Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Acupuncture and Rehabilitation Therapy on the Recovery of Neurological Function and Prognosis of Stroke Patients

Background Stroke is a common cerebrovascular disease among the middle-aged and elderly, which can lead to a series of neurological disorders. Acupuncture is an important part of traditional Chinese medicine, with great value in improving the neurological deficits of stroke patients. In addition, rehabilitation therapy is also of great significance for alleviating the neurological deficits of patients and improving their activities of daily living. Objective To explore the effect of acupuncture and moxibustion combined with rehabilitation therapy on the recovery of neurological function and prognosis of stroke patients. Methods The case data of 100 stroke patients treated in the Wuhan Hospital of Traditional Chinese Medicine from January 2019 to July 2021 were analyzed retrospectively. According to the treatment plan patients received, they were divided into the following two groups: an observation group (n = 52) treated with acupuncture combined with rehabilitation therapy and a control group (n = 48) treated with rehabilitation therapy alone. The two groups were compared in terms of the following items: therapeutic efficacy, plasma levels of cortisol (Cor) and neuropeptide Y (NPY), nerve function, motor function, balance ability, self-care ability, swallowing function, negative emotions, and quality of life. Results The therapeutic effect of the observation group was significantly higher than that of the control group (P < 0.05). The levels of Cor and NPY, as well as the neurological function, motor function, balance ability, self-care ability, swallowing function, and negative emotions, were not significantly different between the two groups before treatment (P > 0.05). While after intervention, all the above indexes improved in both groups, with better improvements in the observation group compared with the control group (P < 0.05). And the various dimensions concerning the quality of life of patients were also significantly better in the observation group when compared with the control group. Conclusion Acupuncture of traditional Chinese medicine combined with rehabilitation therapy has outstanding effects in stroke treatment and can effectively improve the neurological function, prognosis, and quality of life of patients, which is worthy of clinical promotion.
Ontology Representation: Design Patterns and Ontologies That Make Sense

As the (in)famous definition states: 'An ontology is an explicit specification of a conceptualization'. However, an ontology is also a philosophical theory of existence, a knowledge management resource, a database schema, or a type of knowledge representation artefact on the semantic web. Over the years the term 'ontology' has been used in so many different ways that one can no longer be sure what is meant by it at any given occasion. This book clarifies the role ontologies play in knowledge representation; it discusses the distinctions with their use in philosophy, gives insight in the features, rationale and limitations of the OWL 2 web ontology language, and provides a critical review of methodologies and design principles advocated to improve the quality of ontologies. It covers both theory and practice of knowledge acquisition, representation and ontologies; it emphasises human understanding as knowledge structuring principle, and demonstrates this approach in the development of a core ontology of basic legal concepts (LKIF Core) and in the exploration of expressive ontology design patterns for the representation of social reality, change and causation, actions and transactions. In doing so it contributes to a better understanding of the representation of ontologies; or rather, what it means to do ontology representation.IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences
Characterization of Early Stage Parkinson's Disease From Resting-State fMRI Data Using a Long Short-Term Memory Network

Parkinson's disease (PD) is a common and complex neurodegenerative disorder with five stages on the Hoehn and Yahr scaling. Characterizing brain function alterations with progression of early stage disease would support accurate disease staging, development of new therapies, and objective monitoring of disease progression or treatment response. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. While fMRI and FC data have been utilized for diagnosis of PD through application of machine learning approaches such as support vector machine and logistic regression, the characterization of FC changes in early-stage PD has not been investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to distinguish the early stages of PD and understand related functional brain changes. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative (PPMI), the largest-available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method and 11.56% higher than a CNN model, indicating significantly better robustness and accuracy compared with other machine learning classifiers. Finally, we used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD.
Data-Driven Safety Verification for Legged Robots

Planning safe motions for legged robots requires sophisticated safety verification tools. However, designing such tools for such complex systems is challenging due to the nonlinear and high-dimensional nature of these systems' dynamics. In this letter, we present a probabilistic verification framework for legged systems, which evaluates the safety of planned trajectories by learning an assessment function from trajectories collected from a closed-loop system. Our approach does not require an analytic expression of the closed-loop dynamics, thus enabling safety verification of systems with complex models and controllers. Our framework consists of an offline stage that initializes a safety assessment function by simulating a nominal model and an online stage that adapts the function to address the sim-to-real gap. The performance of the proposed approach for safety verification is demonstrated using a quadruped balancing task and a humanoid reaching task. The results demonstrate that our framework accurately predicts the systems' safety both at the planning phase to generate robust trajectories and at execution phase to detect unexpected external disturbances.
Direct diagonalization method for a few particles trapped in harmonic potentials

We describe a procedure to systematically improve direct diagonalization results for few-particle systems trapped in one-dimensional harmonic potentials interacting by contact interactions. We start from the two-body problem to deﬁne a renormalization method for the interparticle interactions. The procedure is benchmarked with state-of-the-art numerical results for three and four symmetric fermions.
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network

Generative Adversarial Networks have been employed successfully to generate high-resolution augmented images of size 1024^2. Although the augmented images generated are unprecedented, the training time of the model is exceptionally high. Conventional GAN requires training of both Discriminator as well as the Generator. In Progressive GAN, which is the current state-of-the-art GAN for image augmentation, instead of training the GAN all at once, a new concept of progressing growing of Discriminator and Generator simultaneously, was proposed. Although the lower stages such as 4x4 and 8x8 train rather quickly, the later stages consume a tremendous amount of time which could take days to finish the model training. In our paper, we propose a novel pipeline that combines Progressive GAN with slight modifications and Super Resolution GAN. Super Resolution GAN up samples low-resolution images to high-resolution images which can prove to be a useful resource to reduce the training time exponentially.
Learning Multitask Gaussian Process Over Heterogeneous Input Domains

Multitask Gaussian process (MTGP) is a well-known nonparametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs are usually limited to the multitask scenario defined in the same input domain, leaving no space for tackling the heterogeneous case, i.e., the features of input domains vary over tasks. To this end, this article presents a novel heterogeneous stochastic variational linear model of coregionalization (HSVLMC) model for simultaneously learning the tasks with varied input domains. Particularly, we develop the stochastic variational framework with Bayesian calibration that: 1) infers posterior domain mappings to consider the effect of dimensionality reduction raised by domain mappings for achieving effective input alignment and 2) employs a residual modeling strategy to leverage the inductive bias brought by prior domain mappings for better-model inference. Finally, the superiority of the proposed model against existing heterogeneous LMC models has been extensively verified on diverse heterogeneous multitask cases and a practical multifidelity steam turbine exhaust case.
An initial alignment between neural network and target is needed for gradient descent to learn

This paper introduces the notion of ``Initial Alignment'' (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent on a fully connected network with normalized i.i. d. initialization will not learn in polynomial time. Thus a certain amount of knowledge about the target (measured by the INAL) is needed in the architecture design. This also provides an answer to an open problem posed in [AS20]. The results are based on deriving lower-bounds for descent algorithms on symmetric neural networks without explicit knowledge of the target function beyond its INAL.
Hybrid Robotic Grasping With a Soft Multimodal Gripper and a Deep Multistage Learning Scheme

Grasping has long been considered an important and practical task in robotic manipulation. Yet achieving robust and efficient grasps of diverse objects is challenging, since it involves gripper design, perception, control, and learning, etc. Recent learning-based approaches have shown excellent performance in grasping a variety of novel objects. However, these methods either are typically limited to one single grasping mode or else more end effectors are needed to grasp various objects. In addition, gripper design and learning methods are commonly developed separately, which may not adequately explore the ability of a multimodal gripper. In this article, we present a deep reinforcement learning (DRL) framework to achieve multistage hybrid robotic grasping with a new soft multimodal gripper. A soft gripper with three grasping modes (i.e., enveloping, sucking, and enveloping_then_sucking) can both deal with objects of different shapes and grasp more than one object simultaneously. We propose a novel hybrid grasping method integrated with the multimodal gripper to optimize the number of grasping actions. We evaluate the DRL framework under different scenarios (i.e., with different ratios of objects of two grasp types). The proposed algorithm is shown to reduce the number of grasping actions (i.e., enlarge the grasping efficiency, with maximum values of 161.0% in simulations, and 153.5% in real-world experiments) compared to single grasping modes.
New Antenna Selection Schemes for Full-Duplex Cooperative MIMO-NOMA Systems

In this paper, we address the antenna selection (AS) problem in full-duplex (FD) cooperative non-orthogonal multiple access (NOMA) systems, where a multi-antenna FD relay bridges the connection between the multi-antenna base station and NOMA far user. Specifically, two AS schemes, namely max-U1 and max-U2, are proposed to maximize the end-to-end signal-to-interference-plus-noise ratio at either or both near and far users, respectively. Moreover, a two-stage AS scheme, namely quality-of-service (QoS) provisioning scheme, is designed to realize a specific rate at the far user while improving the near user’s rate. To enhance the performance of the QoS provisioning AS scheme, the idea of dynamic antenna clustering is applied at the relay to adaptively partition the relay’s antennas into transmit and receive subsets. The proposed AS schemes’ exact outage probability and achievable rate expressions are derived. To provide more insight, closed-form asymptotic outage probability expressions for the max-U1 and max-U2 AS schemes are obtained. Our results show that while the QoS provisioning AS scheme can deliver a near-optimal performance for static antenna setup at the relay, it provides up to 12% average sum rate gain over the optimum AS selection with fixed antenna setup.
Assessing the quality of relaxation-time approximations with fully automated computations of phonon-limited mobilities

The mobility of carriers, as limited by their scattering with phonons, can now routinely be obtained from ﬁrst-principles electron-phonon coupling calculations. However, so far, most computations have relied on some form of simpliﬁcation of the linearized Boltzmann transport equation based on either the self-energy, the momentum- or constant relaxation time approximations. Here, we develop a high-throughput infrastructure and an automatic workﬂow and we compute 69 phonon-limited mobilities in semiconductors. We compare the results resorting to the approximations with the exact iterative solution. We conclude that the approximate values may deviate signiﬁcantly from the exact ones and are thus not reliable. Given the minimal computational overhead, our work encourages to rely on this exact iterative solution and warns on the possible inaccuracy of earlier results reported using relaxation time approximations.
Alternative Metrics to Select Motors for Quasi-Direct Drive Actuators

Robotic systems for legged locomotion -- including legged robots, exoskeletons, and prosthetics -- require actuators with low inertia and high output torque. Traditionally, motors have been selected for these applications by maximizing the motor gap radius. We present alternative metrics for motor selection that are invariant to transmission ratio. The proposed metrics reward minimizing the motor inertia while maximizing the torque and motor constants without special consideration for gap radius, providing a better balance of properties for legged locomotion applications. We rigorously characterize the T-Motor RI50 and demonstrate the use of the metrics by comparing the RI50 to the widely-used T-Motor U8 as a case study.
Probabilistic Data Association for Semantic SLAM at Scale

With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Previously, SLAM was carried out using lower level geometric features (points, lines, and planes) which are often view-point dependent and error prone in visually repetitive environments. Semantic information can improve the ability to recognise previously visited locations, as well as maintain sparser maps for long term SLAM applications. However, SLAM in repetitive environments has the critical problem of assigning measurements to the landmarks which generated them. In this paper, we use k-best assignment enumeration to compute marginal assignment probabilities for each measurement landmark pair, in real time. We present numerical studies on the KITTI dataset to demonstrate the effectiveness and speed of the proposed framework.
Measuring Shocks to Central Bank Independence using Legal Rulings

We investigate the consequences of legal rulings on the conduct of monetary policy. Several unconventional monetary policy measures of the European Central Bank have come under scrutiny before national courts and the European Court of Justice. These lawsuits have the potential to severely impact the scope and flexibility of central bank policies, and central bank independence in a wide sense, with important consequences for the real and financial economy. Since the number of relevant legal challenges is small, we develop an econometric approach that searches for minimum variance regimes which we use to isolate and measure the effects of these events. Our results suggest that legal rulings addressing central bank policies have a powerful effect on financial markets. Expansionary shocks ease financial conditions along various dimensions, and inflation swap reactions suggest inflationary pressures with stronger effects in the short term.
Koopman Spectral Analysis of Intermittent Dynamics in Complex Systems: A Case Study in Pathophysiological Processes of Obstructive Sleep Apnea

Complex systems, such as pathophysiological processes, commonly exhibit chaotic, nonlinear, and intermittent phenomena. Koopman operator theory and Hankel alternative view of Koopman (HAVOK) model have been widely used to decompose the chaos of the complex system dynamics into an intermittent forced linear system. Although the statistics of the intermittent forcing have been proposed to characterize intermittencies in the HAVOK model, they were not adequate to attribute for the mode switching of nonlinear dynamics and the fat-tailed nonGaussian distribution originated from high-frequency bursts and rarely-observed intermittent forcing. The paper proposed a new intermittency dynamics analysis approach to characterize the intermittent phases, chaotic bursts, and local spectraltemporal properties of various intermittent dynamics modes using spectral decomposition and wavelet analysis. To validate our methods, the intermittency behavior of apneic events in obstructive sleep apnea disorder was selected as the case, in which heart rate variability (HRV) features were extracted. Next, we constructed the Hankel matrix from the HRV features and obtained the last eigen time-delay coordinate by singular value decomposition of the Hankel matrix, which was modeled as an intermittent forcing input. The statistics of the forcing in OSA demonstrated the fat-tailed distribution of the intermittent forcing, which correspond to the intermittency of the underlying OSA pathophysiological process. The pooled means and standard deviations of the burst duration and the inter-burst duration across OSA patients were also calculated to be minutes and minutes. Scalogram amplitude and spectral decomposition of the wavelet transform exhibited various predominant frequencies and dynamics modes associated with apneic events.
Automatically Mitigating Vulnerabilities in x86 Binary Programs via Partially Recompilable Decompilation

Vulnerabilities are challenging to locate and repair, especially when source code is unavailable and binary patching is required. Manual methods are time-consuming, require significant expertise, and do not scale to the rate at which new vulnerabilities are discovered. Automated methods are an attractive alternative, and we propose Partially Recompilable Decompilation (PRD). PRD lifts suspect binary functions to source, available for analysis, revision, or review, and creates a patched binary using source- and binary-level techniques. Although decompilation and recompilation do not typically work on an entire binary, our approach succeeds because it is limited to a few functions, like those identified by our binary fault localization. We evaluate these assumptions and find that, without any grammar or compilation restrictions, 70-89% of individual functions are successfully decompiled and recompiled with sufficient type recovery. In comparison, only 1.7% of the full C-binaries succeed. When decompilation succeeds, PRD produces test-equivalent binaries 92.9% of the time. In addition, we evaluate PRD in two contexts: a fully automated process incorporating source-level Automated Program Repair (APR) methods; human-edited source-level repairs. When evaluated on DARPA Cyber Grand Challenge (CGC) binaries, we find that PRD-enabled APR tools, operating only on binaries, performs as well as, and sometimes better than full-source tools, collectively mitigating 85 of the 148 scenarios, a success rate consistent with these same tools operating with access to the entire source code. PRD achieves similar success rates as the winning CGC entries, sometimes finding higher-quality mitigations than those produced by top CGC teams. For generality, our evaluation includes two independently developed APR tools and C++, Rode0day, and real-world binaries.
Structure-aware unsupervised tagged-to-cine MRI synthesis with self disentanglement

Cycle reconstruction regularized adversarial training—e.g., CycleGAN, DiscoGAN, and DualGAN—has been widely used for image style transfer with unpaired training data. Several recent works, however, have shown that local distortions are frequent, and structural consistency cannot be guaranteed. Targeting this issue, prior works usually relied on additional segmentation or consistent feature extraction steps that are task-specific. To counter this, this work aims to learn a general add-on structural feature extractor, by explicitly enforcing the structural alignment between an input and its synthesized image. Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities. The translator and structure encoder are updated, following an alternating training protocol. In addition, the information w.r. t. imaging modality can be eliminated with an asymmetric adversarial game. We train, validate, and test our network on 1,768, 416, and 1,560 unpaired subject-independent slices of tagged and cine magnetic resonance imaging from a total of twenty healthy subjects, respectively, demonstrating superior performance over competing methods.