Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on the Impact of Multipath on Wideband Time-of-Arrival Based Localization

Localization using the time-of-arrival (ToA) of a wideband signal has the potential to achieve high accuracy, thereby making it a promising choice for a variety of applications. However, a major challenge impacting localization accuracy is multipath propagation, i.e., the existence of indirect paths from a target to an anchor (transceiver) via one or more obstacles present in the environment. In this paper, we provide an overview of the techniques proposed in the literature to analyze and address the effects of multipath on localization accuracy. For this purpose, we first cast the localization of one or more targets as a maximum a priori estimation problem, where the distribution of the amplitudes and ToAs of the indirect paths serves as a prior. Under this framework, we show that multipath can either be a blessing or a curse depending on the extent of prior knowledge available about the multipath statistics. Specifically, in the absence of any indirect path information, only the direct paths that go straight from a target to an anchor contain useful position information; in other words, multipath negatively impacts localization performance. Thus, in this case, it is important to detect the anchors having line-of-sight to the target(s), and we review the techniques reported in the literature to solve this problem. On the other hand, if complete indirect path information is available (i.e., the propagation paths of all the multipath components are known), then each indirect path is equivalent to a direct path from a virtual anchor, and hence, the spatial diversity offered by multipath can be exploited for improving localization accuracy.
A Practical Reflectance Transformation Imaging Pipeline for Surface Characterization in Cultural Heritage

We present a practical acquisition and processing pipeline to characterize the surface structure of cultural heritage objects. Using a free-form Reflectance Transformation Imaging (RTI) approach, we acquire multiple digital photographs of the studied object shot from a stationary camera. In each photograph, a light is freely positioned around the object in order to cover a wide variety of illumination directions. Multiple reflective spheres and white Lambertian surfaces are added to the scene to automatically recover light positions and to compensate for non-uniform illumination. An estimation of geometry and reflectance parameters (e.g., albedo, normals, polynomial texture maps coefficients) is then performed to locally characterize surface properties. The resulting object description is stable and representative enough of surface features to reliably provide a characterization of measured surfaces. We validate our approach by comparing RTI-acquired data with data acquired with a high-resolution microprofilometer.
An Approach to Formalization of Architectural Viewpoints Design in Real-Time and Embedded Domain

Development of Real-Time and Embedded Systems (RTES) involves a number of complex activities performed by a diversity of stakeholders. In most cases, RTES requires a number of different physical and logical components with numerous functional and non-functional constraints. Therefore, it is essential to ensure the representation, specification and analysis of quality activities of these systems since the early design activities. SPES (Software Platform Embedded Systems) methodology has been considered to describe a framework to develop RTES, but it does not specify directly how and which modeling languages should be adopted. Thus, this research adopts SPES guidelines and applies SysML and MARTE profiles to create models in the architectural viewpoints, while it highlights functional properties and different non-functional constraints of the system. A formalization of design viewpoints is presented in order to contribute to quantitative measurement to describe an analysis of the design complexity since the initial design activities. As contributions of this paper, it can be highlighted a proposal to formalize the RTES design activities (viewpoints), a measurement of the system design complexity since the initial design activities (in accordance with the design activities) and, also, the definition of a formalized manner to analyse RTES complexity without the interference of user external knowledge.
Learning Nonlinear Soft-Tissue Dynamics for Interactive Avatars

We present a novel method to enrich existing vertex-based human body models by adding soft-tissue dynamics. Our model learns to predict per-vertex 3D offsets, referred to as dynamic blendshapes, that reproduce nonlinear mesh deformation effects as a function of pose information. This enables the synthesis of realistic 3D mesh animations, including soft-tissue effects, using just skeletal motion. At the core of our method there is a neural network regressor trained on high-quality 4D scans from which we extract pose, shape and soft-tissue information. Our regressor uses a novel nonlinear subspace, which we build using an autoencoder, to efficiently compact soft-tissue dynamics information. Once trained, our method can be plugged to existing vertex-based skinning methods with little computational overhead (<10ms), enabling real-time nonlinear dynamics. We qualitatively and quantitatively evaluate our method, and show compelling animations with soft-tissue effects, created using publicly available motion capture datasets.
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception

The combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in an unsupervised fashion from the raw stimuli generated with an event-based camera. A novel adaptive neuron model and stable spike-timing-dependent plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems, namely feature extraction and local and global motion perception. Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer. The proposed solution is validated using synthetic and real event sequences. Along with this paper, we provide the cuSNN library, a framework that enables GPU-accelerated simulations of large-scale spiking neural networks. Source code and samples are available at https://github.com/tudelft/cuSNN.
Empathic Neural Responses Predict Group Allegiance

Watching another person in pain activates brain areas involved in the sensation of our own pain. Importantly, this neural mirroring is not constant; rather, it is modulated by our beliefs about their intentions, circumstances, and group allegiances. We investigated if the neural empathic response is modulated by minimally-differentiating information (e.g., a simple text label indicating another's religious belief), and if neural activity changes predict ingroups and outgroups across independent paradigms. We found that the empathic response was larger when participants viewed a painful event occurring to a hand labeled with their own religion (ingroup) than to a hand labeled with a different religion (outgroup). Counterintuitively, the magnitude of this bias correlated positively with the magnitude of participants' self-reported empathy. A multivariate classifier, using mean activity in empathy-related brain regions as features, discriminated ingroup from outgroup with 72% accuracy; the classifier's confidence correlated with belief certainty. This classifier generalized successfully to validation experiments in which the ingroup condition was based on an arbitrary group assignment. Empathy networks thus allow for the classification of long-held, newly-modified and arbitrarily-formed ingroups and outgroups. This is the first report of a single machine learning model on neural activation that generalizes to multiple representations of ingroup and outgroup. The current findings may prove useful as an objective diagnostic tool to measure the magnitude of one's group affiliations, and the effectiveness of interventions to reduce ingroup biases.
Lightweight Data Streaming from IoT Devices

The CoAP observe feature is a promising way to reduce energy consumption of IoT nodes and improve communication performance. To make the most benefit of this, the proxy which observes data from multiple nodes in a single IoT domain, must estimate the data lifetime and eliminate, or at least diminish, the impact of variability introduced by random transmission times from IoT nodes to proxy. We have investigated impact of lenient versus more conservative estimations of data freshness on traffic performance in the cluster and on energy consumption at IoT nodes. Results show that for exponentially distributed inter-observation times with mean of 60s, conservative estimations of Max\_Age run smoothly on clusters with well over 500 nodes.
Dual-threshold directed execution progress maximization for nonvolatile processors

To meet the needs of the Internet of Things (IoTs) devices, energy harvesting systems are proposed to power the systems instead of battery. Addressing the problem that harvested energy is unstable, nonvolatile processors (NVPs) have been proposed to hold intermediate data and avoid frequent program restarting from the beginning. However, NVPs often suffer a lot of waste on energy and system sources that can not be used for program execution owing to the frequent backup and recovery operations. To further improve the performance of NVPs, the paper proposes a dual-threshold method to maximize execution progress by enabling a system to hibernate to wait for power resumption instead of backing up data directly upon power interruptions. In particular, the optimal high and low thresholds, and the switches of system hibernation and backup, are discussed in details in order to achieve the goal of maximizing computation progress. The evaluation results show an average of up to 82.3% reduction on power failures and 1.5x speedup for forwarding progress by the proposed dual-threshold method compared to the conventional single threshold scheme.
Comparison of Position Control of a Gyroscopic Inverted Pendulum Using PID, Fuzzy Logic and Fuzzy PID controllers

This paper presents the modeling and control of a gyroscopic inverted pendulum. Inverted pendulum is used to realize many classical control problems. Its dynamics are similar to many real-world systems like missile launchers, human walking and many more. The control of this system is challenging as it’s highly unstable which will tend to fall on either side due to the gravitational force. In this paper, we are going to stabilize the gyroscopic inverted pendulum using PID controller, fuzzy logic controller and fuzzy PID controller. These controllers will be compared to determine the performance and to distinguish which one has the better response. Experiments and simulations are conducted to examine the different controllers and results are presented under different disturbance scenarios.
GNEG: Graph-Based Negative Sampling for word2vec

Negative sampling is an important component in word2vec for distributed word representation learning. We hypothesize that taking into account global, corpus-level information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word than the original frequency-based distribution. In this purpose we pre-compute word co-occurrence statistics from the corpus and apply to it network algorithms such as random walk. We test this hypothesis through a set of experiments whose results show that our approach boosts the word analogy task by about 5% and improves the performance on word similarity tasks by about 1% compared to the skip-gram negative sampling baseline.
Is This Seat Taken? Behavioural Analysis of the Telethrone: A Novel Situated Telepresence Display

We present research with two novel components; a system which may improve current small group telecommunication, and an experiment to test the efficacy. Telethrone projects a remote user onto a chair, bringing them into your space. The chair acts as a situated display which can support multi party head gaze, eye gaze, and body torque such that each observer knows where the projected user is looking. It is simpler to implement and cheaper than current systems. Our primary contribution is a counterbalanced repeated measures experiment to analyse gaze interactions. We analyse the multiple independent viewpoint support offered by the system to test if it demonstrates advantage over a set-up which shows a single view to both observers; in this results are inconclusive. Self-report questionnaire data suggests that the current implementation still gives the impression of being a display despite its situated nature although participants did feel the remote user was in the space with them. Results from the eye gaze analysis suggest that the remote user is not excluded from three way poker game-play.
Interaction design process for healthcare professionals: formalizing user's contexts observations

We are presenting our in-the-wild study involving designers and healthcare professionals acting as users participating in the design process of applications to support their communication flow at their workspace. Our principle, as many other possible approaches, is that interaction design is an iterative and interactive process for developing systems in which the Participatory Design (PD) and User Centered Design (UCD) is essential, providing creation of end artifacts to better satisfy user's needs, incorporating their abilities, not only for work perspective but also in other aspects of their lives. From our study we've formalized our observations into design guidelines so that the out-office user's experiences, incorporating concepts of their daily life and social activities become part of the design requirements, what is essential to the system to achieve the necessary properties for ICT adoption.
Network analysis of specific psychopathology and psychiatric symptoms in patients with anorexia nervosa

BACKGROUND Network analysis is increasingly applied to psychiatric populations to understand relationships among symptoms. METHODS Network analysis was applied on 955 patients with anorexia nervosa (AN; 631 restricting-type [ANR] and 324 binge eating-/purging-type [ANBP]), assessed with Symptom Check-List 90 (SCL-90), Eating Disorder Inventory (EDI), and Tridimensional Personality Questionnaire. RESULTS Depression, anxiety, interpersonal sensitivity (SCL-90), and ineffectiveness (EDI) had the highest centrality (strength from 1.19 to 1.35 in ANBP, and from 1.15 to 1.51 in ANR). Body mass index (BMI) had low centrality (0.14 ANBP and 0.41 ANR). Drive for thinness showed the strongest correlation with central nodes in ANBP (correlation around 0.44) and ANR (correlation range 0.38-0.47), and drive for thinness had higher centrality in ANR (1.15) than in ANBP (0.81), whereas body dissatisfaction in ANBP (0.73) than in ANR (0.61). DISCUSSION In addition to ED-core symptoms, psychiatric comorbid symptoms should be the focus of specific treatments in patients with AN, independently from BMI.
“I Shut the Door”: Interactions, tensions, and negotiations from a location-based social app

Location-based social apps leverage mobile phones to provide face-to-face (FtF) social opportunities for physically proximate individuals, such as finding nearby people to socialize, date, or hook up. Prior work on dating and hookup apps has focused mostly on profiles and user goals, but this leaves open important questions of how, after constructing a profile, people use these apps to connect and realize their goals, and what these experiences are like. We report on 22 interviews with users of Grindr, a location-based social app for men who have sex with men. We examine interaction processes from viewing profiles to meeting up. Using the perspective of relational dialectics, we explore tensions around connecting with others, sharing information, and being predictable or novel. We find that profile presentations are flexible and subject to change, disinhibition challenges interaction and revealing goals, and social consequences increase through moving from profile browsing to meeting FtF.
An Analysis and Implementation of Multigrid Poisson Solvers With Verified Linear Complexity

The Poisson equation is the most studied partial diﬀerential equation, and it allows to formulate many useful image processing methods in an elegant and eﬃcient mathematical framework. Using diﬀerent variations of data terms and boundary conditions, Poisson-like problems can be developed, e.g. for local contrast enhancement, inpainting, or image seamless cloning among many other applications. Multigrid solvers are among the most eﬃcient numerical solvers for discrete Poisson-like equations. However, their correct implementation relies on: (i) the proper deﬁnition of the discrete problem, (ii) the right choice of interpolation and restriction operators, and (iii) the adequate formulation of the problem across diﬀerent scales and layers. In the present work we address these aspects, and we provide a mathematical and practical description of multigrid methods. In addition, we present an alternative to the extended formulation of Poisson equation proposed in 2011 by Mainberger et al. The proposed formulation of the problem suits better multigrid methods, in particular, because it has important mathematical properties that can be exploited to deﬁne the problem at diﬀerent scales in a intuitive and natural way. In addition, common iterative solvers and Poisson-like problems are empirically analyzed and compared. For example, the complexity of problems is compared when the topology of Dirichlet boundary conditions changes in the interior of the regular domain of the image. The main contribution of this work is the development and detailed description of an implementation of a multigrid numerical solver which converges in linear time.
Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas

Non-invasive prediction of isocitrate dehydrogenase (IDH) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images. In this paper, we proposed a novel multimodal 3D DenseNet (M3D-DenseNet) model to predict IDH genotypes with multimodal magnetic resonance imaging (MRI) data. To evaluate its performance, we conducted experiments on the BRATS-2017 and The Cancer Genome Atlas breast invasive carcinoma (TCGA-BRCA) dataset to get image data as input and gene mutation information as the target, respectively. We achieved 84.6% accuracy (area under the curve (AUC) = 85.7%) on the validation dataset. To evaluate its generalizability, we applied transfer learning techniques to predict World Health Organization (WHO) grade status, which also achieved a high accuracy of 91.4% (AUC = 94.8%) on validation dataset. With the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multimodal radiogenomics problems and has the potential to be applied in clinical decision making.
A Central Limit Theorem and Hypotheses Testing for Risk-averse Stochastic Programs

We study statistical properties of the optimal value and optimal solutions of the sample average approximation of risk-averse stochastic problems. Central limit theorem-type results are derived for the optimal value when the stochastic program is expressed in terms of a law invariant coherent risk measure having a discrete Kusuoka representation. The obtained results are applied to hypotheses testing problems aiming at comparing the optimal values of several risk-averse convex stochastic programs on the basis of samples of the underlying random vectors. We also consider nonasymptotic tests based on confidence intervals on the optimal values of the stochastic programs obtained using the stochastic mirror descent algorithm. Numerical simulations show how to use our developments to choose among different distributions and on the considered class of risk-averse stochastic programs the asymptotic tests show better results.
Effects of Viewing Condition on User Experience of Panoramic Video

Panoramic video arises at the convergence of TV and virtual reality, and it is necessary to understand how these technologies interact to affect user experience in order to produce useful content. TV and film makers have developed a sophisticated language and set of techniques to achieve directed linear story telling on fixed screens, whereas virtual worlds more often emphasise user led exploration of possibly non-linear narrative and aspects such as presence and immersion in navigable 3D environments. This study focused on the user experience of panoramic video as viewed over two conditions, on a VR headset and using a handheld phone, and compared this to watching on a static screen thus emphasising the differences between traditional and panoramic TV. A qualitative approach to analysis was taken where users participated in semi-structured interviews. A thematic analysis was performed which produced thematic maps describing user experience for each condition. A detailed and nuanced account of emerging themes is given. Subsequently, key themes were identified and graphed to produce user response profiles to the three viewing conditions that highlight differences in user experience in terms of presence, attention, engagement, concentration on story, certainty, comfort and social ease.
PencilArt: A Chromatic Penciling Style Generation Framework

Non‐photorealistic rendering has been an active area of research for decades whereas few of them concentrate on rendering chromatic penciling style. In this paper, we present a framework named as PencilArt for the chromatic penciling style generation from wild photographs. The structural outline and textured map for composing the chromatic pencil drawing are generated, respectively. First, we take advantage of deep neural network to produce the structural outline with proper intensity variation and conciseness. Next, for the textured map, we follow the painting process of artists to adjust the tone of input images to match the luminance histogram and pencil textures of real drawings. Eventually, we evaluate PencilArt via a series of comparisons to previous work, showing that our results better capture the main features of real chromatic pencil drawings and have an improved visual appearance.
The role of the interface in self-disclosure differences in social networks online and offline

Social networks allow users to share information among themselves. In this article we investigate the influence of Facebook's interface on how its users share information. To do so, firstly we present a comparison of how users share their information online and offline. We also analyze Facebook and identify which strategies are used in the system to allow or encourage users to disclose their information. By contrasting the pieces of information that are shared with a larger audience online than offline with which pieces of information the interface gives a bigger encouragement to users to disclose we find that there is a large overlap between the two groups. This finding points to the potential impact that interface decisions have on users' self-disclosure on social networks.