Miracle-dz/UniFAR-FTUs huggingface dataset:
A computational model that predicts behavioral sensitivity to intracortical microstimulation

Objective. Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. Approach. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Main results. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R2 = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber’s law. Significance. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.
New perspectives for the treatment of pulmonary hypertension

Pulmonary hypertension (PH) is a debilitating disease with a poor prognosis. Therapeutic options remain limited despite the introduction of prostacyclin analogues, endothelin receptor antagonists and phosphodiesterase 5 inhibitors within the last 15 years; these interventions address predominantly the endothelial and vascular dysfunctionS associated with the condition, but simply delay progression of the disease rather than offer a cure. In an attempt to improve efficacy, emerging approaches have focused on targeting the pro‐proliferative phenotype that underpins the pulmonary vascular remodelling in the lung and contributes to the impaired circulation and right heart failure. Many novel targets have been investigated and validated in animal models of PH, including modulation of guanylate cyclases, phosphodiesterases, tyrosine kinases, Rho kinase, bone morphogenetic proteins signalling, 5‐HT, peroxisome proliferator activator receptors and ion channels. In addition, there is hope that combinations of such treatments, harnessing and optimizing vasodilator and anti‐proliferative properties, will provide a further, possibly synergistic, increase in efficacy; therapies directed at the right heart may also offer an additional benefit. This overview highlights current therapeutic options, promising new therapies, and provides the rationale for a combination approach to treat the disease.
Effect of governance on management in public project

Project governance (PG) is a newborn theory and it plays a more and more important role in the improvement of public project management performance (PPMP). The purpose of this paper is to explore the effect of PG on project management (PM) which has approved positive effect on PPMP. Therefore, the relationship between PG and PM in public project is discussed, and then the mechanism of PG affecting PM in public project is analyzed theoretically. The findings show that PG and PM belong to the different research paradigms and PG can optimize the process of PM through its three basic functions, i.e. incenting, restricting and harmonizing. We hope that this study is helpful to the understanding of GMP framework presented in our previous research and then can bring public projects into success.
Adaptive control of discrete-time nonlinear systems using recurrent neural networks

A learning and adaptive control scheme for a general class of unknown MIMO discrete-time nonlinear systems using multilayered recurrent neural networks (MRNNs) is presented. A novel MRNN structure is proposed to approximate the unknown nonlinear input-output relationship, using a dynamic back propagation (DBP) learning algorithm. Based on the dynamic neural model, an extension of the concept of the input-output linearisation of discrete-time nonlinear systems is used to synthesise a control technique for model reference control purposes. A dynamic learning control architecture is developed with simultaneous online identification and control. The potentials of the proposed methods are demonstrated by simulation studies.
Distinct frontal and amygdala correlates of change detection for facial identity and expression

Recruitment of ‘top-down’ frontal attentional mechanisms is held to support detection of changes in task-relevant stimuli. Fluctuations in intrinsic frontal activity have been shown to impact task performance more generally. Meanwhile, the amygdala has been implicated in ‘bottom-up’ attentional capture by threat. Here, 22 adult human participants took part in a functional magnetic resonance change detection study aimed at investigating the correlates of successful (vs failed) detection of changes in facial identity vs expression. For identity changes, we expected prefrontal recruitment to differentiate ‘hit’ from ‘miss’ trials, in line with previous reports. Meanwhile, we postulated that a different mechanism would support detection of emotionally salient changes. Specifically, elevated amygdala activation was predicted to be associated with successful detection of threat-related changes in expression, over-riding the influence of fluctuations in top-down attention. Our findings revealed that fusiform activity tracked change detection across conditions. Ventrolateral prefrontal cortical activity was uniquely linked to detection of changes in identity not expression, and amygdala activity to detection of changes from neutral to fearful expressions. These results are consistent with distinct mechanisms supporting detection of changes in face identity vs expression, the former potentially reflecting top-down attention, the latter bottom-up attentional capture by stimulus emotional salience.
Chronic Disabling Fatigue at Age 13 and Association With Family Adversity

OBJECTIVES: 1) Estimate the prevalence of chronic disabling fatigue at age 13, and 2) investigate associations with early family adversity, using a population-based birth cohort study. METHODS: The subjects were 10 001 children, of whom 5657 had sufficient data to define chronic disabling fatigue at age 13. Chronic disabling fatigue was defined as tiredness reported by mothers that had lasted for ≥3 months or ≥6 months, was disabling (school absence or prevented participation in hobbies/sport/leisure activities “quite a lot” or a “great deal”), and not due to another cause. Family adversity was defined by using 14 questions addressing housing, education, social relationships, and maternal health assessed prospectively at birth. We used multiple imputation to correct estimates of prevalence and association for loss to follow-up. RESULTS: Overall, 1995 teenagers had been tired for the last month, of whom 117 and 53 had chronic disabling fatigue of ≥3 and ≥6 months’ duration, respectively. The estimated prevalence of chronic disabling fatigue of ≥3 and ≥6 months’ duration was 2.07% and 0.94%, respectively, but increased to 2.44% and 1.30%, respectively, after imputing missing data. Prevalence was similar in boys and girls. The risk of chronic disabling fatigue increased with family adversity score (odds ratios: 1.25 [95% confidence interval: 1.09, 1.43] and 1.23 [1.01, 1.51] for ≥3 and ≥6 months’ duration, respectively). Only 36 (30.77%) children with chronic disabling fatigue had consulted a doctor about their fatigue. CONCLUSIONS: Chronic disabling fatigue is more common in families who experienced early family adversity and is often not reported to health care services.
Functional Implications of Plasma Membrane Condensation for T Cell Activation

The T lymphocyte plasma membrane condenses at the site of activation but the functional significance of this receptor-mediated membrane reorganization is not yet known. Here we demonstrate that membrane condensation at the T cell activation sites can be inhibited by incorporation of the oxysterol 7-ketocholesterol (7KC), which is known to prevent the formation of raft-like liquid-ordered domains in model membranes. We enriched T cells with 7KC, or cholesterol as control, to assess the importance of membrane condensation for T cell activation. Upon 7KC treatment, T cell antigen receptor (TCR) triggered calcium fluxes and early tyrosine phosphorylation events appear unaltered. However, signaling complexes form less efficiently on the cell surface, fewer phosphorylated signaling proteins are retained in the plasma membrane and actin restructuring at activation sites is impaired in 7KC-enriched cells resulting in compromised downstream activation responses. Our data emphasizes lipids as an important medium for the organization at T cell activation sites and strongly indicates that membrane condensation is an important element of the T cell activation process.
Implicit Active Constraints for robot-assisted arthroscopy

This paper presents an Implicit Active Constraints control framework for robot-assisted minimally invasive surgery. It extends on current frameworks by prescribing the external constraints implicitly from the operator motion, forgoing the need for pre-operative imaging; the constraints are defined in situ so as to avoid the use of invasive fiducial markers. A hands-on cooperatively-controlled robotic platform, comprising of a surgical instrument and a compliant manipulator, has been designed for an arthroscopic procedure. The surgical platform is capable of constraining the pose of the instrument so as to ensure it passes through the incision point and does not cause trauma to the surrounding tissue. A flexible arthroscopic instrument is designed and its use is investigated to enlarge reachable and dexterous workspace, increasing the accessibility to the target anatomy. The behaviour of the flexible instrument is analysed. A detailed performance analysis is conducted on a group of subjects for validating the control framework, simulating a minimally invasive arthroscopic procedure. Results demonstrate a statistically significant enhancement in the control ergonomics as well as the accuracy and safety of the procedure.
Direct cleavage of the human DNA fragmentation factor‐45 by granzyme B induces caspase‐activated DNase release and DNA fragmentation

The protease granzyme B (GrB) plays a key role in the cytocidal activity during cytotoxic T lymphocyte (CTL)‐mediated programmed cell death. Multiple caspases have been identified as direct substrates for GrB, suggesting that the activation of caspases constitutes an important event during CTL‐induced cell death. However, recent studies have provided evidence for caspase‐independent pathway(s) during CTL‐mediated apoptosis. In this study, we demonstrate caspase‐independent and direct cleavage of the 45 kDa unit of DNA fragmentation factor (DFF45) by GrB both in vitro and in vivo. Using a novel and selective caspase‐3 inhibitor, we show the ability of GrB to process DFF45 directly and mediate DNA fragmentation in the absence of caspase‐3 activity. Furthermore, studies with DFF45 mutants reveal that both caspase‐3 and GrB share a common cleavage site, which is necessary and sufficient to induce DNA fragmentation in target cells during apoptosis. Together, our data suggest that CTLs possess alternative mechanism(s) for inducing DNA fragmentation without the requirement for caspases.
Space Efficient Evaluation of ASP Programs with Bounded Predicate Arities

Answer Set Programming (ASP) has been deployed in many applications, thanks to the availability of efficient solvers. Most programs encountered in practice have an important property: Their predicate arities are bounded by a constant, and in this case it is known that the relevant computations can be done using polynomial space. However, all competitive ASP systems rely on grounding, due to which they may use exponential space for these programs. We present three evaluation methods that respect the polynomial space bound and a generic framework architecture for realization. Experimental results for a prototype implementation indicate that the methods are effective. They show not only benign space consumption, but interestingly also good runtime compared to some state of the art ASP solvers.
Imitation and Reinforcement Learning with Heterogeneous Actions

We study the problem of accelerating reinforcement learning through the observation and implicit imitation of expert agents (mentors) acting in the same domain. In this paper, we consider problems that arise when the learner and mentor have heterogeneous actions. We extend an earlier implicit imitation model to allow for feasibility testing (determining whether a specific mentor action can be duplicated) and repair (discovering a “plan” that simulates a mentor’s trajectory) and demonstrate empirically that both of these components allow learning agents to learn much more readily than standard RL agents and implicit imitation agents without these extended capabilities.
Noise and Error Prediction for Neural Networks

In this paper, we are studying the application of a neural network to a problem related to autonomous car driving. In particular, we are interested in the correlation between noise in the data and the average error that can be achieved by the neural network, and study the issue using several data collections. We propose a measure of the noise that can indicate issues in the collected data and can help improve the learning process. We are also interested in ways to use symmetry in the data for this particular problem and analyze how we can take advantage of it for a better training of the network.
Epilepsy, hippocampal sclerosis and febrile seizures linked by common genetic variation around SCN1A

Epilepsy comprises several syndromes, amongst the most common being mesial temporal lobe epilepsy with hippocampal sclerosis. Seizures in mesial temporal lobe epilepsy with hippocampal sclerosis are typically drug-resistant, and mesial temporal lobe epilepsy with hippocampal sclerosis is frequently associated with important co-morbidities, mandating the search for better understanding and treatment. The cause of mesial temporal lobe epilepsy with hippocampal sclerosis is unknown, but there is an association with childhood febrile seizures. Several rarer epilepsies featuring febrile seizures are caused by mutations in SCN1A, which encodes a brain-expressed sodium channel subunit targeted by many anti-epileptic drugs. We undertook a genome-wide association study in 1018 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 7552 control subjects, with validation in an independent sample set comprising 959 people with mesial temporal lobe epilepsy with hippocampal sclerosis and 3591 control subjects. To dissect out variants related to a history of febrile seizures, we tested cases with mesial temporal lobe epilepsy with hippocampal sclerosis with (overall n = 757) and without (overall n = 803) a history of febrile seizures. Meta-analysis revealed a genome-wide significant association for mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures at the sodium channel gene cluster on chromosome 2q24.3 [rs7587026, within an intron of the SCN1A gene, P = 3.36 × 10−9, odds ratio (A) = 1.42, 95% confidence interval: 1.26–1.59]. In a cohort of 172 individuals with febrile seizures, who did not develop epilepsy during prospective follow-up to age 13 years, and 6456 controls, no association was found for rs7587026 and febrile seizures. These findings suggest SCN1A involvement in a common epilepsy syndrome, give new direction to biological understanding of mesial temporal lobe epilepsy with hippocampal sclerosis with febrile seizures, and open avenues for investigation of prognostic factors and possible prevention of epilepsy in some children with febrile seizures.
Real-Time Opaque and Semi-Transparent TV Logos Detection

1 One of the most important strategies created by TV broadcast stations to claim and protect video content ownership is the TV logo. With different colors, shapes and styles, TV logos identify the broadcast station, and sometimes even the kind of the broadcasted program. Consequently, the detection of TV logos is useful in applications ranging from detection of TV commercials to audience measure. In this paper, we propose an improved edgebased template matching to detect opaque, semitransparent and partially animated logos. The proposed algorithm could be implemented in a DSP for real-time logo recognition, because it is economic in memory use and requires low processing power. We tested during more than 24 hours our implementation to recognize logos of Brazilian and American TV channels and achieved 100% of correct recognition rate.
IR Evaluation: Designing an End-to-End Offline Evaluation Pipeline

This tutorial aims to provide attendees with a detailed understanding of end-to-end evaluation pipeline based on human judgments (offline measurement). The tutorial will give an overview of the state of the art methods, techniques, and metrics necessary for each stage of evaluation process. We will mostly focus on evaluating an information retrieval (search) system, but the other tasks such as recommendation and classification will also be discussed. Practical examples will be drawn both from the literature and from real world usage scenarios in industry.
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation

We present LS-CRF, a new method for very efficient large-scale training of Conditional Random Fields (CRFs). It is inspired by existing closed-form expressions for the maximum likelihood parameters of a generative graphical model with tree topology. LS-CRF training requires only solving a set of independent regression problems, for which closed-form expression as well as efficient iterative solvers are available. This makes it orders of magnitude faster than conventional maximum likelihood learning for CRFs that require repeated runs of probabilistic inference. At the same time, the models learned by our method still allow for joint inference at test time. We apply LS-CRF to the task of semantic image segmentation, showing that it is highly efficient, even for loopy models where probabilistic inference is problematic. It allows the training of image segmentation models from significantly larger training sets than had been used previously. We demonstrate this on two new datasets that form a second contribution of this paper. They consist of over 180,000 images with figure-ground segmentation annotations. Our large-scale experiments show that the possibilities of CRF-based image segmentation are far from exhausted, indicating, for example, that semi-supervised learning and the use of non-linear predictors are promising directions for achieving higher segmentation accuracy in the future.
Objective probabilities, quantum counterfactuals, and the ABL rule—A response to R. E. Kastner

The ABL rule is derived as a tool of standard quantum mechanics. The ontological significance of the existence of objective probabilities is discussed. Objections by Kastner [Am. J. Phys. 69, 860–863 (2001)] and others to counterfactual uses of the ABL rule are refuted. Metaphysical presumptions leading to such views as Kastner is defending in her Comment are examined and shown to be unwarranted.
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering

We introduce an algorithm for unsupervised co-segmentation of a set of shapes so as to reveal the semantic shape parts and establish their correspondence across the set. The input set may exhibit significant shape variability where the shapes do not admit proper spatial alignment and the corresponding parts in any pair of shapes may be geometrically dissimilar. Our algorithm can handle such challenging input sets since, first, we perform co-analysis in a descriptor space, where a combination of shape descriptors relates the parts independently of their pose, location, and cardinality. Secondly, we exploit a key enabling feature of the input set, namely, dissimilar parts may be "linked" through third-parties present in the set. The links are derived from the pairwise similarities between the parts' descriptors. To reveal such linkages, which may manifest themselves as anisotropic and non-linear structures in the descriptor space, we perform spectral clustering with the aid of diffusion maps. We show that with our approach, we are able to co-segment sets of shapes that possess significant variability, achieving results that are close to those of a supervised approach.
An Epistemic Formulation of Information Flow Analysis

Most accounts of information flow security in programming languages emphasize non-interference to characterize security: in a secure program, changes to high-security inputs do not alter the values of low-security outputs. The definition of non-interference is incompatible with declassification, which allows some low-security outputs to be influenced by high-security inputs. We propose an alternative account of information flow based on an epistemic logic of computational effects. Rather than view a program as a function from inputs to outputs, we instead embrace the principle that information flow security is concerned with the effects a program has on its execution environment. These effects are modelled using a substructural epistemic logic that tracks the flow of knowledge gained by principals and communication channels during execution. Confidentiality is expressed by proving necessary conditions for a principal to know a sensitive fact at the end of an execution. In the simplest case the necessary condition is falsehood, which means that a principal cannot know a secret as a result of a well-typed execution of a program. In the presence of declassification a necessary condition for disclosure is the existence of a proof of authorization in a formal authorization logic, expressing that sensitive data is disclosed only when explicitly authorized. Rather than taken as the primary result, the classical non-interference property arises in the proof of adequacy of the epistemic theory of disclosure, ensuring that it accurately models program behavior. It is suggested that an epistemic account of information flow security is both more natural and more expressive than classical accounts based only on non-interference.
A Wavelet-based Encoding for Neuroevolution

A new indirect scheme for encoding neural network connection weights as sets of wavelet-domain coefficients is proposed in this paper. It exploits spatial regularities in the weight-space to reduce the gene-space dimension by considering the low-frequency wavelet coefficients only. The wavelet-based encoding builds on top of a frequency-domain encoding, but unlike when using a Fourier-type transform, it offers gene locality while preserving continuity of the genotype-phenotype mapping. We argue that this added property allows for more efficient evolutionary search and demonstrate this on the octopus-arm control task, where superior solutions were found in fewer generations. The scalability of the wavelet-based encoding is shown by evolving networks with many parameters to control game-playing agents in the Arcade Learning Environment.