Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Hierarchical Monte Carlo Belief-State Planning

Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty. Unfortunately, online CPOMDP planning is extremely difficult in large or continuous problem domains. In many large robotic domains, hierarchical decomposition can simplify planning by using tools for low-level control given high-level action primitives (options). We introduce Constrained Options Belief Tree Search (COBeTS) to leverage this hierarchy and scale online search-based CPOMDP planning to large robotic problems. We show that if primitive option controllers are defined to satisfy assigned constraint budgets, then COBeTS will satisfy constraints anytime. Otherwise, COBeTS will guide the search towards a safe sequence of option primitives, and hierarchical monitoring can be used to achieve runtime safety. We demonstrate COBeTS in several safety-critical, constrained partially observable robotic domains, showing that it can plan successfully in continuous CPOMDPs while non-hierarchical baselines cannot.
Flexible metasurface for improving brain imaging at 7T.

PURPOSE Ultra-high field MRI offers unprecedented detail for noninvasive visualization of the human brain. However, brain imaging is challenging at 7T due to the B 1 + $$ {}_1^{+} $$ field inhomogeneity, which results in signal intensity drops in temporal lobes and a bright region in the brain center. This study aims to evaluate using a metasurface to improve brain imaging at 7T and simplify the investigative workflow. METHODS Two flexible metasurfaces comprising a periodic structure of copper strips and parallel-plate capacitive elements printed on an ultra-thin substrate were optimized for brain imaging and implemented via PCB. We considered two setups: (1) two metasurfaces located near the temporal lobes and (2) one metasurface placed near the occipital lobe. The effect of metasurface placement on the transmit efficiency and specific absorption rate was evaluated via electromagnetic simulation studies with voxelized models. In addition, their impact on signal-to-noise ratio (SNR) and diagnostic image quality was assessed in vivo for two male and one female volunteers. RESULTS Placement of metasurfaces near the regions of interest led to an increase in homogeneity of the transmit field by 5% and 10.5% in the right temporal lobe and occipital lobe for a male subject, respectively. SAR efficiency values changed insignificantly, dropping by less than 8% for all investigated setups. In vivo studies also confirmed the numerically predicted improvement in field distribution and receive sensitivity in the desired ROI. CONCLUSION Optimized metasurfaces enable homogenizing transmit field distribution in the brain at 7T. The proposed lightweight and flexible structure can potentially provide MR examination with higher diagnostic value images.
CRYSYS - Use of the Cryospheric System to Monitor Global Change in Canada: Overview and Progress

La cryosphere (neige, glace marine, glace d'eau douce, glaciers, calottes glaciaires et sol gele), un sujet assez familier aux canadiens, peut aussi servir en tant qu'indicateur des changements et de la variabilite du climat. Le programme CRYSYS, qui consiste en l'utilisation du systeme cryospherique pour observer les changements climatiques au Canada, est la contribution du gouvernement canadien et des universites canadiennes au systeme de la NASA pour l'observation de la Terre. Ce systeme utilise la teledetection, la modelisation, les etudes sur le terrain et l'integration des donnees afin de fournir une capacite amelioree pour le suivi de l'etat de la cryosphere, de meme qu'une meilleure comprehension de la variabilite et des processus cryospherique Dans cet article, nous presentons notre strategie scientifique ; nous mettons en evidence les resultats obtenus jusqu'a present, montrons certains des produits en developpement et discutons de la facon dont le travail de l'equipe contribue a l'education et au developpement de politiques.
Signal-Interference Bandpass Filters Using Resonant Transversal Filtering Sections With Asymmetrical Transfer Function Characteristics

This paper introduces a new class of planar bandpass filters (BPFs) with quasi-elliptic transfer functions obtained by cascading resonant transversal filtering sections (TFS) with asymmetric transfer function characteristics. Each TFS includes a parallel-type resonator in one of its two RF signal paths which in turn leads to two transmission zeros (TZs) without increasing the circuit size. Furthermore, it enables high-order filtering transfer functions to be realized by directly cascading multiple TFSs without the need for inter-stage impedance inverters. Theoretical examples of spectrally asymmetrical BPF responses using linear-circuit analysis illustrate the resonator-based TFS BPF concept’s operating principles. Additionally, a microstrip prototype was manufactured and measured at 1 GHz to demonstrate the proof of concept. The prototype exhibited the following RF performance characteristics: a center frequency of 1 GHz, a 3-dB bandwidth of 150 MHz, a minimum in-band insertion loss of 0.8 dB, and in-band group-delay variation: 4.2-7.5 ns.
Operation with Auxiliary Devices of Automatics U sing External Computing Resources on Trusted Control Systems

The possibility of controlling auxiliary devices of electroautomatics of technological equipment with the use of external computing resources on trusted control systems is considered. The method of identification of the user of technological equipment using a single-board computer and RFID (Radio-Frequency Identification) tags is proposed. For realisation of the specified method the structural scheme of the system of safety control for the machine tool with numerical control on the basis of single-board computer Raspberry Pi and RFID-reader is offered. Based on the structural scheme of the solution, the Architectural model of the access control system software is developed. The access control system connected to the CNC system AxiOMA Control, developed at the Department of Computer Control Systems, FGBOU VO “MSTU “STANKIN”, is implemented in the work.
Comparison of Moisture Controllers for Blueberries Based on Hydrodynamic Parameters of the Substrate

The substrate is the physical support and fundamental component for the supply and transfer of water and nutrients, it is therefore an essential component in crops. Understanding their physical characteristics is key to controlling water dynamics through irrigation. In this study, two electronic moisture controllers were designed, implemented and tested in the root zone of blueberry plants using hydrodynamic parameters of the substrate. The objective of this work is to achieve a response in the relative humidity of the substrate that follows a determined reference, keeping this variable within a recommended range for the crop, in addition to minimizing leaching. Two controllers were proposed, the first is a controller made with fuzzy logic and the second whose control action was calculated based on the cumulative squared error obtained by comparing humidity during irrigation and reference curves of the desired behavior of humidity in the substrate. Additionally, the performance of the two controllers was compared with that obtained through the traditional irrigation technique every 12 hours; the results showed a significant decrease in water consumption and improvement in the aeration capacity of the root zone.
Data-Assimilative Hindcast and Climate Forecast of Storm Surges with an ASGF Regression Model

ABSTRACT This study demonstrates that long-term climate model solutions can be efficiently converted to storm surge time series at points of interest (POIs) for the future. The all-source Green's function (ASGF) regression model is used for this conversion. In addition to being data assimilative, the ASGF regression model can also simulate storm surges at a POI faster than the traditional modelling approach by orders of magnitude. This is demonstrated using the tidal gauge at Sept-Îles (Quebec, Canada) in the Gulf of St. Lawrence as the POI. First the ASGF regression model is used to assimilate 32 years of tidal gauge data, producing a continuous hindcast of storm surges and a set of best-estimate regression parameters. Second, the ASGF regression model with the best-estimate parameters is used to convert a Canadian Regional Climate Model solution (CRCM/AHJ) to an hourly time series of storm surges from 1961 to 2100. Gumbel's extreme value analysis (EVA) is then applied to the time series as a whole and also to tri-decadal segments. The tri-decadal approach is used to investigate whether there is any progressive shortening or lengthening of storm surge return periods as a result of future climate change. A method for correcting for bias due to the forcing field at the EVA level is also demonstrated.
ChatGPT and Large Language Models in Healthcare: Opportunities and Risks

ChatGPT, a pre-trained large language model (LLM), has the potential to transform healthcare by providing valid clinical insights and reducing doctors’ workload. There are already signs that such tools can be useful for automating the generation of patient discharge reports, clinical vignettes, and radiology reports. Such tools can also capture the vast medical knowledge base as demonstrated by ChatGPT clearing the United States Medical Licensing Examination (USMLE). Such tools promise to make healthcare more accessible, scalable, and efficient, leading to better patient outcomes. However, such tools are far from perfect and well-known to be susceptible to error, misinformation, and bias. In this paper, we review the potential applications of ChatGPT in healthcare and also identify potentials risks that must be addressed before ChatGPT and other LLM tools can be safely adopted in healthcare. First, we offer case studies on using ChatGPT for passing USMLE, identifying prevention methods for cardiovascular disease, generating patient discharge reports, generating clinical vignettes, and generating radiology reports. Second, we present the opportunities that ChatGPT offers in healthcare. By leveraging its language generation and processing capabilities, ChatGPT can streamline and improve a range of healthcare tasks, from digitizing clinical notes and improving the accuracy of diagnosis to revolutionizing medical education and empowering patients with personalized healthcare information. Finally, we reflect on the associated risks and conclude that caution is advised in interpreting the results of ChatGPT as these studies are preliminary and not entirely error-free.
Feasibility, Adherence, and Effectiveness of Blended Psychotherapy for Severe Mental Illnesses: Scoping Review

Background Blended psychotherapy (bPT) combines face-to-face psychotherapy with digital interventions to enhance the effectiveness of mental health treatment. The feasibility and effectiveness of bPT have been demonstrated for various mental health issues, although primarily for patients with higher levels of functioning. Objective This scoping review aims to investigate the feasibility, adherence, and effectiveness of bPT for the treatment of patients with severe mental illnesses (SMIs). Methods Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we conducted searches in PubMed, MEDLINE, Embase, PsycINFO, and PsycArticles for studies published until March 23, 2023. Results Out of 587 screened papers, we incorporated 25 studies encompassing 23 bPT interventions, involving a total of 2554 patients with SMI. The intervention formats and research designs exhibited significant variation. Our findings offer preliminary evidence supporting the feasibility of bPT for SMI, although there is limited research on adherence. Nevertheless, the summarized studies indicated promising attrition rates, spanning from 0% to 37%, implying a potential beneficial impact of bPT on adherence to SMI treatment. The quantity of evidence on the effects of bPT for SMI was limited and challenging to generalize. Among the 15 controlled trials, 4 concluded that bPT interventions were effective compared with controls. However, it is noteworthy that 2 of these studies used the same study population, and the control groups exhibited significant variations. Conclusions Overall, our review suggests that while bPT appears promising as a treatment method, further research is necessary to establish its effectiveness for SMI. We discuss considerations for clinical implementation, directions, and future research.
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective

Adversarial Training (AT) has become arguably the state-of-the-art algorithm for extracting robust features. However, researchers recently notice that AT suffers from severe robust overfitting problems, particularly after learning rate (LR) decay. In this paper, we explain this phenomenon by viewing adversarial training as a dynamic minimax game between the model trainer and the attacker. Specifically, we analyze how LR decay breaks the balance between the minimax game by empowering the trainer with a stronger memorization ability, and show such imbalance induces robust overfitting as a result of memorizing non-robust features. We validate this understanding with extensive experiments, and provide a holistic view of robust overfitting from the dynamics of both the two game players. This understanding further inspires us to alleviate robust overfitting by rebalancing the two players by either regularizing the trainer's capacity or improving the attack strength. Experiments show that the proposed ReBalanced Adversarial Training (ReBAT) can attain good robustness and does not suffer from robust overfitting even after very long training. Code is available at https://github.com/PKU-ML/ReBAT.
Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions

Image decomposition plays a crucial role in various computer vision tasks, enabling the analysis and manipulation of visual content at a fundamental level. Overlapping images, which occur when multiple objects or scenes partially occlude each other, pose unique challenges for decomposition algorithms. The task intensifies when working with sparse images, where the scarcity of meaningful information complicates the precise extraction of components. This paper presents a solution that leverages the power of deep learning to accurately extract individual objects within multi-dimensional overlapping-sparse images, with a direct application in high-energy physics with decomposition of overlaid elementary particles obtained from imaging detectors. In particular, the proposed approach tackles a highly complex yet unsolved problem: identifying and measuring independent particles at the vertex of neutrino interactions, where one expects to observe detector images with multiple indiscernible overlapping charged particles. By decomposing the image of the detector activity at the vertex through deep learning, it is possible to infer the kinematic parameters of the identified low-momentum particles - which otherwise would remain neglected - and enhance the reconstructed energy resolution of the neutrino event. We also present an additional step - that can be tuned directly on detector data - combining the above method with a fully-differentiable generative model to improve the image decomposition further and, consequently, the resolution of the measured parameters, achieving unprecedented results. This improvement is crucial for precisely measuring the parameters that govern neutrino flavour oscillations and searching for asymmetries between matter and antimatter.
Mud-Diapirism Induced Structuration and Implications for the Definition and Mapping of Hydrocarbon Traps in Makran Accretionary Prism, Pakistan

The mobile mud coupled with tectonic forces has played an important role in shaping the structural styles as well as geomorphic features in the Makran Accretionary Prism. At surface, key structural elements associated with mud tectonics such as broad synclines, very tight anticlines, and mud volcanoes have been observed particularly in the southeastern part of the Makran Accretionary Prism. There are often associated emanating methane gas with mud volcanoes. In the subsurface, mud walls and mud diapirs have been envisaged in the cores of anticlines separated by less deformed broad synclines representing possible mud withdrawal areas. Occasionally, anticlines encircle the synclines and form dish like geomorphic feature, which appear like mini basins. The same as the ones associated with salt tectonics. These kinds of mini basins usually act as depocentres for the sediments eroded from adjacent growing anticlines as well as for pounded turbidites from regional sediment supply. It is also envisaged that the strata probably truncate against the mud walls as in analogous geological settings. Folding related fracture sets and strike slip faults have facilitated the extrusion of the mud at the surface. This phenomenon is characterized by occurrence of active as well as passive mud volcanoes in the study area. Results of the study suggest that potential hydrocarbon traps in the region of the Makran Accretionary Prism could be associated with the truncation of strata against “Mud walls” and around “Mud diapirs” with hydrocarbon accumulations at the limbs of anticlines. To date, a number of dry wells have been drilled in the Makran Accretionary Prism, mostly targeting the crests of these mud-cored anticlines, which could be one of the reasons of not finding the predicted reservoirs, let alone the hydrocarbons. Due to the same geological reason, drilling hazards while drilling through the over pressured mudstones have also led to mechanical failures in some of the wells, i.e. Dhak Anticline wells. The proposed paradigm shift in defining the hydrocarbon traps and locating the exploratory wells on these structures has significant implications for petroleum exploration in the region.
Legionella pneumophila Presence in Dental Unit Waterlines: A Cultural and Molecular Investigation in the West Bank, Palestine

A Legionella pneumophila bacterium is ubiquitous in water distribution systems, including dental unit waterlines (DUWLs). Legionellosis is atypical pneumonia, including Legionnaires’ disease (LD) and the less acute form of Pontiac fever. Legionellosis occurs as a result of inhalation/aspiration of aerosolized Legionella-contaminated water by susceptible patients, health workers, and dentists. In this study, we undertook to determine the prevalence of Legionella in water and biofilm samples from Tap and DUWLs collected from five sites of dental clinics and faculties across the West Bank. Water samples were tested for physical and chemical parameters. The study samples included 185 samples, 89 (48%) water samples, and 96 (52%) biofilm swabs, which were analyzed by cultivation-dependent analysis (CDA) and by the cultivation-independent technique (CIA). Also, partial sequencing of the 16S rRNA gene for fifteen L. pneumophila isolates was performed for quality assurance and identification. L. pneumophila was isolated from 28 (15%) of 185 samples using CDA and was detected in 142 (77%) of 185 samples using CIA. The abundance of culturable L. pneumophila was low in DUWL of the sampling sites (range: 27–115 CFU/Liter). PCR was 5× more sensitive than the culture technique. L. pneumophila Sg 1 was detected in (75%) of the isolates, while (25%) isolates were L. pneumophila Sg 2–14. All fifteen sequenced Legionella isolates were identified as L. pneumophila ≥ 94.5%. The analysis of phylogenetic tree showed that L. pneumophila branch clearly identified and distinguished from other branches. These results show that DUWLs of the examined dental clinics and faculties are contaminated with L. pneumophila. This finding reveals a serious potential health risk for infection of immunocompromised patients and dentists’ post-exposure.
Trend to equilibrium and diffusion limit for the inertial Kuramoto-Sakaguchi equation

In this paper, we study the inertial Kuramoto-Sakaguchi equation for interacting oscillatory systems. On the one hand, we prove the convergence toward corresponding phasehomogeneous stationary states in weighted Lebesgue norm sense when the coupling strength is small enough. In [10], it is proved that when the noise intensity is sufficiently large, equilibrium of the inertial Kuramoto-Sakaguchi equation is asymptotically stable. For generic initial data, every solutions converges to equilibrium in weighted Sobolev norm sense. We improve this previous result by showing the convergence for a larger class of functions and by providing a simpler proof. On the other hand, we investigate the diffusion limit when all oscillators are identical. In [19], authors studied the same problem using an energy estimate, renormlized solutions and a compactness method, through which error estimates could not be discussed. Here we provide error estimates for the diffusion limit with respect to the mass m $\ll$ 1 using a simple proof by imposing slightly more regularity on the solution.
Towards Practical Non-Adversarial Distribution Alignment via Variational Bounds

Distribution alignment can be used to learn invariant representations with applications in fairness and robustness. Most prior works resort to adversarial alignment methods but the resulting minimax problems are unstable and challenging to optimize. Non-adversarial likelihood-based approaches either require model invertibility, impose constraints on the latent prior, or lack a generic framework for alignment. To overcome these limitations, we propose a non-adversarial VAE-based alignment method that can be applied to any model pipeline. We develop a set of alignment upper bounds (including a noisy bound) that have VAE-like objectives but with a different perspective. We carefully compare our method to prior VAE-based alignment approaches both theoretically and empirically. Finally, we demonstrate that our novel alignment losses can replace adversarial losses in standard invariant representation learning pipelines without modifying the original architectures -- thereby significantly broadening the applicability of non-adversarial alignment methods.
An Implementation of Multimodal Fusion System for Intelligent Digital Human Generation

With the rapid development of artificial intelligence (AI), digital humans have attracted more and more attention and are expected to achieve a wide range of applications in several industries. Then, most of the existing digital humans still rely on manual modeling by designers, which is a cumbersome process and has a long development cycle. Therefore, facing the rise of digital humans, there is an urgent need for a digital human generation system combined with AI to improve development efficiency. In this paper, an implementation scheme of an intelligent digital human generation system with multimodal fusion is proposed. Specifically, text, speech and image are taken as inputs, and interactive speech is synthesized using large language model (LLM), voiceprint extraction, and text-to-speech conversion techniques. Then the input image is age-transformed and a suitable image is selected as the driving image. Then, the modification and generation of digital human video content is realized by digital human driving, novel view synthesis, and intelligent dressing techniques. Finally, we enhance the user experience through style transfer, super-resolution, and quality evaluation. Experimental results show that the system can effectively realize digital human generation. The related code is released at https://github.com/zyj-2000/CUMT_2D_PhotoSpeaker.