Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE) -- A novel ICA-based algorithm for removing myoelectric artifacts from EEG -- Part 2.

Extraction of the movement-related high-gamma (80 - 160 Hz) in electroencephalogram (EEG) from traumatic brain injury (TBI) patients who have had hemicraniectomies, remains challenging due to a confounding bandwidth overlap with surface electromyogram (EMG) artifacts related to facial and head movements. In part 1, we described an augmented independent component analysis (ICA) approach for removal of EMG artifacts from EEG, and referred to as EMG Reduction by Adding Sources of EMG (ERASE). Here, we tested ERASE on EEG recorded from six TBI patients with hemicraniectomies while they performed a thumb flexion task. ERASE removed a mean of 52 +/- 12% (mean +/- S.E.M) (maximum 73%) of EMG artifacts. In contrast, conventional ICA removed a mean of 27 +/- 19\% (mean +/- S.E.M) of EMG artifacts from EEG. In particular, high-gamma synchronization was significantly improved in the contralateral hand motor cortex area within the hemicraniectomy site after ERASE was applied. We computed fractal dimension (FD) of EEG high-gamma on each channel. We found relative FD of high-gamma over hemicraniectomy after applying ERASE were strongly correlated to the amplitude of finger flexion force. Results showed that significant correlation coefficients across the electrodes related to thumb flexion averaged 0.76, while the coefficients across the homologous electrodes in non-hemicraniectomy areas were nearly 0. Across all subjects, an average of 83% of electrodes significantly correlated with force was located in the hemicraniectomy areas after applying ERASE. After conventional ICA, only 19% of electrodes with significant correlations were located in the hemicraniectomy. These results indicated that the new approach isolated electrophysiological features during finger motor activation while selectively removing confounding EMG artifacts.
A Note on the Secrecy Capacity of the Multiple-Antenna Wiretap Channel

The secrecy capacity of the multiple-antenna wiretap channel under the average total power constraint was recently characterized, independently, by Khisti and Wornell and Oggier and Hassibi using a Sato-like argument and matrix analysis tools. This paper presents an alternative characterization of the secrecy capacity of the multiple-antenna wiretap channel under a more general matrix constraint on the channel input using a channel-enhancement argument. This characterization is by nature information-theoretic and is directly built on the intuition regarding to the optimal transmission strategy in this communication scenario.
Black carbon simulations using a size‐ and mixing‐state‐resolved three‐dimensional model: 1. Radiative effects and their uncertainties

This study quantifies how uncertainties in the size distribution and mixing state parameters of black carbon (BC) emissions translate into the uncertainties in BC radiative effects by using a particle‐size‐ and mixing‐state‐resolved three‐dimensional model, the Weather Research and Forecasting model with chemistry (WRF‐chem) with the Aerosol Two‐dimensional bin module for foRmation and Aging Simulation (ATRAS) and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). The WRF‐chem/ATRAS‐MOSAIC model can explicitly calculate BC processes in the atmosphere, such as BC aging due to condensation and coagulation and the resulting enhancement of absorption and cloud condensation nuclei activity, with 12 size and 10 BC mixing state bins (128 bins in total). Fifteen model simulations perturbing the emission parameters within their uncertainties are conducted over East Asia (spring 2009) to understand which parameters and processes are important and which are associated with the uncertainty in evaluating BC radiative effects. The simulations reveal a large variability (uncertainty) of BC optical and radiative variables over the East Asian region (the variability is 58–99%), which corresponds to ranges of BC radiative effect of 1.6–2.8 W m−2 at the top of the atmosphere and from −5.2 to −2.1 W m−2 at the surface over East Asia. BC optical and radiative variables are 3 to 5 times sensitive to the size and the mixing state in emissions than BC mass concentrations (the variability is 20%). The two main causes of the difference in sensitivity are the reduction of the variability of BC mass concentrations by coagulation and the enhancement of the variability of BC absorption by resolving BC mixing state. These complicated responses of aerosol processes can be calculated for the first time using a detailed aerosol model such as ATRAS. The results suggest that the following two points are important in the estimation of BC radiative effects: (1) reduction of the uncertainties in the aerosol size distribution and mixing state in emissions and (2) improvement of the representation of BC mixing state and absorption enhancement in aerosol models because most models do not treat them sufficiently.
Future Generation Computer Systems

Social networks are commonly used by citizens as a communication channel for sharing their messages about a crisis situation and by emergency operation centers as a source of information for improving their situation awareness. However, to utilize this source of information, emergency operators and decision makers have to deal with large and unstructured data, the content, reliability, quality, and relevance of whichmayvarygreatly.Inthispaper,toaddressthischallenge,weproposeavisualanalyticssolutionthat filters and visualizes relevant information extracted from Twitter. The tool offers multiple visualizations to provide emergency operators with different points of view for exploring the data in order to gain a better understanding of the situation and take informed courses of action. We analyzed the scope of the problem through an exploratory study in which 20 practitioners answered questions about the integration of social networks in the emergency management process. This study inspired the design of a visualization tool, which was evaluated in a controlled experiment to assess its effectiveness for exploring spatial and temporal data. During the experiment, we asked 12 participants to perform 5 tasks related to data exploration and fill a questionnaire about their experience using the tool. One of the most interesting results obtained from the evaluation concerns the effectiveness of combining several visualization techniques to support different strategies for solving a problem and making decisions. © 2018 Elsevier B.V. All rights reserved
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

For training MobileNet V2 models (Sec. 3), we use 8 GPUs, an SGD optimizer with momentum of 0.9, a batch size of 32 per GPU, input resolution of 224×224, weight decay of 4 × 10−5, learning rate schedule with 5-epoch warmup, initial value of 0.1 and cosine annealing for 100 epochs. For the data augmentation, we only use random cropping and left-right flipping, as a common practice. For training RepLKNet models (Sec. 4.2),we use 32 GPUs and a batch size of 64 per GPU to train for 120 epochs. The optimizer is AdamW [10] with momentum of 0.9 and weight decay of 0.05. The learning rate setting includes an initial value of 4 × 10−3, cosine annealing and 10-epoch warm-up. For the data augmentation and regularization, we use RandAugment [4] (“rand-m9-mstd0.5-inc1” as implemented by timm [15]), label smoothing coefficient of 0.1, mixup [18] with α = 0.8, CutMix with α = 1.0, Rand Erasing [19] with probability of 25% and Stochastic Depth with a drop-path rate of 30%, following the recent works [1, 8, 9, 12]. The RepLKNet-31B reported in Sec. 4.3 is trained with the same configurations except the epoch number of 300 and drop-path rate of 50%. For finetuning the 224×224-trained RepLKNet-31B with 384×384, we use 32 GPUs, a batch size of 32 per GPU, initial learning rate of 4 × 10−4, cosine annealing, 1-epoch warm-up, 30 epochs, model EMA (Exponential Moving Average) with momentum of 10−4, the same RandAugment as above but no CutMix nor mixup.
Rainfall Intensity-Duration-Frequency Relationships for Andhra Pradesh, India: Changing Rainfall Patterns and Implications for Runoff and Groundwater Recharge

Accurate and current rainfall characterization is an important tool for water-related system design and management. Updated rainfall intensity-duration-frequency (IDF) relationships in peninsular India were developed; impacts on runoff and groundwater recharge attributable to changes in rainfall characteristics are discussed. Two data sets were used from gage in Hyderabad city, the capital of Andhra Pradesh: hourly rainfall data for the 19 years from 1993–2011 and daily rainfall data for the 30 years from 1982–2011. Hourly data were used to develop updated rainfall IDF relationships; daily data were used for trend analysis of threshold-based rainfall events. IDF curves were developed for return periods of 2, 5, 10, 15, 25, 50, 75, and 100 years for 1-, 2-, 4-, 8-, and 24-h durations. The updated IDF relationships showed a significant change in rainfall characteristics compared with older relationships for the region surrounding Hyderabad, India; they showed greater rainfall intensities across all durations and return periods. Greater intensity storms may reduce groundwater recharge and increase runoff, making the surface storage of runoff increasingly important to enhance recharge and reduce flooding risks.
Geometric Transformer for Fast and Robust Point Cloud Registration Supplementary Material A. Network Architecture Details

First, we provide the detailed computation for our geometric structure embedding. The geometric structure embedding encodes distances in superpoint pairs and angles in superpoint triplets. Due to the continuity of the sinusoidal embedding function [16], we use it instead of learned embedding vectors to compute the pair-wise distance embedding and the triplet-wise angular embedding. Given the distance ρi,j =‖p̂i − p̂j‖2 between p̂i and p̂j , the pair-wise distance embedding ri,j is computed as:  r i,j,2k = sin( di,j/σd 100002k/dt )
GEOTECHNICAL PROPERTIES OF BAHMANSHIR SERIES, SOUTHWEST KHUZESTAN, IRAN*

Holocene in Southwest Khuzestan is represented by a fluvio-tidal succession termed the Bahmanshir Series which consists of two clayey layers separated by silty sand to silt beds. This paper describes the geotechnical properties of the clayey layers that have wide exposure in the lowlands of the Persian Gulf coast. Studied layers are generally clay sized and consist of a lesser percentage of clay minerals. Most of the clayey layers have intermediate plasticity, and low potential for expansion. The variation of physical and engineering properties of the layers from base to top and along the Bahmanshir River is investigated in this research. The engineering properties of the beds seem to be affected by tidal and river flow currents and totally by their formation sedimentary environments. Links of the geotechnical data with geological features as formation sources in the upstream of the basin, the cementation due to the capillary feature in the floodplain sub-environment and the flocculated texture of clays in the brackish water, have been discussed in this paper. Overall, it is observed that the geotechnical data could be correlated with the geological features of the Bahmanshir Series.
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review

Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Due to the growing popularity of NeRF and its expanding research area, we present a comprehensive survey of NeRF papers from the past two years. Our survey is organized into architecture and application-based taxonomies and provides an introduction to the theory of NeRF and its training via differentiable volume rendering. We also present a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section.
A Theory-Driven Self-Labeling Reﬁnement Method for Contrastive Representation Learning (Supplementary File)

This supplementary document contains more additional experimental details and the technical proofs of convergence results of the NeurIPS’21 submission entitled “A Theory-Driven Self-Labeling Reﬁnement Method for Contrastive Representation Learning”. It is structured as follows. In Appendix A, we provides more experimental details, including training algorithm, network architecture, optimizer details, loss construction and training cost of SANE. Appendix B presents the proof and details of the main results, namely, Theorem 1, in Section 2, which analyzes the generalization performance of MoCo. Next, Appendix C introduces the proof roadmap and details of the main results, i.e. Theorem 2, in Section 3.1. Since the proof framework is relatively complex, we ﬁrst introduce some necessary preliminaries, including notations, conceptions and assumptions that are veriﬁed in subsequent analysis in Appendix C.2. 4. Then we provide the proofs of Theorem 2 in Appendix C.2. Speciﬁcally, we ﬁrst introduce the proof roadmap of Theorem 2 in Appendix C.2. 1. Then we present several auxiliary theories in Appendix C.2. 2. Next, we prove our Theorem 2 in Appendix C.2. 3. Finally, we present all proof details of auxiliary theories in Appendix C.2. 4.
Learning Part Segmentation through Unsupervised Domain Adaptation from

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Robust Deep Reinforcement Learning through Regret Neighborhoods

Deep Reinforcement Learning (DRL) policies have been shown to be vulnerable to small adversarial noise in observations. Such adversarial noise can have disastrous consequences in safety-critical environments. For instance, a self-driving car receiving adversarially perturbed sensory observations about nearby signs (e.g., a stop sign physically altered to be perceived as a speed limit sign) or objects (e.g., cars altered to be recognized as trees) can be fatal. Existing approaches for making RL algorithms robust to an observation-perturbing adversary have focused on reactive approaches that iteratively improve against adversarial examples generated at each iteration. While such approaches have been shown to provide improvements over regular RL methods, they are reactive and can fare signiﬁcantly worse if certain categories of adversarial examples are not generated during training. To that end, we pursue a more proactive approach that relies on directly optimizing a well-studied robustness measure, regret instead of expected value. We provide a principled approach that minimizes maximum regret over a "neighborhood" of observations to the received "observation". Our regret criterion can be used to modify existing value-and policy-based Deep RL methods. We demonstrate that our approaches provide a signiﬁcant improvement in performance across a wide variety of benchmarks against leading approaches for robust Deep RL.
MAC-DO: An Efficient Output-Stationary GEMM Accelerator for CNNs Using DRAM Technology

DRAM-based in-situ accelerators have shown their potential in addressing the memory wall challenge of the traditional von Neumann architecture. Such accelerators exploit charge sharing or logic circuits for simple logic operations at the DRAM subarray level. However, their throughput is limited due to low array utilization, as only a few row cells in a DRAM array participate in operations while most rows remain deactivated. Moreover, they require many cycles for more complex operations such as a multi-bit multiply-accumulate (MAC) operation, resulting in significant data access and movement and potentially worsening power efficiency. To overcome these limitations, this paper presents MAC-DO, an efficient and low-power DRAM-based in-situ accelerator. Compared to previous DRAM-based in-situ accelerators, a MAC-DO cell, consisting of two 1T1C DRAM cells (two transistors and two capacitors), innately supports a multi-bit MAC operation within a single cycle, ensuring good linearity and compatibility with existing 1T1C DRAM cells and array structures. This achievement is facilitated by a novel analog computation method utilizing charge steering. Additionally, MAC-DO enables concurrent individual MAC operations in each MAC-DO cell without idle cells, significantly improving throughput and energy efficiency. As a result, a MAC-DO array efficiently can accelerate matrix multiplications based on output stationary mapping, supporting the majority of computations performed in deep neural networks (DNNs). Furthermore, a MAC-DO array efficiently reuses three types of data (input, weight and output), minimizing data movement.
Genetic framework of Neogene–Quaternary basin closure process in central Turkey

Central Turkey represents the only orogenic plateau in the Mediterranean region. Also, the largest closed drainage basin and the largest intracontinental basin of Turkey, the Lake Tuz Basin, is located in this region. Results from a three-dimensional (3-D) computer modeling study of the Lake Tuz Basin indicate a southward-deepening freshwater lake basin with great depth in the Mio–Pliocene, which regressed toward the north during the Plio–Quaternary into the shallow saline lake basin it is today. The spatio-temporal variations of Neogene and Quaternary deposits reflect the main effects of internal forces (isostasy>volcanism>faulting) that were caused by lithospheric slab breakoff and subsequent asthenospheric upwelling under central Turkey. Climatic change played a relatively minor role during these periods and was closely associated with the results of internal forces.
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During a long period of time we are combating overfitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc. In this paper, we present DisturbLabel, an extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration. Although it seems weird to intentionally generate incorrect training labels, we show that DisturbLabel prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets. To the best of our knowledge, DisturbLabel serves as the first work which adds noises on the loss layer. Meanwhile, DisturbLabel cooperates well with Dropout to provide complementary regularization functions. Experiments demonstrate competitive recognition results on several popular image recognition datasets.
Redescription and taxonomic status of specimens of Episcoposaurus and Typothorax, the earliest known aetosaurs (Archosauria: Suchia) from the Upper Triassic of western North America, and the problem of proxy “holotypes”

ABSTRACT Historic type and referred material of the aetosaurian taxa Typothorax coccinarum, Episcoposaurus horridus and Episcoposaurus haplocerus are redescribed and the non-aetosaurian material identified and removed, a task previously considered “hopeless”. Reexamination of the original material reveals that the holotypes of E. haplocerus and probably T. coccinarum are not diagnosable at the species level and therefore are nomena dubia. The next available names for material referred to these taxa are Desmatosuchus spurensis and E. horridus respectively, although it may be more desirable for reasons of taxonomic stability to attempt to petition for a neotype in the latter case. The redescription of historical specimens is necessary to determine their nomenclatural validity. The use of referred specimens as proxy “type” specimens is problematic, as these referrals were originally made not on the basis of apomorphies, but rather on biostratigraphic and/or geographical assumptions which are inherently circular and cannot be unambiguously supported.
Maternal circulating miRNAs contribute to negative pregnancy outcomes by altering placental transcriptome and fetal vascular dynamics

Circulating miRNAs the in blood are promising biomarkers for predicting pregnancy complications and adverse birth outcomes. Previous work identified 11 gestationally elevated maternal circulating miRNAs (HEamiRNAs) that predicted infant growth deficits following prenatal alcohol exposure and regulated epithelial–mesenchymal transition in the placenta. Here we show that a single intravascular administration of pooled murine-conserved HEamiRNAs to pregnant mice on gestational day 10 (GD10) attenuates umbilical cord blood flow during gestation, explaining the observed intrauterine growth restriction (IUGR), specifically decreased fetal weight, and morphometric indices of cranial growth. Moreover, RNAseq of the fetal portion of the placenta demonstrated that this single exposure has lasting transcriptomic changes, including upregulation of members of the Notch pathway (Dll4, Rfng, Hey1), which is a pathway important for trophoblast migration and differentiation. Weighted gene co-expression network analysis also identified chemokine signaling, which is responsible for regulating immune cell-mediated angiogenesis in the placenta, as an important predictor of fetal growth and head size. Our data suggest that HEamiRNAs perturb the expression of placental genes relevant for angiogenesis, resulting in impaired umbilical cord blood flow and subsequently, IUGR.
Restless legs syndrome affects sleep in de novo Parkinson’s disease patients

Restless legs syndrome (RLS) is common in Parkinson’s disease (PD) patients and can affect the motor symptoms and non-motor symptoms (NMSs) of PD patients. The aim of this study was to identify the clinical factors affected by RLS in patients with PD. We included 369 de novo PD patients. RLS was assessed via face-to-face interviews and the motor symptoms and NMSs of the patients were assessed using relevant scales. RLS frequency in the patients was 12.2% (45/369). PD patients with RLS (PD-RLS) exhibited a greater global Pittsburgh Sleep Quality Index (PSQI) score than those without RLS (PD-No RLS). PD-RLS exhibited significantly greater scores in the daytime dysfunction and sleep disturbances components of the PSQI than PD-No RLS. PD-RLS exhibited a significantly greater score in the cardiovascular, sleep/fatigue, and attention/memory subdomain of the Non-Motor Symptoms Scale than PD-No RLS. The International RLS Study Group rating scale score was significantly related to PSQI components scores in the sleep disturbances, sleep latency, habitual sleep efficiency, and subjective sleep quality. RLS frequency in de novo PD patients is higher than that in the general population, and the main NMS affected by RLS in these patients is sleep disturbances. Therefore, it is necessary to manage RLS in PD patients with sleep disturbances.