Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the effects of head modelling errors and measurement noise on EEG source localization accuracy in preterm newborns: A single‐subject study

The accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)–white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5 mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels.
A three-stage developmental pathway for human Vγ9Vδ2 T cells within the postnatal thymus

Vγ9Vδ2 T cells are the largest population of γδ T cells in adults and can play important roles in providing effective immunity against cancer and infection. Many studies have suggested that peripheral Vγ9Vδ2 T cells are derived from the fetal liver and thymus and that the postnatal thymus plays little role in the development of these cells. More recent evidence suggested that these cells may also develop postnatally in the thymus. Here, we used high-dimensional flow cytometry, transcriptomic analysis, functional assays, and precursor-product experiments to define the development pathway of Vγ9Vδ2 T cells in the postnatal thymus. We identify three distinct stages of development for Vγ9Vδ2 T cells in the postnatal thymus that are defined by the progressive acquisition of functional potential and major changes in the expression of transcription factors, chemokines, and other surface markers. Furthermore, our analysis of donor-matched thymus and blood revealed that the molecular requirements for the development of functional Vγ9Vδ2 T cells are delivered predominantly by the postnatal thymus and not in the periphery. Tbet and Eomes, which are required for IFN-γ and TNFα expression, are up-regulated as Vγ9Vδ2 T cells mature in the thymus, and mature thymic Vγ9Vδ2 T cells rapidly express high levels of these cytokines after stimulation. Similarly, the postnatal thymus programs Vγ9Vδ2 T cells to express the cytolytic molecules, perforin, granzyme A, and granzyme K. This study provides a greater understanding of how Vγ9Vδ2 T cells develop in humans and may lead to opportunities to manipulate these cells to treat human diseases. Description Human Vγ9Vδ2 T cells develop within the postnatal thymus via a three-stage pathway defined by distinct molecular and cellular changes. Editor’s summary Unconventional T cells play an important role in protective immunity; however, the development of these distinctive cell populations is poorly understood. Using tissues donated from children undergoing heart surgery, Perriman et al. investigated the development of Vγ9Vδ2 T cells, a large population of unconventional γδ T cells found in adults, addressing the uncertainty about the role of the postnatal thymus in their maturation. This work identified three distinct populations of Vγ9Vδ2 T cells, distinguished by their transcriptional profiles and functional ability to produce cytokines, consistent with a three-stage developmental transition in the thymus before egress of functionally mature cells into the periphery. The identification of precursor Vγ9Vδ2 T cells extends understanding of factors potentially involved in the differentiation of these cells in humans. —Sarah H. Ross
Learning Human-like Locomotion Based on Biological Actuation and Rewards

We propose a method of learning a policy for human-like locomotion via deep reinforcement learning based on a human anatomical model, muscle actuation, and biologically inspired rewards, without any inherent control rules or reference motions. Our main ideas involve providing a dense reward using metabolic energy consumption at every step during the initial stages of learning and then transitioning to a sparse reward as learning progresses, and adjusting the initial posture of the human model to facilitate the exploration of locomotion. Additionally, we compared and analyzed differences in learning outcomes across various settings other than the proposed method.
3D Character Motion Authoring From Temporally-Sparse Photos

This paper presents a neural network-based learning approach that enables seamless generation of 3D human motion in-between photos to accelerate the process of 3D character motion authoring. This new approach allows users to freely edit (replace, insert, or delete) input photos and specify the transition length to generate a kinematically coherent sequence of 3D human poses and shapes in-between the given photos. We demonstrate through qualitative and subjective evaluations that our approach is capable of generating high-fidelity, natural 3D pose and shape transitions.
Learning to Simulate Crowds with Crowds

Controlling agent behaviors with Reinforcement Learning is of continuing interest in multiple areas. One major focus is to simulate multi-agent crowds that avoid collisions while locomoting to their goals. Although avoiding collisions is important, it is also necessary to capture realistic anticipatory navigation behaviors. We introduce a novel methodology that includes: 1) an RL method for learning an optimal navigational policy, 2) position-based constraints for correcting policy navigational decisions, and 3) a crowd-sourcing framework for selecting policy control parameters. Based on optimally selected parameters, we train a multi-agent navigation policy, which we demonstrate on crowd benchmarks. We compare our method to existing works, and demonstrate that our approach achieves superior multi-agent behaviors.
The perilous promise of productivity: Affective politics of farming media and its consequences for the future of agriculture

This article brings together historically disparate literatures, including rural sociology, critical food studies, new media studies, and affect theory to think critically, and productively, about digital agriculture—for example, robotic milking machines, precision techniques, data-intensive and algorithm-enabled predictive software. To make this argument, the article triangulates multiple forms of data, including that collected through recurring interviews with 25 farmers in Colorado, Kansas, and Nebraska who employed agricultural digital platforms. When taken together, these data tell a story about how new farm media enact worlds by affording networked affective subjectivities. They do this not because they tell users things—as suggested by their “smart” moniker. Perhaps most importantly, they animate what farmers feel through affordances tied to expressions of desire, risk, and promise; anticipatory-affects with immense political potential.
Beam-steering metasurfaces assisted coherent optical wireless multichannel communication system

Abstract The metasurface based beam-steering devices with the advantages of large steering angles, arbitrary channels and ultra-compactness have played an important role for data allocation and exchange in the optical wireless communication. However, the current metasurface based optical wireless communication systems are mainly on intensity modulation and direct detection (IM/DD), which shows a relative lower transmission capacity, lower received optical signal-to-noise ratio (OSNR) and complexity of system. In this study, a bidirectional multichannel optical wireless system enabled by a polarization independent metasurface with coherent modulation and reception is designed and experimentally demonstrated, which exhibits exclusive 100 Gbps coherent optical signals to multiusers with their own wavelengths, 2 m free space distance and field of view of 20° × 20°. In addition, the proposed system can support optical broadcasting system with capacity of 900 Gbps. The demonstrated metasurface assisted optical wireless communication system merges the optical coherent communication techniques and emerging concept of metasurface, which reduces the complexity and cost of the system while contributing a high transmission capacity, opening a new avenue for high performance optical wireless communications.
Cross-modal retrieval of remote sensing images and text based on self-attention unsupervised deep common feature space

ABSTRACT With the increase in multimodal data, such as remote sensing images, text, and video, cross-modal retrieval is widely applied in many fields. Because different modality data are heterogeneous, the main aim of cross-modal retrieval is to bridge their ‘semantic gap’. However, there are few cross-modal retrieval studies focused on remote sensing images and text, and fine-grained information from the remote sensing images and text are not fully considered by existing methods. To improve the retrieval accuracy of remote sensing images and text, we propose a self-attention unsupervised deep common feature space cross-modal retrieval method. First, a self-attention mechanism is applied to capture the fine-grained relationships between word fragments of text and remote sensing images. Then, a deep learning method and the triplet loss of remote sensing image and text pairs are used to extract the features of remote sensing images and text, and generate a semantically consistent common feature space, in which the feature representations of remote sensing images and text exhibit a uniform distribution. The experimental results of three benchmark cross-modal datasets, including RSICD, UCM_captions, and Sydney_captions, show that the proposed method performs better than the state-of-the-art methods. For example, when compared with the best model (VSE++) tested on RSICD, the proposed model demonstrated 2.1% and 5.6% higher precision in text to image and image to text retrieval tasks, respectively, and 3.8% higher mean average precision. However, similar to the other models tested, we found that the proposed model had a weaker performance when retrieving text from an image query than retrieving images from a text query.
Pre-Service Teachers' Concept Images on Fractal Dimension

The analysis of pre-service teachers' concept images can provide information about their mental schema of fractal dimension. There is limited research on students' understanding of fractal and fractal dimension. Therefore, this study aimed to investigate the pre-service teachers' understandings of fractal dimension based on concept image. The descriptive method was used in this study. The sample under investigation comprised of 78 (26 males and 52 females) freshmen pre-service elementary mathematics teachers at Afyon Kocatepe University in Turkey. Data was collected by an open-ended questionnaire and clinical interview. The questionnaire was developed by the researcher based on previous studies in the areas of teaching and learning fractals. Data was categorized by semantic content analysis and analyzed using descriptive methods. Analyzing the data gathered from clinical interviews, themes determined in the questionnaire were taken into consideration. The findings showed that pre-service teachers had difficulty in understanding fractal dimension and in making sense of it. Moreover, pre-service teachers could calculate fractal dimension, but they were not aware of what was being calculated completely.
Government innovation subsidy, monetary policy, and corporate R&D investment: evidence from China

ABSTRACT From a perspective of coordinating fiscal and monetary policies, this study constructs the government innovation subsidy indicator using the text search method, and examines the impact and mechanisms of government innovation subsidy on corporate research and development (R&D) investment, then investigates how expansionary monetary policy influences this effect. We find that government innovation subsidy can be a booster for corporate R&D by reducing corporate debt financing cost and increasing internal financing, and loose monetary policy suppresses the positive effect of government innovation subsidy on corporate R&D investment. This study offers macroeconomic policy implications that support corporate innovation.
The motivations of leaders to lead Facebook online groups: a case study of parenting groups

ABSTRACT Facebook community leaders play a significant role in creating and maintaining social environments, and they characteristically exhibit several qualities and skills, such as the ability to generate involvement in the groups. However, their identities and motivations have yet to be investigated in depth. Using the theoretical framework of the Uses and Gratifications Theory (UGT), this paper addresses this gap by exploring the motivations of leaders to lead a Facebook online group. Leaders of online parents groups were surveyed for this study. A survey was administered electronically to 160 Facebook community leaders to gather data on the relationship between the community leaders’ motivation and the following variables: personality traits (narcissism, altruism, grit), sense of community (immersion, sense of belonging, influence), management features, and demographics. A hierarchical regression indicates that higher narcissism, self-control, immersion, and lower consistency of interest (grit factor) can explain Facebook community leaders’ motivations to lead online groups of parents. The results are discussed in the context of the leaders’ influential role in social media.
Handling difficult cryo-ET samples: A study with primary neurons from Drosophila melanogaster

Cellular neurobiology has benefited from recent advances in the field of cryo-electron tomography (cryo-ET). Numerous structural and ultrastructural insights have been obtained from plunge-frozen primary neurons cultured on electron microscopy grids. With most primary neurons been derived from rodent sources, we sought to expand the breadth of sample availability by using primary neurons derived from 3rd instar Drosophila melanogaster larval brains. Ultrastructural abnormalities were encountered while establishing this model system for cryo-ET, which were exemplified by excessive membrane blebbing and cellular fragmentation. To optimize neuronal samples, we integrated substrate selection, micropatterning, montage data collection, and chemical fixation. Efforts to address difficulties in establishing Drosophila neurons for future cryo-ET studies in cellular neurobiology also provided insights that future practitioners can use when attempting to establish other cell-based model systems.
Behavioral read out of population value signals in primate orbitofrontal cortex

The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking OFC value representations to decision outcomes has remained elusive. To address this question we show for the first time that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally-potent output subspace.
Prof-Life-Log: Monitoring and assessment of human speech and acoustics using daily naturalistic audio streams

Speech technology advancements have progressed significantly in the last decade, yet major research challenges continue to impact effective advancements for diarization in naturalistic environments. Traditional diarization efforts have focused on single audio streams based on telephone communications, broadcast news, and/or scripted speeches or lectures. Limited effort has focused on extended naturalistic data. Here, algorithm advancements are established for an extensive daily audio corpus called Prof-Life-Log, consisting of + 80days of 8-16 hr recordings from an individual’s daily life. Advancements include the formulation of (i) an improved threshold-optimized multiple feature speech activity detector (TO-Combo-SAD), (ii) advanced primary vs. secondary speaker detection, (iii) advanced word-count system using part-of-speech tagging and bag-of-words construction, (iv) environmental “sniffing” advancements to identify location based on properties of the acoustic space, and (v) diarization interaction ana.
Speeding up Risk Analyses of U.S. Flood Insurance Loss Data Using the Diffusion Map

In the insurance industry, catastrophe risk analysis using catalogs of catastrophic events is a major component for quantifying financial risks of an insurance portfolios. To ensure an accurate quantification of risk, particularly for rare, strong catastrophic events, large sizes of catalogs are simulated and used for computing loss estimates location-by-location and event-by-event, but this is computationally intensive. In this paper, we propose to speed up the risk computation by taking a data analytic approach to compress the catalog—specifically, using dimension reduction and clustering. To address the nonlinear geometry of the loss data from the U.S. Flood model, we used a nonlinear dimension reduction technique, the diffusion map. Combined with clustering, we show that it yields accurate catalog compression and produces a realistic representation of hydrometeorological patterns over the entire country. Finally, we discuss how clustering results must be refined to ensure fidelity in retaining the most important catastrophic events, and how in real life, a risk manager can utilize our results to make informed risk management decisions.
The theory of stable allocations and the practice of market design. The Nobel Prize in Economics 2012 for Alvin E. Roth and Lloyd S. Shapley

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2012 was awarded jointly to Alvin E. Roth and Lloyd S. Shapley for their contributions to the theory of stable allocations and the practice of market design. The theory of stable allocations consists of a family of models that study assignment problems in which two disjoint sets of agents (or a set of agents and a set of objects) have to be matched. For example, men to women, workers to firms, students to schools, or patients to live donor kidneys. A matching is stable if no subset of agents can improved upon their proposed matches by rematching only among themselves. Stability is an essential property if matching is voluntary. The practice of market design consists of applying those two-sided matching models to specific assignment problems with the aim of proposing improvements on how they are solved. This paper presents a brief description of the academic career of the laureates and describes their contributions by presenting the most basic two-sided matching model and some of its market design applications, including the organization of a centralized system to propose kidney transplantations to use kidneys of live donors that are incompatible with their respective patients, the yearly assignment of North-American medical students to hospital internship programs, and children to schools in cities such as Boston and New York. [Contrib Sci 11(1): 103-112 (2015)]
The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

Candidatus Liberibacter asiaticus (CLas) is the causal agent of the devastating citrus Huanglongbing (HLB) disease. Our understanding of the pathogenicity mechanism and biology of CLas remain limited because CLas has not been cultured in artificial media. CLas encodes 1136 proteins of which 415 have unknown functions. Since genetic studies of CLas genes with unknown functions are impossible, we utilized genome-wide protein-protein interactions (PPIs) yeast-two-hybrid (Y2H) assays to help solve the mystery. PPIs are fundamental to all cellular processes and machinery and instrumental in investigating uncharacterized proteins and inferring biological pathways. In total, 916 bait and 936 prey proteins were included in the three-phase screening, which identified 4245 interactions. The false positive rate of the Y2H assay was estimated to be 3.1%. Pull-down assays confirmed the robustness of our Y2H. The average interactions per node for CLas Y2H interactome were approximately 15.6, significantly higher than free-living bacteria, indicating genome reduction has led to a multi-function of proteins. PPIs provide clues for functions of 371 uncharacterized proteins of CLas. Forty HUB node proteins were identified which might play critical roles in CLas, including a quinone oxidoreductase and LysR that are known to protect bacteria against oxidative stress. This explains why CLas survives well in the phloem even though it triggers immune-mediated disease, systemic and chronic production of reactive oxygen species, and phloem cell death. This PPI database facilitates the investigation of CLas cellular biochemistry and physiology, functions of uncharacterized proteins, and pathogenicity mechanisms of the pathogen.
Focus-aware Response Generation in Inquiry Conversation

Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation history, neglecting the focus can limit performance in inquiry conversation where the order of the focuses plays there a key role. In this paper, we investigate the problem of response generation in inquiry conversation by taking the focus into consideration. We propose a novel Focus-aware Response Generation (FRG) method by jointly optimizing a multi-level encoder and a set of focal decoders to generate several candidate responses that correspond to different focuses. Additionally, a focus ranking module is proposed to predict the next focus and rank the candidate responses. Experiments on two orthogonal inquiry conversation datasets (ju-dicial, medical domain) demonstrate that our method generates results significantly better in automatic metrics and human evaluation compared to the state-of-the-art approaches.
Contrastive Learning with Generated Representations for Inductive Knowledge Graph Embedding

With the evolution of Knowledge Graphs (KGs), new entities emerge which are not seen before. Representation learning of KGs in such an inductive setting aims to capture and transfer the structural patterns from existing entities to new entities. However, the performance of existing methods in inductive KGs are limited by sparsity and implicit transfer. In this paper, we propose VMCL , a C ontrastive L earning (CL) framework with graph guided V ariational autoencoder on M eta-KGs in the inductive setting. We first propose representation generation to capture the encoded and generated representations of entities, where the generated variations can augment the representation space with complementary features. Then, we design two CL objectives that work across entities and meta-KGs to simulate the transfer mode. With extensive experiments we demonstrate that our proposed VMCL can significantly outperform previous state-of-the-art baselines.