Miracle-dz/UniFAR-FTUs huggingface dataset:
Ephemeral Fabrication: Exploring a Ubiquitous Fabrication Scenario of Low-Effort, In-Situ Creation of Short-Lived Physical Artifacts

Personal fabrication empowers users to create objects increasingly easier and faster. This continuous decrease in effort evokes a speculative scenario of Ephemeral Fabrication (EF), enabled and amplified by emerging paradigms of mobile, wearable, or even body-integrated fabrication. EF yields fast, temporary, in-situ solutions for everyday problems (e.g., creating a protective skin, affixing a phone). Users solely create those, since the required effort is negligible. We present and critically reflect on the EF scenario, by exploring current trends in research and building a body-worn fabrication device. EF is a plausible extrapolation of current developments, entailing both positive (e.g., accessibility) and negative implications (e.g., unsustainability). Using speculative design methodology to question the trajectory of personal fabrication, we argue that to avert the aftermath of such futures, topics like sustainability can not remain an afterthought, but rather be situated in interactions themselves: through embedded constraints, conscious material choice, and constructive embedding of ephemerality.
EmotionBox: a music-element-driven emotional music generation system using Recurrent Neural Network

With the development of deep neural networks, automatic music composition has made great progress. Although emotional music can evoke listeners' different emotions and it is important for artistic expression, only few researches have focused on generating emotional music. This paper presents EmotionBox -an music-element-driven emotional music generator that is capable of composing music given a specific emotion, where this model does not require a music dataset labeled with emotions. Instead, pitch histogram and note density are extracted as features that represent mode and tempo respectively to control music emotions. The subjective listening tests show that the Emotionbox has a more competitive and balanced performance in arousing a specified emotion than the emotion-label-based method.
Predictive Price-Performance Optimization for Serverless Query Processing

We present an efficient, parametric modeling framework for predictive resource allocations, focusing on the amount of computational resources, that can optimize for a range of price-performance objectives for data analytics in serverless query processing settings. We discuss and evaluate in depth how our system, AutoExecutor, can use this framework to automatically select near-optimal executor and core counts for Spark SQL queries running on Azure Synapse. Our techniques improve upon Spark's in-built, reactive, dynamic executor allocation capabilities by substantially reducing the total executors allocated and executor occupancy while running queries, thereby freeing up executors that can potentially be used by other concurrent queries or in reducing the overall cluster provisioning needs. In contrast with post-execution analysis tools such as Sparklens, we predict resource allocations for queries before executing them and can also account for changes in input data sizes for predicting the desired allocations.
Allpix2 — silicon detector Monte Carlo simulations for particle physics and beyond

Allpix2 is a versatile, open-source simulation framework for silicon pixel detectors. Its goal is to ease the implementation of detailed simulations for both single sensors and more complex setups with multiple detectors. While originally created for silicon detectors in high-energy physics, it is capable of simulating a wide range of detector types for various application scenarios, through its interface to Geant4 to describe the interaction of particles with matter, and the different algorithms for charge transport and digitization. The simulation chain is arranged with the help of intuitive configuration files and an extensible system of modules, which implement the individual simulation steps. Detailed electric field maps imported from TCAD simulations can be used to precisely model the drift behavior of the charge carriers, bringing a new level of realism to the Monte Carlo simulation of particle detectors. Recently, Allpix2 has seen major improvements to its core framework to take full advantage of multi- and many-core processor architectures for simulating events fully parallel. Furthermore, new physics models such as charge carrier recombination in silicon have been introduced, further extending the application range. This contribution provides an overview of the framework and its components, highlighting the versatility and recent developments.
Extracting Photometric Redshift from Galaxy Flux and Image Data using Neural Networks in the CSST Survey

The accuracy of galaxy photometric redshift (photo-z) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-z information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogs, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are constructed to predict photo-z from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines the MLP and CNN, by employing the transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-z accuracy and outlier fraction can achieve σNMAD = 0.023 and η = 1.43% for the MLP using flux data only, and σNMAD = 0.025 and η = 1.21% for the CNN using image data only. The result can be further improved in high efficiency as σNMAD = 0.020 and η = 0.90% for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts 0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-z information from the CSST galaxy flux and image data.
DREAM: Uncovering Mental Models behind Language Models

To what extent do language models (LMs) build “mental models” of a scene when answering situated questions (e.g., questions about a speciﬁc ethical dilemma)? While cognitive science has shown that mental models play a fundamental role in human problem-solving, it is unclear whether the high question-answering performance of existing LMs is backed by similar model building - and if not, whether that can explain their well-known catastrophic failures. We observed that Macaw, an existing T5-based LM, when probed provides somewhat useful but inadequate mental models for situational questions (estimated accuracy=43%, usefulness=21%, consistency=42%). We propose DREAM, a model that takes a situational question as input to produce a mental model elaborating the situation, without any additional task spe-ciﬁc training data for mental models. It inherits its social commonsense through distant supervision from existing NLP resources. Our analysis shows that DREAM can produce signiﬁcantly better mental models (estimated accuracy=67%, usefulness=37%, consistency=71%) compared to Macaw. Finally, mental models generated by DREAM can be used as additional context for situational QA tasks. This additional context improves the answer accuracy of a Macaw zero-shot model by between +1% and +4% (absolute) on three different datasets.
Conceiving methods and innovative approaches for tall building structure systems

Conceiving tall building structure systems is a major step in the plan and design of tall buildings. Through summarizing the basic components and the basic lateral elements in various tall building structures, four conceiving methods for tall building structure systems are presented: (1) intercombination or alone forming by the basic lateral elements, (2) intercombination of different structures, (3) adding basic components in structure systems and (4) replacing basic components in structure systems. With these methods, designers could be able to establish the needed structure system to meet architectural functions, spatial arrangement, facade and other conditions, and these conceiving methods are helpful to architects or engineers to innovate the structure system in their special project. Copyright © 2009 John Wiley & Sons, Ltd.
Inherently Explainable Reinforcement Learning in Natural Language

We focus on the task of creating a reinforcement learning agent that is inherently explainable -- with the ability to produce immediate local explanations by thinking out loud while performing a task and analyzing entire trajectories post-hoc to produce causal explanations. This Hierarchically Explainable Reinforcement Learning agent (HEX-RL), operates in Interactive Fictions, text-based game environments in which an agent perceives and acts upon the world using textual natural language. These games are usually structured as puzzles or quests with long-term dependencies in which an agent must complete a sequence of actions to succeed -- providing ideal environments in which to test an agent's ability to explain its actions. Our agent is designed to treat explainability as a first-class citizen, using an extracted symbolic knowledge graph-based state representation coupled with a Hierarchical Graph Attention mechanism that points to the facts in the internal graph representation that most influenced the choice of actions. Experiments show that this agent provides significantly improved explanations over strong baselines, as rated by human participants generally unfamiliar with the environment, while also matching state-of-the-art task performance.
Design considerations on the line-side filter used in the dynamic voltage restorer

The effects of harmonic voltages generated by the voltage source inverter within a dynamic voltage restorer can be mitigated through the use of a line-side filter. However, injudicious design of the harmonic-filtering system can degrade the power-quality-enhancement capability of the restorer. Inappropriate filter design may also result in the need for the inverter current rating to be increased significantly, The purpose of the investigation is therefore to carry out a systematic analysis of the filter design by considering first the selection of the filter-capacitor on the basis of meeting a prespecified harmonic-performance criterion. The effect of the capacitor on the inverter rating is then evaluated and the method of determining the filter capacity is presented. An example, which involves electromagnetic transient studies, is included to demonstrate the efficacy of the proposed design method.
KAT: A Knowledge Augmented Transformer for Vision-and-Language

The primary focus of recent work with large-scale transformers has been on optimizing the amount of information packed into the model’s parameters. In this work, we ask a complementary question: Can multimodal transformers leverage explicit knowledge in their reasoning? Existing, primarily unimodal, methods have explored approaches under the paradigm of knowledge retrieval followed by answer prediction, but leave open questions about the quality and relevance of the retrieved knowledge used, and how the reasoning processes over implicit and explicit knowledge should be integrated. To address these challenges, we propose a - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result (+6% absolute) on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. Additionally, explicit knowledge integration improves interpretability of model predictions in our analysis.
Developing a Suitability Assessment Criteria for Software Developers: Behavioral Assessment Using Psychometric Test

A suitability assessment instrument for software developers was created using a psychometric criteria that identify the impact of behavior on the performance of software engineers. The instrument uses a questionnaire to help both individuals and IT recruiters to identify the psychological factors that affect the working performance of software engineers. Our study identifies the relationship between the behavioral drivers and the programming abilities of the subjects. In order to evaluate the instrument, a total of 100 respondents were compared on the basis of their programming skills and nine behavioral drivers. It was concluded that there is a direct relationship between certain human qualities, such as “Attention to Detail,” and the programming style of the students, while the “Locus of Control” factor was observed to have a negative correlation with performance in programming.
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis

Recently, generalization bounds of the non-convex empirical risk minimization paradigm using Stochastic Gradient Langevin Dynamics (SGLD) have been extensively studied. Several theoretical frameworks have been presented to study this problem from different perspectives, such as information theory and stability. In this paper, we present a unified view from privacy leakage analysis to investigate the generalization bounds of SGLD, along with a theoretical framework for re-deriving previous results in a succinct manner. Aside from theoretical findings, we conduct various numerical studies to empirically assess the information leakage issue of SGLD. Additionally, our theoretical and empirical results provide explanations for prior works that study the membership privacy of SGLD.
A Spurious-Free Characteristic Mode Formulation Based on Surface Integral Equation for Patch Antenna Structures

Conventional surface integral equation (SIE)-based characteristic mode formulation for the patch antenna structure with a finite substrate is susceptible to the spurious (nonphysical) modes due to the dielectric part. To avoid the contamination of spurious modes, we propose a novel generalized eigenvalue formulation based on the electric field integral equation-coupled Poggio–Miller–Chang–Harrington–Wu–Tsai equation. In this formulation, the real and imaginary parts of the exterior integral operators are chosen to construct the finalized weighting matrices, to connect the radiated power of the characteristic current. Compared with other SIE-based methods, this equation does not need additional postprocessing since it can effectively avoid spurious modes. Numerical results compared with the volume-SIE are investigated to validate the accuracy.
Khmer Word Search: Challenges, Solutions, and Semantic-Aware Search

Search is one of the key functionalities in digital platforms and applications such as an electronic dictionary, a search engine, and an e-commerce platform. While the search function in some languages is trivial, Khmer word search is challenging given its complex writing system. Multiple orders of characters and different spelling realizations of words impose a constraint on Khmer word search functionality. Additionally, spelling mistakes are common since robust spellcheckers are not commonly available across the input device platforms. These challenges hinder the use of Khmer language in search-embedded applications. Moreover, due to the absence of WordNet-like lexical databases for Khmer language, it is impossible to establish semantic relation between words, enabling semantic search. In this paper, we propose a set of robust solutions to the above challenges associated with Khmer word search. The proposed solutions include character order normalization, grapheme and phoneme-based spellcheckers, and Khmer word semantic model. The semantic model is based on the word embedding model that is trained on a 30-million-word corpus and is used to capture the semantic similarities between words.
Autonomous Driving in Adverse Weather Conditions: A Survey

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions. Index Terms — autonomous driving, adverse weather conditions, sensor fusion, perception enhancement, sensor robustness
Beamspace MIMO for Satellite Swarms

Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. However, this would require instantaneous perfect channel state information (CSI) and strong cooperation between satellites. In practice, orbital velocities around 7.5 km/s lead to very short channel coherence times on the order of fractions of the inter-satellite propagation delay, invalidating these assumptions. In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information. In particular, each satellite only requires information about its own position and that of the GS, while the GS has complete positional information. Due to the deterministic nature of satellite movement this information is easily obtained and no inter-satellite information exchange is required during transmission. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. Numerical evaluations show that the proposed scheme is, on average, within 99.8 % of the maximum achievable rate for instantaneous CSI and perfect cooperation.
Study on System Loss in Distribution Network with DG

The access of DG changes the operation and structure of traditional distribution network. This study mainly focused on controlling DG output current to reduce network loss of the system. Select a simple radial distribution system as example for theoretical analysis and derive the expressions of load current and node voltage. Assuming that there exists a real number k between DG output current and load. Then list the network loss and voltage deviation expressions. For the purpose of operation optimization, k can be determined by mathematical calculations. It proves that the method has a certain rationality to be effective in controlling network loss.
International Socioeconomic Predictors of Colon and Rectal Cancer Mortality: Is Colorectal Cancer a First World Problem?

PURPOSE Colorectal cancer (CRC) is a leading cause of international morbidity and is the second highest cause of cancer-related mortality in the world. The purpose of this study was to investigate the relationship between international health care spending on CRC mortality over time. METHODS This is a retrospective study using a publicly available data from the WHO Global Health Observatory database. General estimating equations were used to analyze the relationship between total health care expenditure per capita (THEpc) and CRC mortality at the country level. The primary predictors of interest were quartiles of THEpc. Other exposure variables included gross domestic product per capita (GDPpc), smoking (% of adult population smoking), physician density (per 10,000), and time. RESULTS Mortality decreased significantly from 2000 to 2016 (coefficient [95% CI], −2.2 [−3.3 to −1.1]; P < .001). THEpc, GDPpc, time, and percentage of adult population smoking were significant predictors of CRC mortality. Patients in the top two quartiles of THEpc had 3% higher rates of CRC mortality compared with countries in Q1 THEpc (Q3: 3.4 [1.9-4.8], P < .001; Q4: 3.2 [1.4-5.0], P = .001). Similar trends were seen in GDPpc (Q4: 3.2 [1.4-5.0], P = .001; Q3: 3.4 [1.9-4.8], P < .001; Q2: 1.7 [0.7-2.6], P < .001; Q1: reference). CONCLUSION Overall, mortality decreased significantly over the study period. Countries with higher health expenditures and higher gross domestic products experienced higher rates of CRC mortality. Further research will be necessary to determine the cause for this, but we postulate that it may be a result of more robust diagnostic and follow-up methods in countries with more resources.
Surface Reconstruction of Microscale Objects Based on Grid-Patterned Structured-Light Measurements

Abstract A structured-light projection system was designed for microscale objects with surface heights that ranged from tens to hundreds of microns. The system was composed of a universal projector and microscope system that supported editing the attributes of structured-light patterns in real-time and was capable of projecting microscale patterns. On this basis, reconstructing the metal surfaces of microscale objects based on grid patterns of structured light was investigated, the internal and external parameters of microscope vision and projection systems were calibrated, and an image algorithm for grid-node detection was designed. The results indicated that the proposed method successfully reconstructed the three-dimensional (3D) surface of microscale objects, and the reconstruction results were consistent with the original surfaces. With 95% confidence, the reconstruction precision in the X- and Y-directions was approximately ±4.0 μm and in the Z-direction was approximately ±7.5 μm. The designed system and the proposed method were suitable for 3D-shape measurement of microstructures in microscopic fields and can be adapted to meet a broader range of applications, as compared to current methods.
Mortality benefit of catheter ablation versus medical therapy in atrial fibrillation: An RCT only meta‐analysis

Catheter ablation for atrial fibrillation (AF) in comparison to medical therapy alone is known to improve freedom from arrhythmia and quality of life, but the benefit regarding mortality is unclear. The publication of several recent large randomized controlled trials (RCT) comparing ablation with medical therapy has warranted an updated meta‐analysis.
Discrete-time maps for the analysis of bifurcations and chaos in DC/DC converters

The main aim of this paper is to present a systematic classification of discrete-time models proposed in the literature for the analysis of nonlinear phenomena in power electronic DC/DC converters. It is shown how these models can be applied to different operating conditions and used to analyze the converter dynamics under both voltage-mode and current-mode control. The derivation of simplified maps is discussed together with the Jacobian matrices of the maps that are used for the analysis of several bifurcations and chaotic behaviors. The three most common converter topologies, i.e., boost, buck and buck-boost, operating in continuous conduction mode, are considered. An extensive literature review is also reported.