Miracle-dz/UniFAR-FTUs huggingface dataset:
Basic Self-Disturbances Related to Reduced Anterior Cingulate Volume in Subjects at Ultra-High Risk for Psychosis

Introduction: Alterations of the “pre-reflective” sense of first-person perspective (e.g., of the “basic self”) are characteristic features of schizophrenic spectrum disorders and are significantly present in the prodromal phase of psychosis and in subjects at ultra-high risk for psychosis (UHR). Studies in healthy controls suggest that neurobiological substrate of the basic self involves cortical midline structures, such as the anterior and posterior cingulate cortices. Neuroimaging studies have identified neuroanatomical cortical midline structure abnormalities in schizophrenic spectrum disorders. Objectives: i) To compare basic self-disturbances levels in UHR subjects and controls and ii) to assess the relationship between basic self-disturbances and alterations in cortical midline structures volume in UHR subjects. Methods: Thirty-one UHR subjects (27 antipsychotic-naïve) and 16 healthy controls were assessed using the 57-item semistructured Examination of Anomalous Self-Experiences (EASE) interview. All subjects were scanned using magnetic resonance imaging (MRI) at 3 T, and gray matter volume was measured in a priori defined regions of interest (ROIs) in the cortical midline structures. Results: EASE scores were much higher in UHR subjects than controls (p < 0.001). The UHR group had smaller anterior cingulate volume than controls (p = 0.037). There were no structural brain imaging alterations between UHR individuals with or without self-disturbances. Within the UHR sample, the subgroup with higher EASE scores had smaller anterior cingulate volumes than UHR subjects with lower EASE scores and controls (p = 0.018). In the total sample, anterior cingulate volume was inversely correlated with the EASE score (R = 0.52, p < 0.016). Conclusions: Basic self-disturbances in UHR subjects appear to be related to reductions in anterior cingulate volume.
Initiation of the Hypothalamic–Pituitary–Gonadal Axis in Young Girls Undergoing Central Precocious Puberty Exerts Remodeling Effects on the Prefrontal Cortex

Central precocious puberty (CPP) has been shown to exert significant effects on psychosocial development. These early puberty-related hormones and psychosocial functional changes are considered to be associated with specific brain development. However, the biological mechanisms underlying the sculpting of human brain architecture and modulation of psychosocial transformation by puberty-related hormonal maturation remain elusive, especially during the early phase of CPP. The current investigation aims to specify the brain regions in which early hormone-related maturation effects occur during CPP and their relationships with psychological functions. 65 young girls (aged 4.3–8.0 years) underwent structural imaging on a 3T MR system, completed psychological tests and performed the gonadotropin-releasing hormone (GnRH) stimulation test to identify hormonal manifestations of hypothalamic–pituitary–gonadal axis (HPG axis) activation. Based on the GnRH test, 28 young girls were identified with CPP, whereas the other 37 girls were identified with non-central precocious puberty (NCPP). Cortical parameters were calculated and compared between the two groups after adjusting for age, weight, and height. Brain regions showing group differences were extracted and correlated with serum hormone levels and psychological parameters. The CPP girls showed thinner cortices primarily in the right rostral middle frontal cortex. This morphological difference was positively correlated with stimulated estradiol (E2) levels. Further, higher E2 levels were significantly associated with higher hyperactivity scores. Premature HPG axis activation in CPP girls at an early stage appears to exert remodeling effects on brain anatomy, primarily in the prefrontal cortex, which may affect psychological development following the emergence of robust changes in sex hormones.
Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack

"Feint Attack", as a new type of APT attack, has become the focus of attention. It adopts a multi-stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused huge losses inadvertently. However, to our knowledge, all previous works use common methods such as Causal-Correlation or Cased-based to detect outdated multi-stage attacks. Few attentions have been paid to detect the "Feint Attack", because the difficulty of detection lies in the diversification of the concept of "Feint Attack" and the lack of professional datasets, many detection methods ignore the semantic relationship in the attack. Aiming at the existing challenge, this paper explores a new method to solve the problem. In the attack scenario, the fuzzy clustering method based on attribute similarity is used to mine multi-stage attack chains. Then we use a few-shot deep learning algorithm (SMOTE&CNN-SVM) and bidirectional Recurrent Neural Network model (Bi-RNN) to obtain the "Feint Attack" chains. "Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain. So we used Bi-RNN coding to obtain the hidden feature of "Feint Attack" chain. In the end, our method achieved the goal to detect the "Feint Attack" accurately by using the LLDoS1.0 and LLDoS2.0 of DARPA2000 and CICIDS2017 of Canadian Institute for Cybersecurity.
A Dynamic Landscape for Antibody Binding Modulates Antibody-Mediated Neutralization of West Nile Virus

Neutralizing antibodies are a significant component of the host's protective response against flavivirus infection. Neutralization of flaviviruses occurs when individual virions are engaged by antibodies with a stoichiometry that exceeds a required threshold. From this “multiple-hit” perspective, the neutralizing activity of antibodies is governed by the affinity with which it binds its epitope and the number of times this determinant is displayed on the surface of the virion. In this study, we investigated time-dependent changes in the fate of West Nile virus (WNV) decorated with antibody in solution. Experiments with the well-characterized neutralizing monoclonal antibody (MAb) E16 revealed a significant increase in neutralization activity over time that could not be explained by the kinetics of antibody binding, virion aggregation, or the action of complement. Additional kinetic experiments using the fusion-loop specific MAb E53, which has limited neutralizing activity because it recognizes a relatively inaccessible epitope on mature virions, identified a role of virus “breathing” in regulating neutralization activity. Remarkably, MAb E53 neutralized mature WNV in a time- and temperature-dependent manner. This phenomenon was confirmed in studies with a large panel of MAbs specific for epitopes in each domain of the WNV envelope protein, with sera from recipients of a live attenuated WNV vaccine, and in experiments with dengue virus. Given enough time, significant inhibition of infection was observed even for antibodies with very limited, or no neutralizing activity in standard neutralization assays. Together, our data suggests that the structural dynamics of flaviviruses impacts antibody-mediated neutralization via exposure of otherwise inaccessible epitopes, allowing for antibodies to dock on the virion with a stoichiometry sufficient for neutralization.
Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method

Fully parallel architecture at disparity-level for efficient semi-global matching (SGM) with refined rank method is presented. The improved SGM algorithm is implemented with the non-parametric unified rank model which is the combination of Rank filter/AD and Rank SAD. Rank SAD is a novel definition by introducing the constraints of local image structure into the rank method. As a result, the unified rank model with Rank SAD can make up for the defects of Rank filter/AD. Experimental results show both excellent subjective quality and objective performance of the refined SGM algorithm. The fully parallel construction for hardware implementation of SGM is architected with reasonable strategies at disparity-level. The parallelism of the data-stream allows proper throughput for specific applications with acceptable maximum frequency. The results of RTL emulation and synthesis ensure that the proposed parallel architecture is suitable for VLSI implementation.
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

We present a type of Temporal Restricted Boltzmann Machine that defines a probability distribution over an output sequence conditional on an input sequence. It shares the desirable properties of RBMs: efficient exact inference, an exponentially more expressive latent state than HMMs, and the ability to model nonlinear structure and dynamics. We apply our model to a challenging real-world graphics problem: facial expression transfer. Our results demonstrate improved performance over several baselines modeling high-dimensional 2D and 3D data.
Source Environments of the Microbiome in Perennially Ice-Covered Lake Untersee, Antarctica

Ultra-oligotrophic Lake Untersee is among the largest and deepest surface lakes of Central Queen Maud Land in East Antarctica. It is dammed at its north end by the Anuchin Glacier and the ice-cover dynamics are controlled by sublimation — not melt — as the dominating ablation process and therefore surface melt during austral summer does not provide significant amounts of water for recharge compared to subsurface melt of the Anuchin Glacier. Several studies have already described the structure and function of the microbial communities within the water column and benthic environments of Lake Untersee, however, thus far there have been no studies that examine the linkages between the lake ecosystem with that of the surrounding soils or the Anuchin Glacier. The glacier may also play an important role as a major contributor of nutrients and biota into the lake ecosystem. Based on microbial 16S rRNA amplicon sequencing, we showed that the dominant bacterial signatures in Lake Untersee, the Anuchin Glacier and its surrounding soils were affiliated with Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, and Proteobacteria. Aerosol and local soil depositions on the glacier surface resulted in distinct microbial communities developing in glacier ice and cryoconite holes. Based on a source tracking algorithm, we found that cryoconite microbial assemblages were a potential source of organisms, explaining up to 36% of benthic microbial mat communities in the lake. However, the major biotic sources for the lake ecosystem are still unknown, illustrating the possible importance of englacial and subglacial zones. The Anuchin Glacier may be considered as a vector in a biological sense for the bacterial colonization of the perennially ice-covered Lake Untersee. However, despite a thick perennial ice cover, observed “lift-off” microbial mats escaping the lake make a bidirectional transfer of biota plausible. Hence, there is an exchange of biota between Lake Untersee and connective habitats possible despite the apparent sealing by a perennial ice cover and the absence of moat areas during austral summer.
11C-Autoradiographs to Image Phloem Loading

Generally, tree species load photoassimilates passively into the phloem, while herbaceous species load actively. These phloem loading strategies have implications for phloem sugar concentration and growth potential. Whereas in previous research, phloem loading identification was performed with 14C-autoradiography, we suggest 11C-autoradiography, because of its compatibility with plant-PET (positron emission tomography) scans. Because 11C-autoradiography has been hardly used in plant sciences so far, it was tested in contrasting plant species: one temperate tree species, Populus tremula L. , three tropical tree species, Erythrophleum suaveolens (Guill. & Perr.) Brenan, E. ivorense A. Chev. , and Maesopsis eminii Engl. , and two herbaceous crop species Solanum lycopersicum L. and S. tuberosum L. Our results confirmed that P. tremula is a passive loader, and Solanum spp. are active loaders. Erythrophleum spp. and young leaves of M. eminii showed the expected passive loading strategy, but the mature leaves of M. eminii showed an uncommon pattern. Images corrected for leaf tissue thickness supported that mature leaves of M. eminii used active phloem loading, which is linked to continuous investment in growth and new leaves, supporting the lower carbon storage levels often observed in tropical tree species. With this study, we demonstrate that 11C-autoradiography is a powerful tool to acquire detailed tracer distribution in leaves to typify phloem loading strategies in plant species.
Emotion Regulation Flexibility: Gender Differences in Context Sensitivity and Repertoire

Emotion regulation (ER) has been conceptualized as processes through which individuals modulate their emotions consciously and non-consciously to respond appropriately to environmental demands. Emotions can be regulated in many ways and specific strategies may have differing efficacy across situations and individuals. The importance of flexibility in implementing ER strategies has been highlighted in many current models. In this study, we investigated gender differences in two regulatory processes, context sensitivity and repertoire using a novel coding system for ER strategy classification. The results revealed that women consistently used more strategies than men and were more flexible in the implementation of those strategies. These findings validate our novel coding system for ER strategy classification. They further highlight the importance of a comprehensive examination of gender differences in ER processes for understanding the nuances of ER and developing effective treatments for psychopathologies characterized by ER deficits.
AI Enabling Technologies: A Survey

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together in order to provide capabilities that can be used by decision makers, warfighters and analysts. These pieces include data collection, data conditioning, algorithms, computing, robust artificial intelligence, and human-machine teaming. While much of the popular press today surrounds advances in algorithms and computing, most modern AI systems leverage advances across numerous different fields. Further, while certain components may not be as visible to end-users as others, our experience has shown that each of these interrelated components play a major role in the success or failure of an AI system. This article is meant to highlight many of these technologies that are involved in an end-to-end AI system. The goal of this article is to provide readers with an overview of terminology, technical details and recent highlights from academia, industry and government. Where possible, we indicate relevant resources that can be used for further reading and understanding.
Investigation of light ion fusion reactions with plasma discharges

The scaling of reaction yields in light ion fusion to low reaction energies is important for our understanding of stellar fuel chains and the development of future energy technologies. Experiments become progressively more challenging at lower reaction energies due to the exponential drop of fusion cross sections below the Coulomb barrier. We report on experiments where deuterium-deuterium (D-D) fusion reactions are studied in a pulsed plasma in the glow discharge regime using a benchtop apparatus. We model plasma conditions using particle-in-cell codes. Advantages of this approach are relatively high peak ion currents and current densities (0.1 to several A/cm2) that can be applied to metal wire cathodes for several days. We detect neutrons from D-D reactions with scintillator-based detectors. For palladium targets, we find neutron yields as a function of cathode voltage that are over 100 times higher than yields expected for bare nuclei fusion at ion energies below 2 keV (center of mass frame). A possible explanation is a correction to the ion energy due to an electron screening potential of 1000±250 eV, which increases the probability for tunneling through the repulsive Coulomb barrier. Our compact, robust setup enables parametric studies of this effect at relatively low reaction energies.
Homogenous $In_{x}Ga_{1-x}N$ alloys on ZnO substrates: A new approach for high performance thermoelectric materials

High performance thermoelectric materials for wide-range temperature applications still remains a challenge. In this study, we have produced high-quality homogeneous $In_{0.32}Ga_{0.68}N$ on ZnO substrates, with no phase separation at high Indium content, using metal organic chemical vapor deposition for thermoelectric applications. A record high room temperature figure of merit zT is obtained of 0.86, which is five times larger than that of SiGe, the current state of the art high temperature thermoelectric material. These materials are shown to have a nearly perfect doping concentration to maximize zT regardless of the scattering mechanism. This almost one order of magnitude increase in zT is due to large electrical conductivities from oxygen co-doping as well as low thermal conductivities from alloy scattering. The maximum power factor reached was $77.98x10^{-4} W/mK^{2}$ at 300K for $In_{0.32}Ga_{0.68}N$ alloys at a carrier concentration $~6.25x10^{20} cm^{-3}$. This work indicates that $In_{x}Ga_{1-x}N$ alloys have great potential for thermoelectric applications especially at a high temperature range.
Compositional Coding for Collaborative Filtering

Efficiency is crucial to the online recommender systems, especially for the ones which needs to deal with tens of millions of users and items. Because representing users and items as binary vectors for Collaborative Filtering (CF) can achieve fast user-item affinity computation in the Hamming space, in recent years, we have witnessed an emerging research effort in exploiting binary hashing techniques for CF methods. However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data. In this work, we attempt to improve the efficiency without hurting the model performance by utilizing both the accuracy of real-valued vectors and the efficiency of binary codes to represent users/items. In particular, we propose the Compositional Coding for Collaborative Filtering (CCCF) framework, which not only gains better recommendation efficiency than the state-of-the-art binarized CF approaches but also achieves even higher accuracy than the real-valued CF method. Specifically, CCCF innovatively represents each user/item with a set of binary vectors, which are associated with a sparse real-value weight vector. Each value of the weight vector encodes the importance of the corresponding binary vector to the user/item. The continuous weight vectors greatly enhances the representation capability of binary codes, and its sparsity guarantees the processing speed. Furthermore, an integer weight approximation scheme is proposed to further accelerate the speed. Based on the CCCF framework, we design an efficient discrete optimization algorithm to learn its parameters. Extensive experiments on three real-world datasets show that our method outperforms the state-of-the-art binarized CF methods (even achieves better performance than the real-valued CF method) by a large margin in terms of both recommendation accuracy and efficiency. We publish our project at https://github.com/3140102441/CCCF.
Fog-Aided Device to Device Networks with Opportunistic Content Delivery

In this paper, we investigate the caching placement and content delivery strategy in large-scale fog-aided device to device (F-D2D) networks by exploring the idea of opportunistic spectrum access (OSA) which is originally introduced in cognitive radio (CR). Let $\overline{\Phi}_n$ denote the point process formed by the conventional user equipments (C-UEs) which request other files except the $n$-th file. Then, to resolve the contentions among the file requests and constrain the `inter-file' interference received at the C-UEs, an opportunistic content delivery strategy is proposed, under which a fog user equipment (F-UE) with the $n$-th file selected as the candidate file based on the random file selection (RFS) scheme or the most requested file selection (MRFS) scheme is designed to detect and exploit the spatial spectrum hole of $\overline{\Phi}_n$ for the multicast of the $n$-th file to its associated C-UEs. Assuming decentralized probabilistic caching, the activation probability of F-UEs under the proposed opportunistic content delivery strategy is derived. Further, by adopting appropriate approximation, the successful content delivery probability (SCDP) of the F-D2D network is evaluated. We also investigate the caching placement optimization of F-UEs for the maximization of SCDP. It is shown through both the numerical and simulation results that the proposed opportunistic content delivery strategy can enhance the network SCDP performance.
Improving the efficiency of image guided brachytherapy in cervical cancer

Brachytherapy is an essential component of the treatment of locally advanced cervical cancers. It enables the dose to the tumor to be boosted whilst allowing relative sparing of the normal tissues. Traditionally, cervical brachytherapy was prescribed to point A but since the GEC-ESTRO guidelines were published in 2005, there has been a move towards prescribing the dose to a 3D volume. Image guided brachytherapy has been shown to reduce local recurrence, and improve survival and is optimally predicated on magnetic resonance imaging. Radiological studies, patient workflow, operative parameters, and intensive therapy planning can represent a challenge to clinical resources. This article explores the ways, in which 3D conformal brachytherapy can be implemented and draws findings from recent literature and a well-developed hospital practice in order to suggest ways to improve the efficiency and efficacy of a brachytherapy service. Finally, we discuss relatively underexploited translational research opportunities.