Miracle-dz/UniFAR-FTUs huggingface dataset:
Touching the Moon: Leveraging Passive Haptics, Embodiment and Presence for Operational Assessments in Virtual Reality

Space agencies are in the process of drawing up carefully thought-out Concepts of Operations (ConOps) for future human missions on the Moon. These are typically assessed and validated through costly and logistically demanding analogue field studies. While interactive simulations in Virtual Reality (VR) offer a comparatively cost-effective alternative, they have faced criticism for lacking the fidelity of real-world deployments. This paper explores the applicability of passive haptic interfaces in bridging the gap between simulated and real-world ConOps assessments. Leveraging passive haptic props (equipment mockup and astronaut gloves), we virtually recreated the Apollo 12 mission procedure and assessed it with experienced astronauts and other space experts. Quantitative and qualitative findings indicate that haptics increased presence and embodiment, thus improving perceived simulation fidelity and validity of user reflections. We conclude by discussing the potential role of passive haptic modalities in facilitating early-stage ConOps assessments for human endeavours on the Moon and beyond.
Charge Amplification in Sub-atmospheric CF4:He Mixtures for Directional Dark Matter Searches

Low pressure gaseous Time Projection Chambers (TPCs) are a viable technology for directional Dark Matter (DM) searches and have the potential for exploring the parameter space below the neutrino fog. Gases like CF4 are advantageous because they contain flourine which is predicted to have heightened elastic scattering rates with a possible Weakly Interacting Massive Particle (WIMP) DM candidate. The low pressure of CF4 must be maintained, ideally lower than 100 Torr, in order to elongate potential Nuclear Recoil (NR) tracks which allows for improved directional sensitivity and NR/Electron Recoil (ER) discrimination. Recent evidence suggests that He can be added to heavier gases, like CF4, without significantly affecting the length of 12C and 19F recoils due to its lower mass. Such addition of He has the advantage of improving sensitivity to lower mass WIMPs. Simulations can not reliably predict operational stability in these low pressure gas mixtures and thus must be demonstrated experimentally. In this paper we investigate how the addition of He to low pressure CF4 affects the gas gain and energy resolution achieved with a single Thick Gaseous Electron Multiplier (ThGEM).
Implications of self-identified race, ethnicity, and genetic ancestry on genetic association studies in biobanks within health systems

Precision medicine aims to create biomedical solutions tailored to specific factors that affect disease risk and treatment responses within the population. The success of the genomics era and recent widespread availability of electronic health records (EHR) has ushered in a new wave of genomic biobanks connected to EHR databases (EHR-linked biobanks). This perspective aims to discuss how race, ethnicity, and genetic ancestry are currently utilized to study common disease variation through genetic association studies. Although genetic ancestry plays a significant role in shaping the genetic landscape underlying disease risk in humans, the overall risk of a disease is caused by a complex combination of environmental, sociocultural, and genetic factors. When using EHR-linked biobanks to interrogate underlying disease etiology, it is also important to be aware of how the biases associated with commonly used descent-associated concepts such as race and ethnicity can propagate to downstream analyses. We intend for this resource to support researchers who perform or analyze genetic association studies in the EHR-linked biobank setting such as those involved in consortium-wide biobanking efforts. We provide background on how race, ethnicity, and genetic ancestry play a role in current association studies, highlight considerations where there is no consensus about best practices, and provide transparency about the current shortcomings.
Neural optimal controller for stochastic systems via pathwise HJB operator

The aim of this work is to develop deep learning-based algorithms for high-dimensional stochastic control problems based on physics-informed learning and dynamic programming. Unlike classical deep learning-based methods relying on a probabilistic representation of the solution to the Hamilton--Jacobi--Bellman (HJB) equation, we introduce a pathwise operator associated with the HJB equation so that we can define a problem of physics-informed learning. According to whether the optimal control has an explicit representation, two numerical methods are proposed to solve the physics-informed learning problem. We provide an error analysis on how the truncation, approximation and optimization errors affect the accuracy of these methods. Numerical results on various applications are presented to illustrate the performance of the proposed algorithms.
Numerical investigation of the structure factors of the Read-Rezayi series

We numerically investigate the guiding center stucture factors of several states in the Read-Rezayi family. Using exact diagonalizations on the torus and density matrix renormalization group on an infinite cylinder, we test a conjecture proposed in [Can et al, Phys. Rev. Lett. 113 (2014)] for the $\nu = \frac{3}{5}$ and $\nu = \frac{4}{6}$ Read-Rezayi states. Furthermore, we discuss the strong finite-size effects present in numerically accessible wavefunctions, and provide a simple recipe to minimize them on manifolds where non-Abelian theories have topological degeneracies.
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding

Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learning (RL) has shown potential to address the intricacies of MAPF; however, it has also been shown to struggle with scalability, demanding intricate implementation, lengthy training, and often exhibiting unstable convergence, limiting its practical application. In this paper, we introduce Heuristics-Informed Multi-Agent Pathfinding (HiMAP), a novel scalable approach that employs imitation learning with heuristic guidance in a decentralized manner. We train on small-scale instances using a heuristic policy as a teacher that maps each single agent observation information to an action probability distribution. During pathfinding, we adopt several inference techniques to improve performance. With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques.
ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis

The immense popularity of racket sports has fueled substantial demand in tactical analysis with broadcast videos. However, existing manual methods require laborious annotation, and recent attempts leveraging video perception models are limited to low-level annotations like ball trajectories, overlooking tactics that necessitate an understanding of stroke techniques. State-of-the-art action segmentation models also struggle with technique recognition due to frequent occlusions and motion-induced blurring in racket sports videos. To address these challenges, We propose ViSTec, a Video-based Sports Technique recognition model inspired by human cognition that synergizes sparse visual data with rich contextual insights. Our approach integrates a graph to explicitly model strategic knowledge in stroke sequences and enhance technique recognition with contextual inductive bias. A two-stage action perception model is jointly trained to align with the contextual knowledge in the graph. Experiments demonstrate that our method outperforms existing models by a significant margin. Case studies with experts from the Chinese national table tennis team validate our model's capacity to automate analysis for technical actions and tactical strategies. More details are available at: https://ViSTec2024.github. io/.
Fractal Gripper: Adaptive manipulator with mode switching

Although the multi-jointed underactuated manipulator is highly dexterous, its grasping capacity does not match that of the parallel jaw gripper. This work introduces a fractal gripper to enhance the grasping capacity of multi-joint underactuated manipulators, preserving their passive clamping features. We describe in detail the working principle and manufacturing process of the fractal gripper. This work, inspired by the 'Fractal Vise' structure, resulted in the invention of a fractal gripper with mode switching capabilities. The fractal gripper inherits the inherent adaptive properties of the fractal structure and realizes the self-resetting function by integrating spring into the original design, thereby enhancing the efficiency of object grasping tasks. The fractal gripper prevents object damage by distributing pressure evenly and applying it at multiple points through its fractal structure during closure. Objects of various shapes are effectively grasped by the fractal gripper, which ensures a safe and secure grasp. The superior performance was provided by the force distribution characteristics of the fractal gripper. By applying the flexible polymer PDMS, which possesses superior elasticity, to the fractal structure's wrapping surface, potential scratching during grasping is effectively prevented, thus protecting the object's geometric surface. Grab experiments with objects of diverse shapes and sizes confirm fractal gripper multi-scale adaptability and superior grasping stability.
A statistical method for crack detection in 3D concrete images

In practical applications, effectively segmenting cracks in large-scale computed tomography (CT) images holds significant importance for understanding the structural integrity of materials. However, classical methods and Machine Learning algorithms often incur high computational costs when dealing with the substantial size of input images. Hence, a robust algorithm is needed to pre-detect crack regions, enabling focused analysis and reducing computational overhead. The proposed approach addresses this challenge by offering a streamlined method for identifying crack regions in CT images with high probability. By efficiently identifying areas of interest, our algorithm allows for a more focused examination of potential anomalies within the material structure. Through comprehensive testing on both semi-synthetic and real 3D CT images, we validate the efficiency of our approach in enhancing crack segmentation while reducing computational resource requirements.
On the relation between maximum spectral transition positions and phone boundaries

Earlier research has shown that the maximum spectral transition positions are related with the perceptual critical points that contain the most important information for consonant and syllable perception. This paper presents a quantitative analysis of the relation, in time, between the maximum spectral transition positions and the phone boundaries in fluent read speech. This analysis is based on the training part of the TIMIT American English database which contains both phone boundaries and labels manually-determined by a group of experts. The results of this analysis show that there is a significant correlation between the maximum spectral transition positions and the manually selected phone boundaries. This, in turn, suggests that there is an important relation between the commonly accepted phone boundaries and the perceptual critical points. Index Terms: phone boundaries, spectral transition, phonemes
Text Classification Using Machine Learning Techniques

Automated text classification has been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. In general, text classification plays an important role in information extraction and summarization, text retrieval, and question- answering. This paper illustrates the text classification process using machine learning techniques. The references cited cover the major theoretical issues and guide the researcher to interesting research directions.
Scholarly Horizons: University of Minnesota, Morris Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal Undergraduate

One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.
Face Sketch Synthesis and Recognition

In this paper, we propose a novel face photo retrieval system using sketch drawings. By transforming a photo image into a sketch, we reduce the difference between photo and sketch significantly, thus allow effective matching between the two. To improve the synthesis performance, we separate shape and texture information in a face photo, and conduct transformation on them respectively. Finally a Bayesian classifier is used to recognize the probing sketch from the synthesized pseudo-sketches. Experiments on a data set containing 606 people clearly demonstrate the efficacy of the algorithm.
Prolegomena to the recognition of floor plan sketches: A typology of architectural and grafic primitives in freehand representation

This paper describes the set-up of AIDA, a computer tool that supports the conceptual design of complex objects and products. The AIDA system is based on a combination of three methods: Case Based Reasoning for suggesting initial proposals, Rule-Based Reasoning to assess these proposals on their functional merit, and Constraint-Based Geometric Modelling to model and visualise the proposals. These reasoning and modelling techniques are implemented in separate modules. In this paper we present the underlying ideas of the AIDA system and show how this framework can be used for conceptual design of aircraft.
Pre-quantization of the Moduli Space of Flat G-bundles

Pre-quantization of the Moduli Space of Flat G-Bundles Derek Krepski Doctor of Philosophy Graduate Department of Mathematics University of Toronto 2009 This thesis studies the pre-quantization of quasi-Hamiltonian group actions from a cohomological viewpoint. The compatibility of pre-quantization with symplectic reduction and the fusion product are established, and are used to understand the necessary and sufficient conditions for the pre-quantization of MG(Σ), the moduli space of flat G-bundles over a closed surface Σ. For a simply connected, compact, simple Lie group G, MG(Σ) is known to be prequantizable at integer levels. For non-simply connected G, however, integrality of the level is not sufficient for pre-quantization, and this thesis determines the obstruction— namely a certain cohomology class in H(G×G; Z)—that places further restrictions on the underlying level. The levels that admit a pre-quantization of the moduli space are determined explicitly for all non-simply connected, compact, simple Lie groups G. Partial results are obtained for the case of a surface Σ with marked points. Also, it is shown that via the bijective correspondence between quasi-Hamiltonian group actions and Hamiltonian loop group actions, the corresponding notions of prequantization coincide.
The Alloc Stream Facility: A Redesign of Application-Level Stream I/O

This paper describes the design and implementation of a new application level I/O facility, called the Alloc Stream Facility. The Alloc Stream Facility has several key advantages. First, performance is substantially improved as a result of a) the structure of the facility that allows it to take advantage of system speci(cid:12)c features like mapped (cid:12)les, and b) a reduction in data copying and the number of I/O system calls. Second, the facility is designed for multi-threaded applications running on multiprocessors and allows for a high degree of concurrency. Finally, the facility can support a variety of I/O interfaces, including stdio, emulated Unix I/O, ASI, and C++ streams, in a way that allows applications to freely intermix calls to the di(cid:11)erent interfaces, resulting in improved code reusability. We show that on several Unix workstation platforms the performance of Unix applications using the Alloc Stream Facility can be substantially better that when the applications use the original I/O facilities.
How Light‐Absorbing Properties of Organic Aerosol Modify the Asian Summer Monsoon Rainfall?

Light‐absorbing aerosols not only contribute to Earth's radiative balance but also influence regional climate by cooling the surface and warming the atmosphere. Following recent suggestions that organic aerosols (OAs) absorb substantial amount of solar radiation, we examine the role of light‐absorbing properties of OA on Asian summer monsoon rainfall redistribution using observational data and an atmospheric general circulation model experiment. Results suggest that the enhanced light absorption by OA in Southeast Asia and Northeast Asia is associated with the advance of the Indian summer monsoon in May and the southward shift of East Asian summer monsoon rainband in June. The rainfall redistribution in May is induced by elevated orographic effect with a warm‐core upper‐level anticyclone and surface warming of 1–2 °C over the Tibetan Plateau, whereas that of the East Asian summer monsoon in June is formed by stable conditions associated with surface cooling and atmospheric warming around 30°N.
Rapid detection of earthquake damage areas using VIIRS nearly constant contrast night-time light data

ABSTRACT To rapidly identify earthquake damage areas after earthquakes occur, a method based on Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) images with nearly constant contrast (NCC) is proposed. The differences between post-earthquake and mean image of 3 months VIIRS NCC NTL before an earthquake are taken to reflect the effects of earthquakes on city light brightness loss, with eliminating the influence of clouds, lunar and solar irradiance, and poor-quality NCC NTL pixels. In addition, the standard deviation (STD) of the NTL images collected during the 3 months before an earthquake is calculated. Then, the areas with the differences less than −1 times the local STD are taken to be earthquake damage areas, and the areas with differences less than −2 times the local STD are taken to be severely damage areas. Finally, this method is applied to the 2015 Nepal moment magnitude (Mw) 7.8 earthquake, the 2014 Chile Mw 8.1 earthquake, and the 2015 Chile Mw 8.3 earthquake. Compared with Prompt Assessment of Global Earthquakes for Response output for selected cities impacted by these earthquakes, the results show that the overall accuracies are 14.9%, 75.0%, and 68.4% respectively, for the three earthquakes with the accuracy increases with greater earthquake intensities.
Synopsis Diffusion for Robust Aggregation in Sensor Networks

Aggregating sensor readings within the network is an essential technique for conserving energy in sensor networks. Previous work proposes aggregating along a tree overlay topology in order to conserve energy. However, a tree overlay is very fragile, and the high rate of node and link failures in sensor networks often results in a large fraction of readings being unaccounted for in the aggregate. Value splitting on multi-path overlays, as proposed in TAG, reduces the variance in the error, but still results in signi(cid:2)cant errors. Previous approaches are fragile, fundamentally, because they tightly couple aggregate computation and message routing. In this paper, we propose a family of aggregation techniques, called synopsis diffusion , that decouples the two, enabling aggregation algorithms and message routing to be optimized independently. As a result, the level of redundancy in message routing (as a trade-off with energy consumption) can be adapted to both expected and encountered network conditions. We present a number of concrete examples of synopsis diffusion algorithms, including a broadcast-based instantiation of synopsis diffusion that is as energy ef(cid:2)cient as a tree, but dramatically more robust.
Blockchain Oracles: A Framework for Blockchain-Based Applications Systematic Literature Review Protocol and Results

We live in an age of rapid development of information technology and increased value it brings to businesses and consumers. Blockchain is one of those revolutionary inventions, that is increasingly being adopted throughout various industries. It is a shared decentralized public ledger containing transactions executed by network participants. [1] Increasingly this adoption is followed by the necessity for bringing external data which exists outside of blockchain into the network. Oracles can help achieve this objective by bridging the gap between the outside world and the blockchain network. This is not an easy task to accomplish, which is why understanding the role oracles play in blockchain solutions, as well as investigating the mechanisms governing this relationship is important in assessing their impact on overall blockchain adoption.
Efficient Model Checking of Fault-tolerant Distributed Protocols Using Symmetry Reduction

Fault-tolerant (FT) distributed protocols (such as group membership, consensus, etc.) represent fundamental building blocks behind many practical systems, e.g., the Google File System. Not only does one desire rigor in the protocol design but especially in its verification given the complexity and fallibility of manual proofs. The application of model checking (MC) for protocol verification is attractive with its full automation and rich property language. However, being an exhaustive exploration method, its scalable use is very much constrained by the overall number of different system states. We observe that, although FT distributed protocols usually display a very high degree of symmetry which stems from permuting different processes, MC efforts targeting their automated verification often disregard this symmetry. In this paper we leverage the framework of symmetry reduction to define a high-level description language specific to FT distributed protocols which is able to automatically capture their basic symmetries. The language supports synchronous as well as asynchronous protocols, a variety of fault types, and the specification of safety and liveness properties. Specifications written in this language can directly be analyzed by tools supporting symmetry reduction. We demonstrate the benefit of our approach using the example of well-known and highly complex distributed FT protocols, specifically Paxos and the Byzantine Generals.
Control-Flow Integrity for Real-Time Embedded Systems

While security protections continue to be developed for general-purpose computers, real-time computing has remained unprotected against control-flow hijacking attacks. Existing solutions rely on hardware unavailable to embedded systems due to the cost, or impose excessive overhead, leaving real-time applications unable to operate within their time constraints. We propose RECFISH++, a Control-Flow Integrity implementation focused on protecting real-time embedded systems. By modifying LLVM and FreeRTOS, a popular compiler back-end and real-time operating system, we provide an end-to-end solution for protecting any real-time application on the ARM Cortex-M microprocessor against control-flow hijacking attacks.