Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable functional specialization emerges in deep convolutional networks trained on brain signals

Objective. Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain–computer interfacing (BCI) task. Approach. We trained CNNs to predict hand movement speed from intracranial electroencephalography (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal. Main results. We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations. Significance. We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.
Accelerated longitudinal cortical atrophy in OEF/OIF/OND veterans with severe PTSD and the impact of comorbid TBI

Veterans who deployed in support of Operation Enduring Freedom (OEF), Iraqi Freedom (OIF), and New Dawn (OND) commonly experience severe psychological trauma, often accompanied by physical brain trauma resulting in mild traumatic brain injury (mTBI). Prior studies of individuals with posttraumatic stress disorder (PTSD) have revealed alterations in brain structure, accelerated cellular aging, and impacts on cognition following exposure to severe psychological trauma and potential interactive effects of military‐related mTBI. To date, however, little is known how such deployment‐related trauma changes with time and age of injury of the affected veteran. In this study, we explored changes in cortical thickness, volume, and surface area after an average interval of approximately 2 years in a cohort of 254 OEF/OIF/OND Veterans ranging in age from 19 to 67 years. Whole‐brain vertex‐wise analyses revealed that veterans who met criteria for severe PTSD (Clinician‐Administered PTSD Scale ≥60) at baseline showed greater negative longitudinal changes in cortical thickness, volume, and area over time. Analyses also revealed a significant severe‐PTSD by age interaction on cortical measures with severe‐PTSD individuals exhibiting accelerated cortical degeneration with increasing age. Interaction effects of comorbid military‐related mTBI within the severe‐PTSD group were also observed in several cortical regions. These results suggest that those exhibiting severe PTSD symptomatology have accelerated atrophy that is exacerbated with increasing age and history of mTBI.
Melanocore uptake by keratinocytes occurs through phagocytosis and involves protease‐activated receptor‐2 internalization

In the skin epidermis, melanin is produced and stored within melanosomes in melanocytes, and then transferred to keratinocytes. Different models have been proposed to explain the melanin transfer mechanism, which differ essentially in how melanin is transferred—either in a membrane‐bound melanosome or as a melanosome core, that is, melanocore. Here, we investigated the endocytic route followed by melanocores and melanosomes during internalization by keratinocytes, by comparing the uptake of melanocores isolated from the supernatant of melanocyte cultures, with melanosomes isolated from melanocytes. We show that inhibition of actin dynamics impairs the uptake of both melanocores and melanosomes. Moreover, depletion of critical proteins involved in actin‐dependent uptake mechanisms, namely Rac1, CtBP1/BARS, Cdc42 or RhoA, together with inhibition of Rac1‐dependent signaling pathways or macropinocytosis suggest that melanocores are internalized by phagocytosis, whereas melanosomes are internalized by macropinocytosis. Interestingly, we found that Rac1, Cdc42 and RhoA are differently activated by melanocore or melanosome stimulation, supporting the existence of two distinct routes of melanin internalization. Furthermore, we show that melanocore uptake induces protease‐activated receptor‐2 (PAR‐2) internalization by keratinocytes to a higher extent than melanosomes. Because skin pigmentation was shown to be regulated by PAR‐2 activation, our results further support the melanocore‐based mechanism of melanin transfer and further refine this model, which can now be described as coupled melanocore exo/phagocytosis.
Towards free breathing 3D ASL imaging of the human liver using prospective motion correction

Measurements of liver perfusion yield valuable information about certain diseases like carcinomas and cirrhosis. To assess perfusion, noninvasive arterial spin labeling (ASL) MRI has the potential to become an important alternative to contrast agent–based methods. Unfortunately, ASL perfusion‐weighted images are highly susceptible to breathing motion. This work presents a prospective motion‐compensation technique, tackling breathing motion during ASL experiments of the human liver.
Deletion of Calsyntenin-3, an atypical cadherin, suppresses inhibitory synapses but increases excitatory parallel-fiber synapses in cerebellum

Cadherins contribute to the organization of nearly all tissues, but the functions of several evolutionarily conserved cadherins, including those of calsyntenins, remain enigmatic. Puzzlingly, two distinct, non-overlapping functions for calsyntenins were proposed: As postsynaptic neurexin ligands in synapse formation, or as presynaptic kinesin adaptors in vesicular transport. Here, we show that, surprisingly, acute CRISPR-mediated deletion of calsyntenin-3 in mouse cerebellum in vivo causes a large decrease in inhibitory synapse, but a robust increase in excitatory parallel-fiber synapses in Purkinje cells. As a result, inhibitory synaptic transmission was suppressed, whereas parallel-fiber synaptic transmission was enhanced in Purkinje cells by the calsyntenin-3 deletion. No changes in the dendritic architecture of Purkinje cells or in climbing-fiber synapses were detected. Sparse selective deletion of calsyntenin-3 only in Purkinje cells recapitulated the synaptic phenotype, indicating that calsyntenin-3 acts by a cell-autonomous postsynaptic mechanism in cerebellum. Thus, by inhibiting formation of excitatory parallel-fiber synapses and promoting formation of inhibitory synapses in the same neuron, calsyntenin-3 functions as a postsynaptic adhesion molecule that regulates the excitatory/inhibitory balance in Purkinje cells.
E3 ligase BRUTUS Is a Negative Regulator for the Cellular Energy Level and the Expression of Energy Metabolism-Related Genes Encoded by Two Organellar Genomes in Leaf Tissues

E3 ligase BRUTUS (BTS), a putative iron sensor, is expressed in both root and shoot tissues in seedlings of Arabidopsis thaliana. The role of BTS in root tissues has been well established. However, its role in shoot tissues has been scarcely studied. Comparative transcriptome analysis with shoot and root tissues revealed that BTS is involved in regulating energy metabolism by modulating expression of mitochondrial and chloroplast genes in shoot tissues. Moreover, in shoot tissues of bts-1 plants, levels of ADP and ATP and the ratio of ADP/ATP were greatly increased with a concomitant decrease in levels of soluble sugar and starch. The decreased starch level in bts-1 shoot tissues was restored to the level of shoot tissues of wild-type plants upon vanadate treatment. Through this study, we expand the role of BTS to regulation of energy metabolism in the shoot in addition to its role of iron deficiency response in roots.
Delivering multidisciplinary neuromuscular care for children via telehealth

In response to coronavirus disease 2019 (COVID‐19) pandemic restrictions int 2020, our face‐to‐face (F2F) multidisciplinary neuromuscular clinic (NMC) transitioned to widespread use of telehealth (TH). This study aimed to (1) understand parent/guardian, child, and clinician perceptions of TH; (2) examine TH‐related changes in clinical activity; and (3) use these findings to inform a future model of care for the NMC.
Axial Flux Permanent Magnet Generator with Low Cogging Torque for Maintenance Free Under Water Power Generating System

The purpose of this study is to develop an all in one and maintenance free power-generating facility for river, ocean or tidal flow. A swivel mile with turbine, power generator, and auxiliary systems are installed in the same axial line and under water level. This architecture can reduce the mechanical loss and increase the efficiency of electric power generation. The facility can be easily lifted up on water level for its maintenance. To realize the all in one architecture, an excellent shaft seal is required. The shaft seal can prevent ingress of water into the facility. The frictional torque of the shaft seal should also be minimized to increase the efficiency of electric power generation. A diameter of the power generator, installed into the facility, should be minimized hydrodynamic resistance from its architecture. Barnacles and algae growth onto the surface of facility reduce the generating efficiency and increase the hydrodynamic resistance. A new technology is adopted from a coating of hydrated material.
LEARNING DYNAMICAL INFORMATION Diffusion of a contagious disease, degree = 3 .

We present a mathematical and computational framework for the problem of learning a dynami- cal system from noisy observations of a few trajectories and subject to side information . Side information is any knowledge we might have about the dynamical system we would like to learn besides trajectory data. It is typically inferred from domain-specific knowledge or basic principles of a scientific discipline. We are interested in explicitly integrating side information into the learning process in order to compensate for scarcity of trajectory observations. We identify six types of side information that arise naturally in many applications and lead to convex constraints in the learning problem. First, we show that when our model for the unknown dynamical system is parameterized as a polynomial, one can impose our side information constraints computationally via semidefinite programming. We then demonstrate the added value of side information for learning the dynamics of basic models in physics and cell biology, as well as for learning and controlling the dynamics of a model in epidemiology. Finally, we study how well polynomial dynamical systems can approximate continuously-differentiable ones while satisfying side information (either exactly or approximately). Our overall learning methodology combines ideas from convex optimization, real algebra, dynamical systems, and functional approximation theory, and can potentially lead to new synergies between these areas.
Three Phase Modular Multilevel PV Inverter With Distributed MPPT for Grid-Connected Applications

In this paper a three - stage modular cascaded H-bridge multilevel inverter for a grid associated photovoltaic (PV) framework is depicted . To expand the solar energy extraction of each PV string, an individual most extreme power point tracking (MPPT) control plan is connected, which permits the autonomous control of every dc-interface voltage. PV confuses may acquaint uneven power provided with the three-stage framework. To fathom this issue, a control plot with modulation compensession is proposed. The three-stage modular cascaded multilevel inverter model has been constructed. A fuzzy controller is actualized in this venture in the place of PI controller . Every H-bridge module is associated with a 185-W ssolar panel. Simulation results are exhibited to confirm the achievability of the proposed approach in MATLAB/SIMULINK environment.
MaxEnt Learners are Biased Against Giving Probability to Harmonically Bounded Candidates

One of the major differences between MaxEnt Harmonic Grammar (Goldwater and Johnson, 2003) and Noisy Harmonic Grammar (Boersma and Pater, 2016) is that in MaxEnt harmonically bounded candidates are able to get some probability, whereas in most other constraint-based grammars they can never be output (Jesney, 2007). The probability given to harmonically bounded candidates is taken from other candidates, in some cases allowing MaxEnt to model grammars that subvert some of the universal implications that are true in Noisy HG and categorical forms of HG (Anttila and Magri, 2018). Magri (2018) argues that the types of implicational universals that remain valid in MaxEnt are phonologically implausible, suggesting that MaxEnt overgenerates Noisy HG in a problematic way. However, a variety of recent work has shown that some of the possible grammars in a constraint based grammar may be unlikely to be observed because they are difficult to learn (Staubs, 2014; Stanton, 2016; Pater and Moreton, 2012; Hughto, 2019; O’Hara, 2021). Here, I show that grammars that give too much weight to harmonically bounded candidates, and violate the implicational universals that hold in Noisy HG are significantly harder to learn than those grammars that are also possible in Noisy HG. With learnability applied, I claim that the typological predictions of MaxEnt and Noisy HG are in fact much more similar than they would seem based on the grammars alone. This paper focuses on the classically harmonically bounded candidates, because collectively bounded candidates reflect a different type of constraint weighting, and are more often observed typologically (see local optionality Riggle and Wilson (2005); Hayes (2017)).
How well do LSTM language models learn filler-gap dependencies?

This paper revisits the question of what LSTMs know about the syntax of filler-gap dependencies in English. One contribution of this paper is to adjust the metrics used by Wilcox et al. (2018) and show that their language models (LMs) learn embedded wh -questions – a kind of filler-gap dependencies – better than they originally claimed. Another contribution of this paper is to examine four additional filler-gap dependency constructions to see whether LMs perform equally on all types of filler-gap dependencies. We find that different constructions are learned to different extents, and there is a correlation between performance and frequency of constructions in the Penn Treebank
The Relationship between Adverse Childhood Experiences, Emotional States and Personality Disorders in Offenders

ABSTRACT The current study focused on examining the relationship between adverse childhood experiences (ACEs) and personality disorders in offenders with antisocial, borderline or narcissistic personality disorders. We tested whether emotional states mediate the relationship between ACEs and personality disorders in an offender sample. Self-ratings of adverse childhood experiences, emotional states and personality pathology by 102 male offenders with personality disorders were examined using hierarchical multiple regression analyses and mediation analyses. The findings show that personality disorders were characterized by certain emotional states. ACEs did not differentiate between personality disorders. Only the relationship between Physical Neglect and NPD showed a small negative direct effect and one small positive indirect effect, with healthy emotional states as mediator.
Towards Robust Detection of PDF-based Malware

With the indisputable prevalence of PDFs, several studies into PDF malware and their evasive variants have been conducted to test the robustness of ML-based PDF classifier frameworks, Hidost and Mimicus. As heavily documented, the fundamental difference between them is that Hidost investigates the logical structure of PDFs, while Mimicus detects malicious indicators through their structural features. However, there exists techniques to mutate such features such that malicious PDFs are able to bypass these classifiers. In this work, we investigated three known attacks: Mimicry, Mimicry+, and Reverse Mimicry to compare how effective they are in evading classifiers in Hidost and Mimicus. The results shows that Mimicry and Mimicry+ are effective in bypassing models in Mimicus but not in Hidost, while Reverse Mimicy is effective against both models in Mimicus and Hidost.
ReSIL: Revivifying Function Signature Inference using Deep Learning with Domain-Specific Knowledge

Function signature recovery is important for binary analysis and security enhancement, such as bug finding and control-flow integrity enforcement. However, binary executables typically have crucial information vital for function signature recovery stripped off during compilation. To make things worse, recent studies show that many compiler optimization strategies further complicate the recovery of function signatures with intended violations to function calling conventions. In this paper, we first perform a systematic study to quantify the extent to which compiler optimizations (negatively) impact the accuracy of existing deep learning techniques for function signature recovery. Our experiments show that a state-of-the-art deep learning technique has its accuracy dropped from 98.7% to 87.7% when training and testing optimized binaries. We further identify specific weaknesses in existing approaches and propose an enhanced deep learning approach named \sysname (\underlineRe vivifying Function \underlineS ignature \underlineI nference using Deep \underlineL earning) to incorporate compiler-optimization-specific domain knowledge into the learning process. Our experimental results show that \sysname significantly improves the accuracy and F1 score in inferring function signatures, e.g., with accuracy in inferring the number of arguments for callees compiled with optimization flag O1 from 84.8% to 92.67%. We also demonstrate security implications of \sysname in Control-Flow Integrity enforcement in stopping potential Counterfeit Object-Oriented Programming (COOP) attacks.
NEUTRON: A Graph-based Pipeline for Zero-trust Network Architectures

The Zero-Trust Architecture (ZTA) security paradigm deploys comprehensive user- and resource-aware defenses both at the network's perimeter and inside the network. However, deploying a ZTA approach requires specifying and managing a large, network spanning set of fine-grained security policies, which will increase administrators' workloads and increase the chance of errors. This paper presents the design and prototype implementation of the NEUTRON policy framework, which provides an automated end-to-end policy pipeline, specification, management, testing, and deployment. NEUTRON uses a flexible, graph-based approach to specify and share complex, fine-grained network security policies. NEUTRON provides a software structure so that policy patterns may be easily shared between organizations, reducing the burden of creating the policy. Administrators assemble the software for their site, and the NEUTRON policy generator creates the entire network-wide security policy. Treating the security policy like software also allows new approaches to policy verification and policy change impact analysis. Thus we designed the Security Policy Regression Tool (SPRT), which uses our novelRuleset Aggregation Algorithm to perform scalable verification of the network-wide security policy across the network model. Moreover, our graph-based framework allows for efficient computation and visualization of the policy change impact.
Recovering Structure of Input of a Binary Program

This paper presents an algorithm to automatically infer a recursive state machine (RSM) describing the space of acceptable input of an arbitrary binary program by executing that program with one or more valid inputs. The algorithm automatically identifies atomic fields of fixed and variable lengths and syntactic elements, such as separators and terminators, and generalizes them into regular expression tokens. It constructs an RSM of tokens to represent structures such as arrays and records. Further, it constructs nested states in the RSM to represent complex, nested structures. The RSM may serve as an independent parser for the program's acceptable inputs. A controlled experiment was performed using a prototype implementation of the algorithm and a set of synthetic programs with input formats that mimic characteristics of conventional data formats, such as CSV, PNG, PE file, etc. The experiment demonstrates that the inferred RSMs correctly identify the syntactic elements and their grammatical orderings. When used as generators, the RSMs also produced syntactically correct data for the formats that use terminators to end a sequence of elements, but not so when the format maintains a count of elements for variable length fields instead of a terminator. Experiments with real-world programs produced similar results.
Building a Commit-level Dataset of Real-world Vulnerabilities

While CVE have become a de facto standard for publishing advisories on vulnerabilities, the state of current CVE databases is lackluster. Yet, CVE advisories are insufficient to bridge the gap with the vulnerability artifacts in the impacted program. Therefore, the community is lacking a public real-world vulnerabilities dataset providing such association. In this paper, we present a method restoring this missing link by analyzing the vulnerabilities from the AOSP, an aggregate of more than 1,800 projects. It is the perfect target for building a representative dataset of vulnerabilities, as it covers the full spectrum that may be encountered in a modern system where a variety of low-level and higher-level components interact. More specifically, our main contribution is a dataset of more than 1,900 vulnerabilities, associating generic metadata (e.g. vulnerability type, impact level) with their respective patches at the commit granularity (e.g. fix commit-id, affected files, source code language). Finally, we also augment this dataset by providing precompiled binaries for a subset of the vulnerabilities. These binaries open various data usage, both for binary only analysis and at the interface between source and binary. In addition of providing a common baseline benchmark, our dataset release supports the community for data-driven software security research.
Associations of human papillomavirus (HPV) genotypes and related risk factors in a cohort of women living with HIV in a Brazilian countryside city

The human papillomavirus (HPV) is associated with cervical abnormalities. People living with HIV are more susceptible to HPV. Campos dos Goytacazes implemented the quadrivalent HPV vaccine (4vHPV) for women living with HIV (WLWH) in 2011, 4 years before the Brazilian public vaccination program. We aimed to characterize the genomic diversity and predictors of HPV infection in WLWH through a prospective cohort study. After the consent form was received, a questionnaire was applied and an endocervical sample was collected. For genotyping, a microarray HPV technique was performed. Two intervention moments were performed: T1, the initial moment, with collection and vaccination; T2 moment, 2 years after T1. Univariate and multivariate analyses were performed. The T1 moment cohort was formed by 146 women,107 belonging to Group 1(HPV‐negative) and 39 to Group 2 (HPV‐positive). The variables age, marital status, number of children, number of sexual partners, and CD4 count were protective against HPV. The variables number of sexual partners, marital status, and the number of children lost significance in multivariate analysis. Concerning T2 moment, 42 patients were followed with three positive cases. The use of 4vHPV is beneficial for this population and should also be recommended at an age from 26 to 45 years inside the public vaccination program.