Miracle-dz/UniFAR-FTUs huggingface dataset:
Converging evidence for functional and structural segregation within the left ventral occipitotemporal cortex in reading

Significance Understanding the function, structure, and connections of the ventral occipitotemporal cortex (vOTC) is critical to unravel the neural mechanisms determining how our brain accomplishes reading. Here, we identified two segregated areas along the vOTC posterior–anterior axis involved in two different aspects of visual word recognition: a posterior part responsible for visual feature extraction and an anterior part involved in integrating information from and to the language network. Converging evidence from functional, structural, microarchitectonic, and behavioral measurements consistently confirmed this posterior–anterior segregation in the vOTC and, importantly, revealed the pathways involved in different processes supporting reading. The ventral occipitotemporal cortex (vOTC) is crucial for recognizing visual patterns, and previous evidence suggests that there may be different subregions within the vOTC involved in the rapid identification of word forms. Here, we characterize vOTC reading circuitry using a multimodal approach combining functional, structural, and quantitative MRI and behavioral data. Two main word-responsive vOTC areas emerged: a posterior area involved in visual feature extraction, structurally connected to the intraparietal sulcus via the vertical occipital fasciculus; and an anterior area involved in integrating information with other regions of the language network, structurally connected to the angular gyrus via the posterior arcuate fasciculus. Furthermore, functional activation in these vOTC regions predicted reading behavior outside of the scanner. Differences in the microarchitectonic properties of gray-matter cells in these segregated areas were also observed, in line with earlier cytoarchitectonic evidence. These findings advance our understanding of the vOTC circuitry by linking functional responses to anatomical structure, revealing the pathways of distinct reading-related processes.
NDroid: Toward Tracking Information Flows Across Multiple Android Contexts

For performance and compatibility reasons, developers tend to use native code in their applications (or simply apps). This makes a bidirectional data flow through multiple contexts, i.e., the Java context and the native context, in Android apps. Unfortunately, this interaction brings serious challenges to existing dynamic analysis systems, which fail to capture the data flow across different contexts. In this paper, we first performed a large-scale study on apps using native code and reported some observations. Then, we identified several scenarios where data flow cannot be tracked by existing systems, leading to uncaught information leakage. Based on these insights, we designed and implemented NDroid, an efficient dynamic taint analysis system that could track the data flow between both Java context and native context. The evaluation of real apps demonstrated the effectiveness of NDroid in identifying information leakage with reasonable performance overhead.
Slipping Control Algorithms for Object Manipulation with Sensorized Parallel Grippers

Parallel jaw grippers have a limited dexterity, however they can still be used for in-hand manipulation tasks, such as pivoting or other controlled sliding motions of the grasped object. A rotational sliding maneuver is challenging since the grasped object can easily slip if the grip force is not properly adjusted to allow rotational sliding while avoiding translational sliding at the same time. This paper has a twofold aim. First, it intends to refine control algorithms to avoid both rotational and linear slippage, already presented by the authors, by proposing a novel sliding motion model that leads to a grip force as small as possible to avoid slippage, so as to enlarge the set of fragile and deformable objects that can be safely grasped with this approach. Second, the paper exploits the motion model to set up a new algorithm for controlled rotational sliding, thus enabling challenging in-hand manipulation actions. All control algorithms are sensor-based, exploiting a sensorized gripper equipped with a six-axis force/tactile sensor, which provides contact force and torque measurements as well as orientation of the object with respect to the gripper. A set of experiments are executed on a Kuka iiwa showing how the proposed control algorithms are effective to both avoid slippage and allow a controlled sliding motion.
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection

We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance. A multi-loss function is designed to incorporate uncertainty estimations predicted by auxiliary output layers. Using our proposed method, the network ignores to train from noisy samples, and focuses more on informative ones. We validate our method on the KITTI object detection benchmark. Our method surpasses the baseline method which does not explicitly estimate uncertainties by up to nearly 9% in terms of Average Precision (AP). It also produces state-of-the-art results compared to other methods, while running with an inference time of only 72ms. In addition, we conduct extensive experiments to understand how aleatoric uncertainties behave. Extracting aleatoric uncertainties brings almost no additional computation cost during the deployment, making our method highly desirable for autonomous driving applications.
Deciphering the Contribution of Oriens-Lacunosum/Moleculare (OLM) Cells to Intrinsic θ Rhythms Using Biophysical Local Field Potential (LFP) Models

Abstract Oscillations in local field potentials (LFPs) are prevalent and contribute to brain function. An understanding of the cellular correlates and pathways affecting LFPs is needed, but many overlapping pathways in vivo make this difficult to achieve. A prevalent LFP rhythm in the hippocampus associated with memory processing and spatial navigation is the θ (3–12 Hz) oscillation. θ rhythms emerge intrinsically in an in vitro whole hippocampus preparation and this reduced preparation makes it possible to assess the contribution of different cell types to LFP generation. We focus on oriens-lacunosum/moleculare (OLM) cells as a major class of interneurons in the hippocampus. OLM cells can influence pyramidal (PYR) cells through two distinct pathways: by direct inhibition of PYR cell distal dendrites, and by indirect disinhibition of PYR cell proximal dendrites. We use previous inhibitory network models and build biophysical LFP models using volume conductor theory. We examine the effect of OLM cells to ongoing intrinsic LFP θ rhythms by directly comparing our model LFP features with experiment. We find that OLM cell inputs regulate the robustness of LFP responses without affecting their average power and that this robust response depends on coactivation of distal inhibition and basal excitation. We use our models to estimate the spatial extent of the region generating LFP θ rhythms, leading us to predict that about 22,000 PYR cells participate in intrinsic θ generation. Besides obtaining an understanding of OLM cell contributions to intrinsic LFP θ rhythms, our work can help decipher cellular correlates of in vivo LFPs.
Deep Domain Adaptation in Action Space

In the general settings of supervised learning, human action recognition has been a widely studied topic. The classiﬁers learned in this setting assume that the training and test data have been sampled from the same underlying probability distribution. However, in most of the practical scenarios, this assumption is not true, resulting in a sub-optimal performance of the classiﬁers. This problem, referred to as Domain Shift, has been extensively studied, but mostly for image/object classiﬁcation task. In this paper, we investigate the problem of Domain Shift in action videos, an area that has remained under-explored, and propose two new approaches named Action Modeling on Latent Subspace (AMLS) and Deep Adversarial Action Adaptation (DAAA). In the AMLS approach, the action videos in the target domain are modeled as a sequence of points on a latent subspace and adaptive kernels are successively learned between the source domain point and the sequence of target domain points on the manifold. In the DAAA approach, an end-to-end adversarial learning framework is proposed to align the two domains. The action adaptation experiments were conducted using various combinations of multi-domain action datasets, including six common classes of Olympic Sports and UCF50 datasets and all classes of KTH, MSR and our own SonyCam datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.
Linear Classification in Speech-Based Objective Differential Diagnosis of Parkinsonism

Parkinsonism refers to Parkinsons disease (PD) and Atypical parkinsonian syndromes (APS). Speech disorder is a common and early symptom in Parkinsonism which makes speech analysis a very important research area for the purpose of early diagnosis. Most of research have however focused on discrimination between PD and healthy controls. Such research does not take into account the fact that PD and APS syndromes are very similar in early disease stages. The main problem that has to be addressed first is then differential diagnosis: discrimination between PD and APS and within APS. This paper is a continuation of an earlier pioneer work in differential diagnosis where we mostly address the machine learning problem due to the small amount of training data. We show that classical linear and generalized linear models can provide interpretable and robust classifiers in term of accuracy and generalization ability.
Bidirectional Long Short-Term Memory Variational Autoencoder

Variational Autoencoder (VAE) has achieved promising success since its emergence. In recent years, its various variants have been developed, especially those works which extend VAE to handle sequential data [1, 2, 5, 7]. However, these works either do not generate sequential latent variables, or encode latent variables only based on inputs from earlier time-steps. We believe that in real-world situations, encoding latent variables at a speciﬁc time-step should be based on not only previous observations, but also succeeding samples. In this work, we emphasize such fact and theoretically derive the bidirectional Long Short-Term Memory Variational Autoencoder ( bLSTM-VAE ), a novel variant of VAE whose encoders and decoders are implemented by bidirectional Long Short-Term Memory (bLSTM) networks. The proposed bLSTM-VAE can encode sequential inputs as an equal-length sequence of latent variables. A latent variable at a speciﬁc time-step is encoded by simultaneously processing observations from the ﬁrst time-step till current time-step in a forward order and observations from current time-step till the last time-step in a backward order. As a result, we consider that the proposed bLSTM-VAE could learn latent variables reliably by mining the contextual information from the whole input sequence. In order to validate the proposed method, we apply it for gesture recognition using 3D skeletal joint data. The evaluation is conducted on the ChaLearn Look at People gesture dataset and NTU RGB+D dataset. The experimental results show that combining with the proposed bLSTM-VAE , the classiﬁcation network performs better than when combining with a standard VAE, and also outperforms several state-of-the-art methods.
Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis

While the neurobiology of simple and habitual choices is relatively well known, our current understanding of goal-directed choices and planning in the brain is still limited. Theoretical work suggests that goal-directed computations can be productively associated to model-based (reinforcement learning) computations, yet a detailed mapping between computational processes and neuronal circuits remains to be fully established. Here we report a computational analysis that aligns Bayesian nonparametrics and model-based reinforcement learning (MB-RL) to the functioning of the hippocampus (HC) and the ventral striatum (vStr)–a neuronal circuit that increasingly recognized to be an appropriate model system to understand goal-directed (spatial) decisions and planning mechanisms in the brain. We test the MB-RL agent in a contextual conditioning task that depends on intact hippocampus and ventral striatal (shell) function and show that it solves the task while showing key behavioral and neuronal signatures of the HC—vStr circuit. Our simulations also explore the benefits of biological forms of look-ahead prediction (forward sweeps) during both learning and control. This article thus contributes to fill the gap between our current understanding of computational algorithms and biological realizations of (model-based) reinforcement learning.
“Boom, Headshot!”: Violent first‐person shooter (FPS) video games that reward headshots train individuals to aim for the head when shooting a realistic firearm

When shooting a gun at a human target, firearms training instructors teach individuals to shoot for the upper torso because it is the largest lethal target on the human body. In contrast, violent first-person shooter (FPS) video games reward players for headshots. The head is the smallest lethal target, and requires careful aim to hit. In this experiment, participants were randomly assigned to play a violent FPS game with humanoid targets that rewarded headshots, a nonviolent shooting game that punished hits to bull's-eye targets with faces, or a nonviolent non-shooting game. After gameplay, participants shot 16 "bullets" from a realistic gun at a life-sized human-shaped mannequin. Participants were told to hit the mannequin with as many bullets as possible, but they were not told where to aim. Consistent with operant conditioning theory, participants who played a violent FPS game that rewarded headshots had the most hits to the mannequin's head. Participants whose favorite video games were violent shooting games also had the most hits to the mannequin's head. These findings suggest that FPS games that reward headshots can influence people to aim for the head with a realistic gun after the game is turned off, even though the head is a much smaller target to hit and they are much less likely to hit another body part if they miss. FPS games are often used to train soldiers and police officers, but these findings suggest that such games might train individuals to hit the wrong part of the body.
Towards Accessories-Aware Ear Recognition

Automatic ear recognition is gaining popularity within the research community due to numerous desirable properties, such as high recognition performance, the possibility of capturing ear images at a distance and in a covert manner, etc. Despite this popularity and the corresponding research effort that is being directed towards ear recognition technology, open problems still remain. One of the most important issues stopping ear recognition systems from being widely available are ear occlusions and accessories. Ear accessories not only mask biometric features and by this reduce the overall recognition performance, but also introduce new non-biometric features that can be exploited for spoofing purposes. Ignoring ear accessories during recognition can, therefore, present a security threat to ear recognition and also adversely affect performance. Despite the importance of this topic there has been, to the best of our knowledge, no ear recognition studies that would address these problems. In this work we try to close this gap and study the impact of ear accessories on the recognition performance of several state-of-the-art ear recognition techniques. We consider ear accessories as a tool for spoofing attacks and show that CNN-based recognition approaches are more susceptible to spoofing attacks than traditional descriptor-based approaches. Furthermore, we demonstrate that using inpainting techniques or average coloring can mitigate the problems caused by ear accessories and slightly outperforms (standard) black color to mask ear accessories.
The Relation Between Morphometric Features and Susceptibility to Mandibular Angle Fractures

Abstract The study was aimed to determine possible relations between skeletal morphologic parameters to mandibular angle fractures. Retrospective study of 100 patients suffering from mandibular fractures and treated in the Rambam Health Care Campus between the years 2013 and 2017 was conducted. Predictor variable was facial skeletal morphologic features as derived from the measurements. Outcome variable was mandibular angle fractures. About 42 patients suffered from angle fractures. Gonial angle (131.3° versus 118.1°), condylar neck width (8.3 mm versus 6.8 mm sagittaly and 7.1 mm versus 5.8 mm horizontally), and wisdom tooth prevalence and impaction were positively correlated to the fracture. Ramus height (48.3 mm versus 53.4 mm) was negatively correlated to angle fractures. Many reports in the literature show positive relations between impacted 3rd molars and angle fractures, yet only sporadic reports describe relations between facial fractures and facial features. Our results showed the predictable positive relation between angle fractures and 3rd molar prevalence and impaction. Yet surprisingly, we found unequivocal significant positive correlation between angle fractures to gonial angle and condylar neck width and negative correlation to ramus height. Based on the correlation to the gonial angle and ramal height, it is concluded that enlarged anterior vertical growth is a predictor for angle fractures, as are condylar neck width, and wisdom teeth.
Low-Drift Visual Odometry in Structured Environments by Decoupling Rotational and Translational Motion

We present a low-drift visual odometry algorithm that separately estimates rotational and translational motion from lines, planes, and points found in RGB-D images. Previous methods estimate drift-free rotational motion from structural regularities to reduce drift in the rotation estimate, which is the primary source of positioning inaccuracy in visual odometry. However, multiple orthogonal planes are required to be visible throughout the entire motion estimation process; otherwise, these VO approaches fail. We propose a new approach to estimate drift-free rotational motion jointly from both lines and planes by exploiting environmental regularities. We track the spatial regularities with an efficient SO(3)-manifold constrained mean shift algorithm. Once the drift-free rotation is found, we recover the translational motion from all tracked points with and without depth by minimizing the de-rotated reprojection error. We compare the proposed algorithm to other state-of-the-art visual odometry methods on a variety of RGB-D datasets (including especially challenging pure rotations) and demonstrate improved accuracy and lower drift error.
TRPC6 Channels Are Required for Proliferation, Migration and Invasion of Breast Cancer Cell Lines by Modulation of Orai1 and Orai3 Surface Exposure

Transient receptor potential channels convey signaling information from a number of stimuli to a wide variety of cellular functions, mainly by inducing changes in cytosolic Ca2+ concentration. Different members of the TRPC, TRPM and TRPV subfamilies have been reported to play a role in tumorigenesis. Here we show that the estrogen receptor positive and triple negative breast cancer cell lines, MCF7 and MDA-MB-231, respectively, exhibit enhanced expression of the TRPC6 channel as compared to the non-tumoral MCF10A cell line. In vitro TRPC6 knockdown using shRNA impaired MCF7 and MDA-MB-231 cell proliferation, migration and invasion detected by BrdU incorporation, wound healing and Boyden chamber assays, respectively. Using RNAi-mediated TRPC6 silencing as well as overexpression of the pore-dead dominant-negative TRPC6 mutant we have found that TRPC6 plays a relevant role in the activation of store-operated Ca2+ entry in the breast cancer cell lines but not in non-tumoral breast cells. Finally, we have found that TRPC6 interacts with Orai1 and Orai3 in MCF7 and MDA-MB-231 cells and is required for the translocation of Orai1 and Orai3 to the plasma membrane in MDA-MB-231 and MCF7 cells, respectively, upon Ca2+ store depletion. These findings introduce a novel mechanism for the modulation of Ca2+ influx and the development of different cancer hallmarks in breast cancer cells.
Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in nonstationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estimators in terms of tracking speed and estimation accuracy.
Deeptongue: Tongue Segmentation Via Resnet

Accurate tongue image segmentation is helpful to acquire correct automatic tongue diagnosis result. However, traditional methods cannot bring satisfying results in most cases. This paper proposes an end-to-end trainable tongue image segmentation method using deep convolutional neural network based on ResNet. The proposed method, named DeepTongue, segments tongue by using a forward network without preprocessing. The proposed method has no restrictions of the illumination and size of tongue images. Experimental results show that the proposed DeepTongue improves the segmentation accuracy by a noticeable margin. In addition, DeepTongue is much faster than the existing tongue image segmentation methods.
Think and Tell: Preview Network for Image Captioning

Image captioning is a particularly challenging task, which has attracted considerable interest in the community of computer vision. Most of the existing methods follow the single-pass forward encoding-decoding process to generate image description sentence word by word. When generating a speciﬁc word, these methods are only able to utilize the previously generated words, but not the un-generated future words. However, for humans to describe a scene, it’s a common behaviour to ﬁrst preview and organize all the observed visual contents in a semantically-meaningful order, and then form a complete description sentence. In such process, humans can obtain a global information of the visual contents related to both previous words and possible future words. In this paper, we propose a preview network that incorporates such preview mechanism in the encoder-decoder framework. The proposed model consists of two visual encoders and two language decoders: one encoder is used to extract image’s high-level attributes and feed them into the ﬁrst-stage decoder to preview image’s contents and to generate a ﬁrst coarse image caption. Then, together with the convolutional features extracted by another encoder, this coarse caption is then fed into the second-stage decoder to generate a second reﬁned image caption. The experimental results on the benchmark Microsoft COCO dataset show that our method yields state-of-the-art performance on various quantitative metrics.