Miracle-dz/UniFAR-FTUs huggingface dataset:
Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks

Why does the human brain contain cortical regions specialized for the perception of some stimulus categories (e.g., faces), but not others (e.g., cars)? And why might functional specialization be a good design strategy for brains in the first place? Here, we used deep convolutional neural networks (CNNs) to test whether models optimized to recognize faces and objects require functional segregation for each task. First, we trained two separate CNNs with the same architecture to categorize either faces or objects. Unsurprisingly, the face-trained CNN performed worse on object categorization than the object-trained CNN and vice versa, demonstrating that the features optimized for each task differ from one another. Second, following the method of Kell et al (2018), we trained a family of dualtask CNNs on both tasks, asking how many layers can be shared before performance declines. Somewhat surprisingly, even the dual-task CNN that shared all layers performed nearly as well as the separate networks. This result is consistent with two hypotheses: 1) face and object recognition may be performed well by using a shared pool of common features or 2) the shared network has learned “hidden” functional specialization. In ongoing work, we are seeking to disambiguate these two hypotheses.
Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection

Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.
Advancing the Attitude Determination and Control System for the CubeSat MOVE-II

This thesis evaluates an existing state feedback controller designed for the CubeSat MOVE-II. In addition, the control system is further developed and a variety of control strategies are evaluated. The goal is to achieve a pointing accuracy of 5° and a guaranteed convergence to the desired attitude within the domain of the nonlinear system. The evaluated concepts are restricted to low earth orbits for small satellites utilizing magnetic actuation only. Furthermore, a spin stabilization around a non-principal axis of inertia is assumed. The presence of sensor noise and external disturbances, especially the residual magnetic dipole, are considered. The existing controller is analyzed for different orbits and the convergence rate for randomized initial conditions is obtained in a Monte Carlo simulation. A new gain is developed for the state feedback controller under consideration of the system requirements for MOVE-II. Furthermore, the concepts of residual magnetic dipole moment compensation, the use of an Extended Kalman Filter and a new operation point switching strategy are proposed in order to increase the pointing accuracy and convergence rate. Three nonlinear control laws based on different Lyapunov functions are evaluated. One of them is selected as an implementation candidate. A Software-in-the-Loop and a Hardware-in-the-Loop simulation successfully verifies this candidate for use on the flight hardware as part of the control firmware. It is found that the existing controller is sensitive to inclination changes of the orbit. Furthermore, with an average pointing accuracy of 5.8° and a corresponding variance of 4.3 deg^2 it does not fulfill the stated goal. In total, 69% of the evaluated cases converge to the desired attitude. The linear controller with the new gain and extensions attains a pointing accuracy of 3° with a variance of 0.38 deg^2. It converges in 80% of the cases. The nonlinear implementation candidate shows convergence in all Monte Carlo simulation runs and achieves a final pointing accuracy of 3.3° with a variance of 2.3 deg^2. Furthermore, a Hardware-in-the-Loop simulation shows that this controller is stable in the presence of large delays and with low numeric precision. It also shows to be compatible with the existing firmware of MOVE-II. Dynamically updateable control parameters render this implementation interesting to satellite operators.
A Sub-10-pJ/bit 5-Mb/s Magnetic Human Body Communication Transceiver

This article presents a transceiver designed to exploit the low path loss offered by magnetic human body communication (mHBC) toward ultra-efficient body area networking. A single-stage power oscillator is used to efficiently generate on–off-keying (OOK) modulated magnetic fields via body-worn coils that act as both resonant and magnetic-field-generating elements. To de-couple improved path loss yet reduced data-rate tradeoffs with increasing <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> of coils, a synchronous injection-locked kick-start circuit is proposed, enabling 5-Mb/s data at a carrier frequency of 40 MHz with <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> of ~50 and also providing dynamic frequency tuning to compensate normal inductor variation during motion. The receiver (RX) exploits the low path loss toward the design of a low-power sub-threshold-biased dynamic-threshold amplifier and envelope detector and successfully performs non-coherent demodulation of received OOK data. The transmitter and RX consume 18.56 and 6.3 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, which when including a 17.2-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> crystal oscillator for the reference clock, results in an efficiency of 7.15 and 4.7 pJ/bit, respectively, when closing a link across the body.
CROWD4EMS: A CROWDSOURCING PLATFORM FOR GATHERING AND
GEOLOCATING SOCIAL MEDIA CONTENT IN DISASTER RESPONSE

Abstract. Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. These initiatives face two main challenges: (1) most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders, and (2) they lack tools to manage volunteers contributions and aggregate them in order to ensure high quality and reliable results. This paper illustrates the use of a crowdsourcing platform that combines automatic methods for gathering information from social media and crowdsourcing techniques, in order to manage and aggregate volunteers contributions. High precision geolocation is achieved by combining data mining techniques for estimating the location of photos and videos from social media, and crowdsourcing for the validation and/or improvement of the estimated location. The evaluation of the proposed approach is carried out using data related to the Amatrice Earthquake in 2016, coming from Flickr, Twitter and Youtube. A common data set is analyzed and geolocated by both the volunteers using the proposed platform and a group of experts. Data quality and data reliability is assessed by comparing volunteers versus experts results. Final results are shown in a web map service providing a global view of the information social media provided about the Amatrice Earthquake event.
Abstraction as a Tool to Bridge the Reality Gap in Evolutionary Robotics

Automatically optimizing robotic behavior to solve complex tasks has been one of the main, long-standing goals of Evolutionary Robotics (ER). When successful, this approach will likely fundamentally change the rate of development and deployment of robots in everyday life. Performing this optimization on real robots can be risky and time consuming. As a result, much of the work in ER is done using simulations which can operate many times faster than realtime. The only downside of this, is that, due to the limited fidelity of the simulated environment, the optimized robotic behavior is typically different when transferred to a robot in the real world. This difference is referred to as the reality gap.
Data-Aided Channel Estimation for Multiple-Antenna Users in Massive MIMO Systems

In this paper, we study the performance of large-scale MIMO network where the base station has a large number of antennas and performs semi-blind channel estimation. We show that one can increase the number of antennas at the user side without increasing the overhead imposed by the channel estimation linearly with the number of user antennas. Specifically, we propose a simple data-aided channel estimation scheme which exploits the quasi-orthogonality of the channel vectors and takes advantage of the conventional pilot-aided channel estimation performed for the first antenna at each user. Two schemes are then analyzed using the extra antenna(s) at the users: data-multiplexing and interference alignment. We obtain achievable rates for the proposed uplink multi-cell MIMO system in both schemes. The rate bounds are tight and the system performance is also analyzed in terms of the outage probability to study the effect of the data length used for channel estimation scheme.
Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator

Pointer Generators have been the de facto standard for modern summarization systems. However, this architecture faces two major drawbacks: Firstly, the pointer is limited to copying the exact words while ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. Secondly, the copy mechanism results in a strong bias towards extractive generations, where most sentences are produced by simply copying from the source text. In this paper, we address these problems by allowing the model to “edit” pointed tokens instead of always hard copying them. The editing is performed by transforming the pointed word vector into a target space with a learned relation embedding. On three large-scale summarization dataset, we show the model is able to (1) capture more latent alignment relations than exact word matches, (2) improve word alignment accuracy, allowing for better model interpretation and controlling, (3) generate higher-quality summaries validated by both qualitative and quantitative evaluations and (4) bring more abstraction to the generated summaries.
Improving IT Decisions with Enterprise Architecture

M ATIN AN EN BERG One of the often mentioned applications of enterprise architecture (EA) is IT decision-making support. However, little is known about how EA can improve IT decisions. This research reveals the success factors of how EA can be used to improve IT decisions. The number one success factor is the enterprise architect’s ability to anticipate the decision-making context. When enterprise architects understand their decision-making context correctly, they are likely to find the right way to collaborate with decisionmakers, to present the right artifacts, and to provide the correct insights. The second success factor is to develop more actionable and diagnostic EA artifacts and to provide more strategic EA insights. In terms of IT investment, the most successful organizations use more actionable and diagnostic EA artifacts and more strategic EA insights in the preparation of IT investment decisions. Martin van den Berg (1955) was born in Boxmeer, the Netherlands. He studied at Tilburg University, where he obtained an MSc in Business Economics and an MSc in Logistics Management. In 1984 he started work at Sogeti where he was responsible for building Sogeti’s enterprise architecture (EA) practice. In 2012 he joined De Nederlandsche Bank, where he has since worked as an enterprise architect. In that same year he started his PhD research. His research interests include EA as a management instrument. He has co-authored various books on EA and is one of the founders of the Dynamic Architecture (DYA) approach. MARTIN VAN DEN BERG Fo to : S ja ak R am ak er s
Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep Regression

Appropriate robot behavior during human-robot interaction is a key part in the development of human-compliant assistive robotic systems. This study poses the question of how to continuously evaluate the quality of robotic behavior in a hybrid brain-computer interfacing (BCI) task, combining brain and non-brain signals, and how to use the collected information to adapt the robot's behavior accordingly. To this aim, we developed a rating system compatible with EEG recordings, requiring the users to execute only small movements with their thumb on a wireless controller to rate the robot's behavior on a continuous scale. The ratings were recorded together with dry EEG, respiration, ECG, and robotic joint angles in ROS. Pilot experiments were conducted with three users that had different levels of previous experience with robots. The results demonstrate the feasibility to obtain continuous rating data that give insight into the subjective user perception during direct human-robot interaction. The rating data suggests differences in subjective perception for users with no, moderate, or substantial previous robot experience. Furthermore, a variety of regression techniques, including deep CNNs, allowed us to predict the subjective ratings. Performance was better when using the position of the robotic hand than when using EEG, ECG, or respiration. A consistent advantage of features expected to be related to a motor bias could not be found. Across-user predictions showed that the models most likely learned a combination of general and individual features across-users. A transfer of pre-trained regressor to a new user was especially accurate in users with more experience. For future research, studies with more participants will be needed to evaluate the methodology for its use in practice. Data and code to reproduce this study are available at https://github.com/TNTLFreiburg/NiceBot.
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior

This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset.
Calibration tests in multi-class classification: A unifying framework

In safety-critical applications a probabilistic model is usually required to be calibrated, i.e., to capture the uncertainty of its predictions accurately. In multi-class classification, calibration of the most confident predictions only is often not sufficient. We propose and study calibration measures for multi-class classification that generalize existing measures such as the expected calibration error, the maximum calibration error, and the maximum mean calibration error. We propose and evaluate empirically different consistent and unbiased estimators for a specific class of measures based on matrix-valued kernels. Importantly, these estimators can be interpreted as test statistics associated with well-defined bounds and approximations of the p-value under the null hypothesis that the model is calibrated, significantly improving the interpretability of calibration measures, which otherwise lack any meaningful unit or scale.
Integrative Approaches for Large-scale Biomedical Data Analysis

INTEGRATIVE APPROACHES FOR LARGE-SCALE BIOMEDICAL DATA ANALYSIS Ashis Kumer Biswas, Ph.D. The University of Texas at Arlington, 2016 Supervising Professor: Jean X. Gao Advancement of the Next Generation Sequencing (NGS), also known as the High Throughput Sequencing (HTS) technologies allow researchers investigate genome, transcriptome, or epigenome of any organism from any perspective, thereby contributing to the enrichment of the biomedical data repositories for many of the lesser known phenomena. The regulatory activities inside genome by the non-coding RNAs (ncRNAs), the transcribed product of the long-neglected “junk DNA” molecules is one such phenomenon. While large-scale data about the ncRNAs are becoming publicly available, the computational challenges are being imposed to the bioinformaticians for efficient mining to get reliable answers to few subtle questions. Given the fact that a huge number of transcript sequences are retrieved every day, how can one distinguish a coding transcript from an ncRNA transcript? Can the structural patterns of the ncRNAs define their functions? Finally, from the accumulating evidences of dysregulations by ncRNAs leading to their association with a wide variety of human diseases, can one devise an inference engine to model the existing disease links as well as deduce unexplored associations? ii Most prior works on ncRNA data analysis are not applicable for addressing the challenges due to the size and scope of the available datasets. In this dissertation, we present efficient in silico integrative methods to mine biomedical data pertaining to answering aforementioned questions. We design CNCTDiscriminator method for reliably classifying the coding and non-coding RNAs coming from any part of the genome. This is achieved through an extensive feature extraction process for learning an ensemble classifier. We design algorithm, PR2S2Clust, to characterize functional ncRNAs by considering their structural features. For this, we formulate the problem as a clustering of the structures of the patched RNA-seq read segments, which is first of its kind in literature. Finally, we propose three algorithms to deal with the disease-ncRNA association inference problem. The first algorithm formulates the inference as a modified Non-negative Matrix Factorization (NMF) problem that can handle additional features of both the entities. The second algorithm formulates the problem as an Inductive Matrix Completion (IMC) problem presenting a generalized feature integration platform overcoming the cold-start issue common to most of the prior works including the NMF strategy. The final algorithm, Robust Inductive Matrix Completion (RIMC) is presented to solve two major issues with the IMC formulation pertaining to data outliers and sparsity. For all the problems, we provide rigorous theoretical foundations of the proposed algorithms and conduct extensive experiments over real-world biomedical data available in the public domains. The performance evaluation validates the utility and effectiveness of the proposed algorithms over existing state-of-the-art methods.
Inducing brain-relevant bias in natural language processing models

Progress in natural language processing (NLP) models that estimate representations of word sequences has recently been leveraged to improve the understanding of language processing in the brain. However, these models have not been specifically designed to capture the way the brain represents language meaning. We hypothesize that fine-tuning these models to predict recordings of brain activity of people reading text will lead to representations that encode more brain-activity-relevant language information. We demonstrate that a version of BERT, a recently introduced and powerful language model, can improve the prediction of brain activity after fine-tuning. We show that the relationship between language and brain activity learned by BERT during this fine-tuning transfers across multiple participants. We also show that, for some participants, the fine-tuned representations learned from both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are better for predicting fMRI than the representations learned from fMRI alone, indicating that the learned representations capture brain-activity-relevant information that is not simply an artifact of the modality. While changes to language representations help the model predict brain activity, they also do not harm the model's ability to perform downstream NLP tasks. Our findings are notable for research on language understanding in the brain.
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation

Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. UDA is of particular significance since no extra effort is devoted to annotating target domain samples. However, the different data distributions in the two domains, or \emph{domain shift/discrepancy}, inevitably compromise the UDA performance. Although there has been a progress in matching the marginal distributions between two domains, the classifier favors the source domain features and makes incorrect predictions on the target domain due to category-agnostic feature alignment. In this paper, we propose a novel category anchor-guided (CAG) UDA model for semantic segmentation, which explicitly enforces category-aware feature alignment to learn shared discriminative features and classifiers simultaneously. First, the category-wise centroids of the source domain features are used as guided anchors to identify the active features in the target domain and also assign them pseudo-labels. Then, we leverage an anchor-based pixel-level distance loss and a discriminative loss to drive the intra-category features closer and the inter-category features further apart, respectively. Finally, we devise a stagewise training mechanism to reduce the error accumulation and adapt the proposed model progressively. Experiments on both the GTA5$\rightarrow $Cityscapes and SYNTHIA$\rightarrow $Cityscapes scenarios demonstrate the superiority of our CAG-UDA model over the state-of-the-art methods. The code is available at \url{https://github.com/RogerZhangzz/CAG\_UDA}.