Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node-node and edge-edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.

Reconstructing an object behind a scattering medium needs to tackle different light scatterings inside the medium and in the free space. Major approaches, e.g., diffuse optical tomography and non-line-of-sight imaging, address either of the light scatterings. Confocal diffuse tomography (CDT) considers the media acting as a diffuse kernel on the free-space scattering and recovers the object by deconvolving the inside-medium scattering. Inspired by CDT, we present a Neural De-Scatterer to solve this challenging problem. We exploit neural implicit fields to represent the the free-space scattering and use a multilayer perceptron (MLP) to learn the density and albedo of the hidden object. Furthermore, we tailor a bi-directional training strategy to optimize the MLP with forward and backward losses and employ hash encoding for memory and computation efficiency. The Neural De-Scatterer enables us to reconstruct objects at arbitrary resolution. Comprehensive experiments with synthetic and real measurements demonstrate that our Neural De-Scatterer outperforms state-of-the-art methods. Our data and code are publicly available.

Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem. Most physics-based NLOS imaging methods address the complexity of this problem by assuming three-bounce reflections and no self-occlusion. However, these assumptions may break down for objects with large depth variations, preventing physics-based algorithms from accurately reconstructing the details and high-frequency information. On the other hand, while learning-based methods can avoid these assumptions, they may struggle to reconstruct details without specific designs due to the spectral bias of neural networks. To overcome these issues, we propose a novel approach that enhances physics-based NLOS imaging methods by introducing a learnable inverse kernel in the Fourier domain and using an attention mechanism to improve the neural network to learn high-frequency information. Our method is evaluated on publicly available and new synthetic datasets, demonstrating its commendable performance compared to prior physics-based and learning-based methods, especially for objects with large depth variations. Moreover, our approach generalizes well to real data and can be applied to tasks such as classification and depth reconstruction. We will make our code and dataset publicly available: https://sci2020.github.io.

Automated Program Repair (APR) techniques have drawn wide attention from both academia and industry. Meanwhile, one main limitation with the current state-of-the-art APR tools is that patches passing all the original tests are not necessarily the correct ones wanted by developers, i.e., the plausible patch problem. To date, various Patch-Correctness Checking (PCC) techniques have been proposed to address this important issue. However, they are only evaluated on very limited datasets as the APR tools used for generating such patches can only explore a small subset of the search space of possible patches, posing serious threats to external validity to existing PCC studies. In this paper, we construct an extensive PCC dataset, PraPatch (the largest manually labeled PCC dataset to our knowledge), to revisit all nine state-of-the-art PCC techniques. More specifically, our PCC dataset PraPatch includes 1,988 patches generated from the recent PraPR APR tool, which leverages highly-optimized bytecode-level patch executions and can exhaustively explore all possible plausible patches within its large predefined search space (including well-known fixing patterns from various prior APR tools). Our extensive study of representative PCC techniques on PraPatch has revealed various findings, including: 1) the assumption made by existing static PCC techniques that correct patches are more similar to buggy code than incorrect plausible patches no longer holds, 2) state-of-the-art learning-based techniques tend to suffer from the dataset overfitting problem, 3) while dynamic techniques overall retain their effectiveness on our new dataset, their performance drops substantially on patches with more complicated changes and 4) the very recent naturalness-based techniques can substantially outperform traditional static techniques and could be a promising direction for PCC. Based on our findings, we also provide various guidelines/suggestions for advancing PCC in the near future.

Nearly all existing human pose estimation techniques address the problem under the line-of-sight (LOS) setting. Many real-life applications such as rescue missions and autonomous driving, in contrast, require estimating the pose of hidden subjects. In this paper, we present a non-line-of-sight (NLOS) pose estimator, which produces a skeletal representation of hidden human poses. A brute-force approach would first conduct albedo reconstruction of a hidden subject and then apply LOS pose estimation. We show that such an implementation does not effectively exploit features unique to NLOS and subsequently yields artifacts such as missing joints. We instead first generate a comprehensive NLOS human pose dataset of 19 subjects under 9 motions. We then present a spatially aware deep learning technique based on convolutional neural networks that explicitly employ NLOS features. Comprehensive experiments on both synthetic and real data show that our new estimator is both effective and robust and can be seamlessly integrated into learning-based NLOS scene reconstruction. Our HiddenPose transient dataset contains synthetic transients with ground-truths of the volumes and the joints and real-world transients captured from our NLOS imaging system. Extensive assessments demonstrate that the HiddenPose transient dataset is valuable for effective NLOS research. We will make our data and code publicly available.

While mobile applications (i.e., apps) are becoming capable of handling various needs from users, their increasing access to sensitive data raises privacy concerns. To inform such sensitive behaviors to users, existing techniques propose to automatically identify explanatory sentences from app descriptions; however, many sensitive behaviors are not explained in the corresponding app descriptions. There also exist general techniques that translate code to sentences. However, these techniques lack the vocabulary to explain the uses of sensitive data and fail to consider the context (i.e., the app functionalities) of the sensitive behaviors. To address these limitations, we propose DescribeCtx, a context-aware description synthesis approach that trains a neural machine translation model using a large set of popular apps, and generates app-specific descriptions for sensitive behaviors. Specifically, DescribeCtx encodes three heterogeneous sources as input, i.e., vocabularies provided by privacy policies, behavior summary provided by the call graphs in code, and contextual information provided by GUI texts. Our evaluations on 1,262 Android apps show that, compared with existing baselines, DescribeCtx produces more accurate descriptions (24.96 in BLEU) and achieves higher user ratings with respect to the reference sentences manually identified in the app descriptions.

As an essential step of form processing, form classification has attracted much attention from researchers. However, for the forms with similar layout, most of the previous classification methods still suffer from two issues: huge variation among areas of user-filled-in data and insufficient discriminative identifiers in areas of preprinted data. In this paper, we propose a novel Mixed Gaussian Weighted Mask (MGWM) based method to identify forms with similar layouts by leveraging the multiple information extracted from areas of user-filled-in data, areas of preprinted data and dithering data of a form. The proposed method utilizes a combination of three Gaussian weighted masks to mitigate the impact of noise from areas of user-filled-in data, layout consistency and position dithering among form images respectively. Experimental results show that the proposed method achieves more than 85% classification accuracy on a number of forms and outperforms the state-of-the-art form classification method.