earthquake, this model exhibits exponential time decay of afterslip and allows for variable sensitivity to coseismic event magnitude and residual geometric moment. This model provides a partial explanation for the spatial relationship of co‐ and post‐seismic slip associated with the 2011 MW = 9 Tohoku‐oki earthquake.
No Free Slide: Spurious Contact Forces in Incremental Potential Contact

Modeling contact between deformable solids is a fundamental problem in computer animation, mechanical design, and robotics. Existing methods based on $C^0$-discretizations -- piece-wise linear or polynomial surfaces -- suffer from discontinuities and irregularities in tangential contact forces, which can significantly affect simulation outcomes and even prevent convergence. To overcome this limitation, we employ smooth surface representations for both contacting bodies. Through a series of test cases, we show that our approach offers advantages over existing methods in terms of accuracy and robustness for both forward and inverse problems. The contributions of our work include identifying the limitations of existing methods, examining the advantages of smooth surface representation, and proposing forward and inverse problems to analyze contact force irregularities.
What Is the Difference Between a Mountain and a Molehill? Quantifying Semantic Labeling of Visual Features in Line Charts

Relevant language describing visual features in charts can be useful for authoring captions and summaries about the charts to help with readers’ takeaways. To better understand the interplay between concepts that describe visual features and the semantic relationships among those concepts (e.g., ‘sharp increase’ vs. ‘gradual rise’), we conducted a crowdsourced study to collect labels and visual feature pairs for univariate line charts. Using this crowdsourced dataset of labeled visual signatures, this paper proposes a novel method for labeling visual chart features based on combining feature-word distributions with the visual features and the data domain of the charts. These feature-word-topic models identify word associations with similar yet subtle differences in semantics, such as ‘flat,’ ‘plateau,’ and ‘stagnant,’ and descriptors of the visual features, such as ‘sharp increase,’ ‘slow climb,’ and ‘peak. ’ Our feature-word-topic model is computed using both a quantified semantics approach and a signal processing-inspired least-errors shape-similarity approach. We finally demonstrate the application of this dataset for annotating charts and generating textual data summaries.
Multi-variable Hard Physical Constraints for Climate Model Downscaling

Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.
Voronoi cells of non-general position spheres using the GPU

ABSTRACTWe present a GPU algorithm for computing the Voronoi cells for a set of spheres in R3. The algorithm is based on sampling rays from each sphere and finding the lower envelope of intersections of the rays with the sphere bisectors on the GPU. The presence of Voronoi vertices and edges is determined based on where neighboring rays intersect different bisectors. For accurate geometry, we present a numerical iteration approach to calculate the Voronoi vertices’ locations within a user-defined tolerance. The algorithm robustly handles input in non-general position and large input sets of thousands of spheres.
A Survey on Deep Learning-based Spatio-temporal Action Detection

Spatio-temporal action detection (STAD) aims to classify the actions present in a video and localize them in space and time. It has become a particularly active area of research in computer vision because of its explosively emerging real-world applications, such as autonomous driving, visual surveillance, entertainment, etc. Many efforts have been devoted in recent years to building a robust and effective framework for STAD. This paper provides a comprehensive review of the state-of-the-art deep learning-based methods for STAD. Firstly, a taxonomy is developed to organize these methods. Next, the linking algorithms, which aim to associate the frame- or clip-level detection results together to form action tubes, are reviewed. Then, the commonly used benchmark datasets and evaluation metrics are introduced, and the performance of state-of-the-art models is compared. At last, this paper is concluded, and a set of potential research directions of STAD are discussed.
Nonlinear Principal Component Analysis: Tropical Indo–Pacific Sea Surface Temperature and Sea Level Pressure

Abstract Nonlinear principal component analysis (NLPCA) is a generalization of traditional principal component analysis (PCA) that allows for the detection and characterization of low-dimensional nonlinear structure in multivariate datasets. The authors consider the application of NLPCA to two datasets: tropical Pacific sea surface temperature (SST) and tropical Indo–Pacific sea level pressure (SLP). It is found that for the SST data, the low-dimensional NLPCA approximations characterize the data better than do PCA approximations of the same dimensionality. In particular, the one-dimensional NLPCA approximation characterizes the asymmetry between spatial patterns characteristic of average El Nino and La Nina events, which the 1D PCA approximation cannot. The differences between NLPCA and PCA results are more modest for the SLP data, indicating that the lower-dimensional structures of this dataset are nearly linear.
OPTIMIZATION OF PLACEMENTVOLTAGE OF PIEZOELECTRIC ACTUATORS ON AN L-SHAPE BEAM USING PARTICLE SWARM OPTIMIZATION ALGORITHM

In this paper, controlling the location of the tip of an L-shape beam under gravity field is investigated. The beam is covered with piezoelectric patches. The gravity filed moves the tip of beam downward and the actuators with induced voltage move the tip to the previous location. to optimize the best location and voltages for actuators, the particle swarm optimization algorithm code is developed. The results show that the best position for the most effective actuators is located at the corner of the beam. Also with considering the best location for patches, with lower induced voltage, the location of the tip of beam cab controlled. Also, the results show that with the optimum location of actuators and appropriate voltage lead to using minimum energy with the desired shape in the beam. The results are compared with those reported in previous work.
Mutually unbiased bases as a commuting polynomial optimisation problem

We consider the problem of mutually unbiased bases as a polynomial optimization problem over the reals. We heavily reduce it using known symmetries before exploring it using two methods, combining a number of optimization techniques. The first of these is a search for bases using Lagrange-multipliers that converges rapidly in case of MUB existence, whilst the second combines a hierarchy of semidefinite programs with branch-and-bound techniques to perform a global search. We demonstrate that such an algorithm would eventually solve the open question regarding dimension 6 with finite memory, although it still remains intractable. We explore the idea that to show the inexistence of bases, it suffices to search for orthonormal vector sets of certain smaller sizes, rather than full bases. We use our two methods to conjecture the minimum set sizes required to show infeasibility, proving it for dimensions 3. The fact that such sub-problems seem to also be infeasible heavily reduces the number of variables, by 66\% in the case of the open problem, potentially providing an large speedup for other algorithms and bringing them into the realm of tractability.
Floorplet: Performance-aware Floorplan Framework for Chiplet Integration

A chiplet is an integrated circuit that encompasses a well-defined subset of an overall system's functionality. In contrast to traditional monolithic system-on-chips (SoCs), chiplet-based architecture can reduce costs and increase reusability, representing a promising avenue for continuing Moore's Law. Despite the advantages of multi-chiplet architectures, floorplan design in a chiplet-based architecture has received limited attention. Conflicts between cost and performance necessitate a trade-off in chiplet floorplan design since additional latency introduced by advanced packaging can decrease performance. Consequently, balancing power, performance, cost, area, and reliability is of paramount importance. To address this challenge, we propose Floorplet, a framework comprising simulation tools for performance reporting and comprehensive models for cost and reliability optimization. Our framework employs the open-source Gem5 simulator to establish the relationship between performance and floorplan for the first time, guiding the floorplan optimization of multi-chiplet architecture. The experimental results show that our framework decreases inter-chiplet communication costs by 24.81%.
Neural Poisson Surface Reconstruction: Resolution-Agnostic Shape Reconstruction from Point Clouds

We introduce Neural Poisson Surface Reconstruction (nPSR), an architecture for shape reconstruction that addresses the challenge of recovering 3D shapes from points. Traditional deep neural networks face challenges with common 3D shape discretization techniques due to their computational complexity at higher resolutions. To overcome this, we leverage Fourier Neural Operators to solve the Poisson equation and reconstruct a mesh from oriented point cloud measurements. nPSR exhibits two main advantages: First, it enables efficient training on low-resolution data while achieving comparable performance at high-resolution evaluation, thanks to the resolution-agnostic nature of FNOs. This feature allows for one-shot super-resolution. Second, our method surpasses existing approaches in reconstruction quality while being differentiable and robust with respect to point sampling rates. Overall, the neural Poisson surface reconstruction not only improves upon the limitations of classical deep neural networks in shape reconstruction but also achieves superior results in terms of reconstruction quality, running time, and resolution agnosticism.
DocBook: The Definitive Guide

Preface I. Introduction 1. Getting Started with SGML/XML HTML and SGML vs. XML Basic SGML/XML Concepts Elements and Attributes Entities How Does DocBook Fit In? 2. Creating DocBook Documents Making an SGML Document Making an XML Document Public Identifiers, System Identifiers, and Catalog Files Physical Divisions: Breaking a Document into Physical Chunks Logical Divisions: The Categories of Elements in DocBook Making a DocBook Book Making a Chapter Making an Article Making a Reference Page Making Front- and Backmatter. 3. Parsing DocBook Documents Validating Your Documents Understanding Parse Errors 4. Publishing DocBook Documents A Survey of Stylesheet Languages Using Jade and DSSSL to Publish DocBook Documents A Brief Introduction to DSSSL Customizing the Stylesheets Dealing with Multiple Declarations The DSSSL Architecture. 5. Customizing DocBook Should You Do This? If You Change DocBook, It's Not DocBook Anymore! Customization Layers Understanding DocBook Structure The General Structure of Customization Layers Writing, Testing, and Using a Customization Layer Testing Your Work Removing Elements Removing Attributes Adding Elements: Adding a Sect6 Other Modifications: Classifying a Role II. Reference DocBook Element Reference DocBook Parameter Entity Reference DocBook Character Entity Reference III. Appendixes A. Installation B. DocBook and XML C. DocBook Versions D. Resources E. What's on the CD-ROM? F. Interchanging DocBook Documents G. DocBook V3.1 Quick Reference Glossary
Private learning implies quantum stability

Figure 1: Summary of results for learning real-valued concept classes and quantum states with imprecise feedback. Except for the ? -arrow, an arrow A→ B implies that, if the sample complexity of learning in model A or the combinatorial parameter A is SA, then the complexity of learning in model B or the combinatorial parameter B is SB = poly(SA). The dotted arrow signifies that a technique used to prove that arrow for Boolean functions is a no-go for our quantum learning setting.
Active Acoustic Sensing for Robot Manipulation

Perception in robot manipulation has been actively explored with the goal of advancing and integrating vision and touch for global and local feature extraction. However, it is difficult to perceive certain object internal states, and the integration of visual and haptic perception is not compact and is easily biased. We propose to address these limitations by developing an active acoustic sensing method for robot manipulation. Active acoustic sensing relies on the resonant properties of the object, which are related to its material, shape, internal structure, and contact interactions with the gripper and environment. The sensor consists of a vibration actuator paired with a piezo-electric microphone. The actuator generates a waveform, and the microphone tracks the waveform's propagation and distortion as it travels through the object. This paper presents the sensing principles, hardware design, simulation development, and evaluation of physical and simulated sensory data under different conditions as a proof-of-concept. This work aims to provide fundamentals on a useful tool for downstream robot manipulation tasks using active acoustic sensing, such as object recognition, grasping point estimation, object pose estimation, and external contact formation detection.
High-Resolution Spectroscopy of Astrophysical Gamma-Ray Lines

High-resolution spectroscopy of gamma-ray line emission from radioactive nuclei allows studying the structure and kinematics of the inner regions of the Milky Way Galaxy. Measurements of the gamma rays produced in the decay of radioactive 26 Al with the INTEGRAL space observatory yield the radial velocity of the interstellar medium enriched with fresh nucleosynthesis products. The gas ejected by massive stars moves with considerably higher velocities than the colder components of the ISM. Comparing my results for velocity as a function of position along the Galactic plane with other observations and theory suggests that this hot gas shows this unususal kinematics due to the barred structure of our Galaxy.
Prognostic Value and Clinicopathologic Features of Platelet Distribution Width in Cancer: A Meta-Analysis

Background The association between platelet distribution width (PDW) and cancer has been evaluated by a few studies, but the influence of PDW on cancer prognosis is unclear. Therefore, we conducted the present meta-analysis. Material/Methods We identified relevant research using identical search strategies. The influence of PDW level on cancer prognosis, as well as clinical characteristics, was analyzed. Results A total of 11 studies comprising 2625 cancer patients were included in our meta-analysis. The results suggested that high PDW level was obviously related to poor OS (HR=1.54, 95%CI 1.18–2.00), especially for breast cancer (HR=1.21, 95%CI 1.07–1.36) and pharyngolaryngeal cancer (HR=3.06, 95%CI 1.68–5.57). Furthermore, high PDW was obviously related to poor OS both in older and younger subgroups, with combined HR estimates of 1.58 (95%CI 1.15–2.16) and 1.64 (95%CI 1.19–2.26), respectively. High PDW level was notably related to poor OS in the cut-off value ≥16% subgroup (HR=1.84, 95%CI 1.01–3.40). Moreover, high PDW level was obviously associated with lymph node metastasis (OR=1.43, 95%CI 1.04–1.99). Conclusions The findings of this study suggest that PDW is an effective and convenient indicator of cancer prognosis. Furthermore, high PDW level is obviously associated with lymph node metastasis.
The Journal of Systems & Software

Background: Developers use Static Analysis Tools (SATs) to control for potential quality issues in source code, including defects and technical debt. Tool vendors have devised quite a number of tools, which makes it harder for practitioners to select the most suitable one for their needs. To better support developers, researchers have been conducting several studies on SATs to favor the understanding of their actual capabilities. Aims: Despite the work done so far, there is still a lack of knowledge regarding (1) what is their agreement, and (2) what is the precision of their recommendations. We aim at bridging this gap by proposing a large-scale comparison of six popular SATs for Java projects: Better Code Hub, CheckStyle, Coverity Scan, FindBugs, PMD, and SonarQube. Methods: We analyze 47 Java projects applying 6 SATs. To assess their agreement, we compared them by manually analyzing – at line – and class-level — whether they identify the same issues. Finally, we evaluate the precision of the tools against a manually-defined ground truth. Results: The key results show little to no agreement among the tools and a low degree of precision. Conclusion: Our study provides the first overview on the agreement among different tools as well as an extensive analysis of their precision that can be used by researchers, practitioners, and tool vendors to map the current capabilities of the tools and envision possible improvements.
Domain Aggregation Networks for Multi-Source Domain Adaptation

Proof: Given ↵ 2 , the mixture b S = P i ↵i b Si can be considered as the joint source data with km points, where a point x (i) from b Si has weight ↵i/m. Define = suph2H LT (h, fT ) P i ↵iLb Si(h, fSi). Changing a point x (i) from b Si will change at most MS↵i m . Using the McDiarmid’s inequality, we have Pr( E[ ] > ✏)  exp ⇣ 2✏m M2 Sk↵k 2 2 ⌘ . As a result, for 2 (0, 1), w.p. at least 1 , the following holds for any h 2 H
SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs

Binary similarity detection is a critical technique that has been applied in many real-world scenarios where source code is not available, e.g., bug search, malware analysis, and code plagiarism detection. Existing works are ineffective in detecting similar binaries in cases where different compiling optimizations, compilers, source code versions, or obfuscation are deployed. We observe that all the cases do not change a binary's key code behaviors although they significantly modify its syntax and structure. With this key observation, we extract a set of key instructions from a binary to capture its key code behaviors. By detecting the similarity between two binaries' key instructions, we can address well the ineffectiveness limitation of existing works. Specifically, we translate each extracted key instruction into a self-defined key expression, generating a key-semantics graph based on the binary's control flow. Each node in the key-semantics graph denotes a key instruction, and the node attribute is the key expression. To quantify the similarity between two given key-semantics graphs, we first serialize each graph into a sequence of key expressions by topological sort. Then, we tokenize and concatenate key expressions to generate token lists. We calculate the locality-sensitive hash value for all token lists and quantify their similarity. %We implement a prototype, called SemDiff, consisting of two modules: graph generation and graph diffing. The first module generates a pair of key-semantics graphs and the second module diffs the graphs. Our evaluation results show that overall, SemDiff outperforms state-of-the-art tools when detecting the similarity of binaries generated from different optimization levels, compilers, and obfuscations. SemDiff is also effective for library version search and finding similar vulnerabilities in firmware.