Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Manifestations, Diagnosis, and Treatment of Mycobacterium haemophilum Infections

SUMMARY Mycobacterium haemophilum is a slowly growing acid-fast bacillus (AFB) belonging to the group of nontuberculous mycobacteria (NTM) frequently found in environmental habitats, which can colonize and occasionally infect humans and animals. Several findings suggest that water reservoirs are a likely source of M. haemophilum infections. M. haemophilum causes mainly ulcerating skin infections and arthritis in persons who are severely immunocompromised. Disseminated and pulmonary infections occasionally occur. The second at-risk group is otherwise healthy children, who typically develop cervical and perihilar lymphadenitis. A full diagnostic regimen for the optimal detection of M. haemophilum includes acid-fast staining, culturing at two temperatures with iron-supplemented media, and molecular detection. The most preferable molecular assay is a real-time PCR targeting an M. haemophilum-specific internal transcribed spacer (ITS), but another approach is the application of a generic PCR for a mycobacterium-specific fragment with subsequent sequencing to identify M. haemophilum. No standard treatment guidelines are available, but published literature agrees that immunocompromised patients should be treated with multiple antibiotics, tailored to the disease presentation and underlying degree of immune suppression. The outcome of M. haemophilum cervicofacial lymphadenitis in immunocompetent patients favors surgical intervention rather than antibiotic treatment.
Well-posedness of stochastic evolution equations with H\"older continuous noise

We show existence and pathwise uniqueness of probabilistically strong solutions to a pseudomonotone stochastic evolution problem on a bounded domain $D\subseteq\mathbb{R}^d$, $d\in\mathbb{N}$, with homogeneous Dirichlet boundary conditions and random initial data $u_0\in L^2(\Omega;L^2(D))$. The main novelty is the presence of a merely H\"older continuous multiplicative noise term. In order to show the well-posedness, we simultaneously regularize the H\"older noise term by inf-convolution and add a perturbation by a higher order operator to the equation. Using a stochastic compactness argument we may pass to the limit and we obtain first a martingale solution. Then by a pathwise uniqueness argument we get existence of a probabilistically strong solution.
Evaluating FPGA-acceleration for real-time unstructured search

Emerging data-centric workloads that operate on and harvest useful insights from large amounts of unstructured data require corresponding new data-centric system architecture optimizations. In particular, with the growing importance of power and cooling costs, a key challenge for such future designs is to achieve increased performance at high energy efficiency. At the same time, recent trends towards better support for reconfigurable logic enable the use of energy-efficient accelerators. Combining these trends, in this paper, we examine the applicability of acceleration in future data-centric system architectures. We focus on an important class of data-centric workloads, real-time unstructured search, or information filtering, where large collections of documents are scored against specific topic profiles, and present an FPGA-based implementation to accelerate such workloads. Our implementation, based on the GiDEL PROCStar IV board using Altera Stratix IV FPGAs, demonstrates excellent performance and energy efficiency, 20 to 40 times better than baseline server systems for typical usage scenarios. Our results also highlight interesting insights for the design of accelerators in future data-centric systems.
Student t-L\'evy regression model in YUIMA

The aim of this paper is to discuss an estimation and a simulation method in the \textsf{R} package YUIMA for a linear regression model driven by a Student-$t$ L\'evy process with constant scale and arbitrary degrees of freedom. This process finds applications in several fields, for example finance, physic, biology, etc. The model presents two main issues. The first is related to the simulation of a sample path at high-frequency level. Indeed, only the $t$-L\'evy increments defined on an unitary time interval are Student-$t$ distributed. In YUIMA, we solve this problem by means of the inverse Fourier transform for simulating the increments of a Student-$t$ L\'{e}vy defined on a interval with any length. A second problem is due to the fact that joint estimation of trend, scale, and degrees of freedom does not seem to have been investigated as yet. In YUIMA, we develop a two-step estimation procedure that efficiently deals with this issue. Numerical examples are given in order to explain methods and classes used in the YUIMA package.
Comparative digestibility of amino acids of maize, sorghum, finger millet and pearl millet in cockerels and Japanese quails

1. The objective of the present study was to determine the apparent digestibilities of amino acids of maize, low tannin sorghum, pearl millet and finger millet in adult cockerels and Japanese quails. 2. Adult White Leghorn cockerels (25 weeks of age) and adult Japanese quails (15 weeks of age) were used to determine the apparent digestibilities of amino acids in cereal grains. 3. The digestibilities of amino acids in sorghum determined with quails were similar to cockerels, with the exception of histidine, which was significantly lower in quails. 4. In finger millet, the digestibilities of methionine and leucine were significantly higher, while that of histidine and cystine were significantly lower in adult quails than in adult cockerels. 5. The digestibilities of pearl millet amino acids were similar in quails and cockerels, except for that of histidine, which was significantly lower in quails. 6. Almost all the amino acids in maize, except histidine, had significantly higher digestibility values in quails than in cockerels. Histidine digestibility of all the 4 cereal grains was significantly lower in quails than in cockerels. 7. The amino acid digestibility values for certain cereal grains determined with cockerels do not support their use in formulating practical quail diets. The amino acid digestibility values of maize were underestimated by using cockerels while that of finger millet was overestimated.
Advances in MR Imaging of Leukodystrophies

Leukodystrophies are hereditary disorders of white matter that impair brain that is initially normally formed and developed (1, 2). They can affect brain myelin throughout life. The disorders are commonly progressive in nature and ultimately fatal. First manifestations are often cognitive deterioration and neuropsychological problems. Motor and balance difficulties occur, as do visual abnormalities. Classic leukodystrophies lead to a vegetative state or death within months to years. In general, the earlier the onset of symptoms, the more progressive the disease course. Most leukodystrophies are monogenetic disorders. The mutant gene often encodes an enzyme or protein that maintains neuronal and/or glial health and is responsible for regulation of brain metabolism. Many enzymes involved play a role in lipid metabolism. The inability to degrade or synthesize substrate leads to an upstream excess or downstream lack of vital lipids. This can cause a wide range of pathology, from inflammatory demyelination to axonal degeneration and microglial activation. Hence, the often cited prominent demyelination is only one manifestation in leukodystrophies, and myelinforming oligodendrocytes are not the only cells affected in these disorders. Yet, there are some common characteristics that distinguish the pathology in leukodystrophies from that of other disorders. MRI has played a seminal role in visualization of the lesion pattern. (3). The demyelinating lesions are usually confluent and symmetric. Many of the classic disorders, such as X-linked adrenoleukodystrophy (X-ALD), metachromatic leukodystrophy (MLD) and Krabbe, show relative sparing of subcortical fibers. Yet again, other disorders have early involvement of the U fibers and other unique characteristics, such as cystic rarefaction and degeneration. Hereditary disorders that do not show demyelination but rather hypomyelination have come to increasing attention (4). Common neurological features in hypomyelinating disorders are developmental delay, nystagmus, cerebellar ataxia and spasticity. One of the better characterized hypomyelinating disorders is Pelizaeus Merzbacher disease (PMD). Beyond this classic disorder, a multitude of other hypomyelinating disorders exist. Only about half of the patients with evidence of hypomyelination on MRI come to a definitive diagnosis. Yet, specific clinical and MRI features can be pathognomonic and lead to diagnosis.
System Support for Environmentally Sustainable Computing in Data Centers

Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability. While hardware accelerators and renewable energy have been utilized to enhance sustainability, addressing Quality of Service (QoS) degradation caused by renewable energy supply and hardware recycling remains challenging: (1) prior accelerators exhibit significant carbon footprints due to limited reconfigurability and inability to adapt to renewable energy fluctuations; (2) integrating recycled NAND flash chips in data centers poses challenges due to their short lifetime, increasing energy consumption; (3) the absence of a sustainability estimator impedes data centers and users in evaluating and improving their environmental impact. This study aims to improve system support for environmentally sustainable data centers by proposing a reconfigurable hardware accelerator for intensive computing primitives and developing a fractional NAND flash cell to extend the lifetime of recycled flash chips while supporting graceful capacity degradation. We also introduce a sustainability estimator to evaluate user task energy consumption and promote sustainable practices. We present our preliminary results and recognize this as an ongoing initiative with significant potential to advance environmentally sustainable computing in data centers and stimulate further exploration in this critical research domain.
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning

The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most methods based on the fine-tuning strategy have limited transfer learning ability for cross-domain applications. This study proposed a novel approach that employs a soft prompt-based learning architecture, which introduces trainable prompts to guide LLMs toward desired outputs. We examined two types of LLM architectures, including encoder-only GatorTron and decoder-only GatorTronGPT, and evaluated their performance for the extraction of social determinants of health (SDoH) using a cross-institution dataset from the 2022 n2c2 challenge and a cross-disease dataset from the University of Florida (UF) Health. The results show that decoder-only LLMs with prompt tuning achieved better performance in cross-domain applications. GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and 5.5% and 14.5% in a cross-disease setting.
Universal fluctuations and noise learning from Hilbert-space ergodicity

Systems reaching thermal equilibrium are ubiquitous. For classical systems, this phenomenon is typically understood statistically through ergodicity in phase space, but translating this to quantum systems is a long-standing problem of interest. Recently a quantum notion of ergodicity has been proposed, namely that isolated, global quantum states uniformly explore their available state space, dubbed Hilbert-space ergodicity. Here we observe signatures of this process with an experimental Rydberg quantum simulator and various numerical models, before generalizing to the case of a local quantum system interacting with its environment. For a closed system, where the environment is a complementary subsystem, we predict and observe a smooth quantum-to-classical transition in that observables progress from large, quantum fluctuations to small, Gaussian fluctuations as the bath size grows. This transition is universal on a quantitative level amongst a wide range of systems, including those at finite temperature, those with itinerant particles, and random circuits. Then, we consider the case of an open system interacting noisily with an external environment. We predict the statistics of observables under largely arbitrary noise channels including those with correlated errors, allowing us to discriminate candidate error models both for continuous Hamiltonian time evolution and for digital random circuits. Ultimately our results clarify the role of ergodicity in quantum dynamics, with fundamental and practical consequences.
Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning

A new method is proposed and evaluated that improves distantly supervised learning of pattern rules for n-ary relation extraction. The new method employs knowledge from a large lexical semantic repository to guide the discovery of patterns in parsed relation mentions. It extends the induced rules to semantically relevant material outside the minimal subtree containing the shortest paths connecting the relation entities and also discards rules without any explicit semantic content. It significantly raises both recall and precision with roughly 20% f-measure boost in comparison to the baseline system which does not consider the lexical semantic information.
Decomposing Control Lyapunov Functions for Efficient Reinforcement Learning

Recent methods using Reinforcement Learning (RL) have proven to be successful for training intelligent agents in unknown environments. However, RL has not been applied widely in real-world robotics scenarios. This is because current state-of-the-art RL methods require large amounts of data to learn a specific task, leading to unreasonable costs when deploying the agent to collect data in real-world applications. In this paper, we build from existing work that reshapes the reward function in RL by introducing a Control Lyapunov Function (CLF), which is demonstrated to reduce the sample complexity. Still, this formulation requires knowing a CLF of the system, but due to the lack of a general method, it is often a challenge to identify a suitable CLF. Existing work can compute low-dimensional CLFs via a Hamilton-Jacobi reachability procedure. However, this class of methods becomes intractable on high-dimensional systems, a problem that we address by using a system decomposition technique to compute what we call Decomposed Control Lyapunov Functions (DCLFs). We use the computed DCLF for reward shaping, which we show improves RL performance. Through multiple examples, we demonstrate the effectiveness of this approach, where our method finds a policy to successfully land a quadcopter in less than half the amount of real-world data required by the state-of-the-art Soft-Actor Critic algorithm.
Quasi-metric Relativity

This is a short survey of a new type of relativistic space-time framework; the socalled quasi-metric framework. The basic geometric structure underlying quasi-metric relativity is a one-parameter family g t of Lorentzian 4-metrics parameterized by a global time function t. A unique, linear and symmetric affine connection ∇ ⋆ compatible with the family g t is defined, giving rise to equations of motion. Furthermore a quasi-metric theory of gravity, including field equations and conservation laws, is presented. It is not yet clear if this theory is viable, but it satisfies all the classical solar system tests. Moreover, in its nonmetric sector the new theory makes successful predictions where General Relativity fails or is irrelevant.
PoNQ: a Neural QEM-based Mesh Representation

Although polygon meshes have been a standard representation in geometry processing, their irregular and combinatorial nature hinders their suitability for learning-based applications. In this work, we introduce a novel learnable mesh representation through a set of local 3D sample Points and their associated Normals and Quadric error metrics (QEM) w.r. t. the underlying shape, which we denote PoNQ. A global mesh is directly derived from PoNQ by efficiently leveraging the knowledge of the local quadric errors. Besides marking the first use of QEM within a neural shape representation, our contribution guarantees both topological and geometrical properties by ensuring that a PoNQ mesh does not self-intersect and is always the boundary of a volume. Notably, our representation does not rely on a regular grid, is supervised directly by the target surface alone, and also handles open surfaces with boundaries and/or sharp features. We demonstrate the efficacy of PoNQ through a learning-based mesh prediction from SDF grids and show that our method surpasses recent state-of-the-art techniques in terms of both surface and edge-based metrics.
Locomotion Generation for a Rat Robot based on Environmental Changes via Reinforcement Learning

This research focuses on developing reinforcement learning approaches for the locomotion generation of small-size quadruped robots. The rat robot NeRmo is employed as the experimental platform. Due to the constrained volume, small-size quadruped robots typically possess fewer and weaker sensors, resulting in difficulty in accurately perceiving and responding to environmental changes. In this context, insufficient and imprecise feedback data from sensors makes it difficult to generate adaptive locomotion based on reinforcement learning. To overcome these challenges, this paper proposes a novel reinforcement learning approach that focuses on extracting effective perceptual information to enhance the environmental adaptability of small-size quadruped robots. According to the frequency of a robot's gait stride, key information of sensor data is analyzed utilizing sinusoidal functions derived from Fourier transform results. Additionally, a multifunctional reward mechanism is proposed to generate adaptive locomotion in different tasks. Extensive simulations are conducted to assess the effectiveness of the proposed reinforcement learning approach in generating rat robot locomotion in various environments. The experiment results illustrate the capability of the proposed approach to maintain stable locomotion of a rat robot across different terrains, including ramps, stairs, and spiral stairs.
Einstein locality: An ignored core element of quantum mechanics

Quantum mechanics is commonly accepted as a complete theory thanks to experimental tests of non-locality based on Bell's theorem. However, we discover that the completeness of the quantum theory practically suffered from detrimental ignorance of a core element -- Einstein locality. Without this element, important experimental results of relevance could hardly receive full understanding or were even completely misinterpreted. Here we present the discovery with a theory of Einstein locality developed to recover the completeness of quantum mechanics. The developed theory provides a unified framework to account for the results of, e.g., Bell experiments (on Bell non-locality) and double-slit experiments with entangled photons (on wave-particle duality). The theory reveals the dynamics of Bell non-locality and the principle of biased sampling in measurement in the double-slit experiments, which otherwise will be impossible tasks without introducing Einstein locality. Worse still, ignorance of this element has caused misinterpretation of observations in the double-slit experiments, leading to perplexing statements of duality violation. Einstein locality also manifests indispensability in theory by its connection to the foundations of other fundamental concepts and topics (e.g., entanglement, decoherence, and quantum measurement) and may advance quantum technology by offering a promising approach to optimizing quantum computing hardware.
Reinforcement Learning from Delayed Observations via World Models

In standard Reinforcement Learning settings, agents typically assume immediate feedback about the effects of their actions after taking them. However, in practice, this assumption may not hold true due to physical constraints and can significantly impact the performance of RL algorithms. In this paper, we focus on addressing observation delays in partially observable environments. We propose leveraging world models, which have shown success in integrating past observations and learning dynamics, to handle observation delays. By reducing delayed POMDPs to delayed MDPs with world models, our methods can effectively handle partial observability, where existing approaches achieve sub-optimal performance or even degrade quickly as observability decreases. Experiments suggest that one of our methods can outperform a naive model-based approach by up to %30. Moreover, we evaluate our methods on visual input based delayed environment, for the first time showcasing delay-aware reinforcement learning on visual observations.
Gas bubble retention and its effect on waste properties: Retention mechanisms, viscosity, and tensile and shear strengths

Several of the underground nuclear storage tanks at Hanford have been placed on a flammable gas watch list, because the waste is either known or suspected to generate, store, and episodically release flammable gases. Because retention and episodic release of flammable gases from these tanks containing radioactive waste slurries are critical safety concerns, Pacific Northwest Laboratory (PNL) is studying physical mechanisms and waste properties that contribute to the episodic gas release from these storage tanks. This study is being conducted for Westinghouse Hanford Company as part of the PNL Flammable Gas project. Previous investigations have concluded that gas bubbles are retained by the slurry or sludge that has settled at the bottom of the tanks; however, the mechanisms responsible for the retention of these bubbles are not well understood. Understanding the rheological behavior of the waste, particularly of the settled sludge, is critical to characterizing the tendency of the waste to retain gas bubbles and the dynamics of how these bubbles are released from the waste. The presence of gas bubbles is expected to affect the rheology of the sludge, specifically its viscosity and tensile and shear strengths, but essentially no literature data are available to assess the effect of bubbles. The objectives of this study were to conduct experiments and develop theories to understand better how bubbles are retained by slurries and sludges, to measure the effect of gas bubbles on the viscosity of simulated slurries, and to measure the effect of gas bubbles on the tensile and shear strengths of simulated slurries and sludges. In addition to accomplishing these objectives, this study developed correlations, based on the new experimental data, that can be used in large-scale computations of waste tank physical phenomena.