Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Scene Parsing via Class-Wise Adaptation

We propose the method that uses only computer graphics datasets to parse the real world 3D scenes. 3D scene parsing based on semantic segmentation is required to implement the categorical interaction in the virtual world. Convolutional Neural Networks (CNNs) have recently shown state-of-theart performance on computer vision tasks including semantic segmentation. However, collecting and annotating a huge amount of data are needed to train CNNs. Especially in the case of semantic segmentation, annotating pixel by pixel takes a significant amount of time and often makes mistakes. In contrast, computer graphics can generate a lot of accurate annotated data and easily scale up by changing camera positions, textures and lights. Despite these advantages, models trained on computer graphics datasets cannot perform well on real data, which is known as the domain shift. To address this issue, we first present that depth modal and synthetic noise are effective to reduce the domain shift. Then, we develop the class-wise adaptation which obtains domain invariant features of CNNs. To reduce the domain shift, we create computer graphics rooms with a lot of props, and provide photo-realistic rendered images.We also demonstrate the application which is combined semantic segmentation with Simultaneous Localization and Mapping (SLAM). Our application performs accurate 3D scene parsing in real-time on an actual room.
Towards Improving Speech Emotion Recognition for In-Vehicle Agents: Preliminary Results of Incorporating Sentiment Analysis by Using Early and Late Fusion Methods

The automotive industry is integrating a number of Artificial Intelligence (AI) functions, such as speech recognition and face recognition into in-vehicle agents, aiming to make them able to conduct human-like interaction with the driver [1, 5, 8, 12]. Among the many cutting-edge functions, emotion recognition has been proved significant to improve driving experience and road safety [14]. However, due to the noisy in-vehicle environment, trustworthy Speech Emotion Recognition (SER) is still hard to achieve. The voice from other passengers and the outside noise make SER unreliable. Positive emotion is sometimes recognized as negative and vice versa.
Real-Time Myocontrol of a Human–Computer Interface by Paretic Muscles After Stroke

Biosignals from skeletal muscle have been used to control human–machine interface. Signals from paretic muscles of humans with stroke are distorted and highly variable. Here, we examine the stability of surface electromyography (sEMG) features from paretic hand muscles to enable continuous, real-time multicommand control of a human–computer interface (HCI). Subjects with long standing cortical strokes (>6 months, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> = 12) and neurologically intact controls (<inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> = 12) performed two wrist rotations (wrist extend and wrist flex) and two grips (power grip and fine grip) with the nondominant (controls) or paretic (stroke patients) hand. Data reduction analyses revealed a distinct pattern of coactivation across muscles for each gesture. These synergies were similar for control and stroke groups and stable across sessions. Results of offline experiments involving wrist rotation and hand grips confirmed that gestures performed in isolation or combination were recognized at greater than chance level in both groups (<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> < 0.01). In online experiments, HCI control was evaluated with a balloon shooter game. Users in both groups were able to control the direction and speed of a simulated bullet to a balloon target with greater than chance-level accuracy (<inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> < 0.01). Taken together, these results demonstrate that sEMG synergy features from paretic hand muscles can be used to drive continuous, real-time multicommand control of an HCI.
Closed-form Inference and Prediction in Gaussian Process State-Space Models

We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling. Our approach performs variational inference over both the system states and the transition function. We exploit Markov structure in the true posterior, as well as an inducing point approximation to achieve linear time complexity in the length of the time series. Contrary to previous approaches, no Monte Carlo sampling is required: inference is cast as a deterministic optimisation problem. In a number of experiments, we demonstrate the ability to model non-linear dynamics in the presence of both process and observation noise as well as to impute missing information (e.g. velocities from raw positions through time), to de-noise, and to estimate the underlying dimensionality of the system. Finally, we also introduce a closed-form method for multi-step prediction, and a novel criterion for assessing the quality of our approximate posterior.
Hierarchical Bipartite Graph Convolution Networks

Recently, graph neural networks have been adopted in a wide variety of applications ranging from relational representations to modeling irregular data domains such as point clouds and social graphs. However, the space of graph neural network architectures remains highly fragmented impeding the development of optimized implementations similar to what is available for convolutional neural networks. In this work, we present BiGraphNet, a graph neural network architecture that generalizes many popular graph neural network models and enables new efficient operations similar to those supported by ConvNets. By explicitly separating the input and output nodes, BiGraphNet: (i) generalizes the graph convolution to support new efficient operations such as coarsened graph convolutions (similar to strided convolution in convnets), multiple input graphs convolution and graph expansions (unpooling) which can be used to implement various graph architectures such as graph autoencoders, and graph residual nets; and (ii) accelerates and scales the computations and memory requirements in hierarchical networks by performing computations only at specified output nodes.
Identifying Sustainable Population Segments Using a Multi-Domain Questionnaire

This study presents the creation of a measurement device to determine and define sustainability attitudes into identifiable sustainability segments. These segments were profiled with behavioral and sociodemographic data. Based on previous literature, key sustainability topics were identified from which a 31-item questionnaire was developed, the Five Factor Sustainability Scale (FFSS). With the FFSS, multiple domains of environmental sustainability can be assessed. We present results validating this measure using a factor–cluster segmentation approach in a nationally representative sample (N = 508). Five sustainability factors emerged: (1) sustainable spending, (2) sustainable skepticism, (3) sustainable responsibility, (4) sustainable support, and (5) sustainable mobility. A cluster analysis on this sample yielded four segments in which people were grouped according to their sustainable attitudes: (1) Convinced Sustainers, (2) Sustainable Wannabes, (3) Sustainable Non-Believers, and (4) Non-Sustainers. Results linking these segments to behavioral and demographic data show discernable differences between the segments, making the FFSS a valuable tool for future intervention studies aiming at sustainable behavior change.
Achieving unity power factor at floating grid conditions in a cement plant in India - Case Study

Most cement plants in India and around the world face a unique problem in regards with power factor. In industries where their local generation with their Captive Power Plant (CPP) and Waste Heat Recovery System (WHRS) is equal to or greater than the plant's total utilization, the load import from the Grid is brought down close to zero. This is done to reduce the power cost because the Grid power is comparatively expensive. Though the Grid power is not required, the connection to the Grid is mandatory for the system in floating mode to ensure the stability of the system, to avoid black-outs in the event of CPP failures/trips and to tackle unplanned/sudden variations in the load. Such a setup makes it extremely complex to maintain power factor at the Grid incomer. For more than a decade, it was assumed that no solution exists to this problem where CPPs are connected and power drawn from the Grid is close zero. In India, a state like Chhattisgarh, where Grid billing is in kVAh and on TOD11TOD- Time of Day billing system. It refers to the practice of setting different price for a of unit energy at different times of the day. The general classification is On-Peak, Off-Peak and Normal hours. basis, power factor plays a major role in Grid cost optimization. This paper contains a discussion on the problem, the solution and a case study of achieving unity power factor at floating grid conditions in a cement plant in India.
Chimeric Antigen Receptor Signaling Domains Differentially Regulate Proliferation and Native T Cell Receptor Function in Virus-Specific T Cells

The efficacy of T cells expressing chimeric antigen receptors (CARs) for solid tumors has been limited by insufficient CAR T cell expansion and persistence. The use of virus-specific T cells (VSTs) as carriers for CARs may overcome this limitation since CAR-VSTs can be boosted by viral vaccines or oncolytic viruses. However, there is limited understanding of the optimal combination of endodomains and their influence on the native T cell receptor (TCR) in VSTs. We therefore compared the function of GD2.CARs expressing the TCR zeta chain (ζ) alone or combined with endodomains from CD28 and 4-1BB in varicella zoster virus-specific (VZV) T cells. VZVSTs expressing GD2-CARs recognized VZV-derived peptides and killed GD2-expressing tumor cells. However, after repeated stimulation through their native TCR, the expansion of GD2-CAR. CD28ζ-VZVSTs was 3.3-fold greater (p < 0.001) than non-transduced VZVSTs, whereas GD2-CARζ- and GD2-CAR. 41BBζ inhibited VZVST expansion (p < 0.01). Compared to control VZVSTs, GD2-CAR. ζ VZVSTs showed a greater frequency of apoptotic (p < 0.01) T cells, whereas prolonged downregulation of the native αβ TCR was observed in GD2-CAR. 41BBζ VZVSTs (p < 0.001). We confirmed that CD28ζ can best maintain TCR function by expressing GD2.CARs in Epstein-Barr virus-specific T cells and CD19-CARs in VZVSTs. In response to CAR stimulation VSTs with CD28ζ endodomains also showed the greatest expansion (6 fold > GD2-CAR.41BBζ VZVSTs (p < 0.001), however anti-tumor efficacy was superior in GD2-CAR. 41BBζ-VZVSTs. These findings demonstrate that CAR signaling domains can enhance or diminish the function of the native TCR and indicate that only CD28ζ may preserve the function of the native TCR in tonically signaling CAR-VSTs.
Effects of Pre-Processing on the ECG Signal Sparsity and Compression Quality

Pre-processing is necessary for many applications before data transmission. In this paper, signal sparsity variations due to some pre-processing steps such as filtering and compression are considered; and after complete and educational reviewing preliminaries, it is shown that (i) Adding noise to a signal decreases the signal sparsity and increases the diversity index named Gini-Sympson as a special case of Tsallis entropy; (ii) the sparsity of filtered signal is increased; (iii) the compression metrics such as PRD and CR are improved if the compressed sensing method is performed on the filtered signal; and finally (iv) it is tried that the theoretical explanations are validated numerically.
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud

We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data. Instead of treating object proposal as a direct bounding box regression problem, we take an analysis-by-synthesis strategy and generate proposals by reconstructing shapes from noisy observations in a scene. We incorporate GSPN into a novel 3D instance segmentation framework named Region-based PointNet (R-PointNet) which allows flexible proposal refinement and instance segmentation generation. We achieve state-of-the-art performance on several 3D instance segmentation tasks. The success of GSPN largely comes from its emphasis on geometric understandings during object proposal, greatly reducing proposals with low objectness.
A Miniaturized and High Gain Double-Slot Vivaldi Antenna Using Wideband Index-Near-Zero Metasurface

In this paper, a wideband, miniaturized, and high gain double-slot Vivaldi antenna is proposed. First, a corner cutting technique is adopted to broaden the frequency band. Second, a pair of defected ground slots is added to the edges of the antenna to extend the low-end frequency limitation from 2.27 to 1.15 GHz, which provides a great design freedom. Third, a novel unit cell of an index-near-zero metasurface is proposed in our design. The unit cell is a two-stub-loaded split-ring resonator, which has a compact size, a wide frequency band, and a good reflection coefficient. The gain of the double-slot Vivaldi antenna is improved over a wide frequency band by the inclusion of the index-near-zero metasurface in the aperture, without changing the overall dimensions or compromising the whole frequency band performance of the antenna. Finally, to verify the design method, an ultra-wideband double-slot Vivaldi antenna with defected ground slots and metasurface was fabricated and measured. The circuit has an overall size of $0.76\,\,\lambda \text{g}\,\,\times0.41\,\,\lambda \text{g}\,\,\times 0.005\,\,\lambda \text{g}$ . The modified antenna exhibits a measured gain of 0.7–14 dBi over the frequency band of 1.13–12 GHz.
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process

Ensemble learning is a mainstay in modern data science practice. Conventional ensemble algorithms assigns to base models a set of deterministic, constant model weights that (1) do not fully account for variations in base model accuracy across subgroups, nor (2) provide uncertainty estimates for the ensemble prediction, which could result in mis-calibrated (i.e. precise but biased) predictions that could in turn negatively impact the algorithm performance in real-word applications. In this work, we present an adaptive, probabilistic approach to ensemble learning using dependent tail-free process as ensemble weight prior. Given input feature $\mathbf{x}$, our method optimally combines base models based on their predictive accuracy in the feature space $\mathbf{x} \in \mathcal{X}$, and provides interpretable uncertainty estimates both in model selection and in ensemble prediction. To encourage scalable and calibrated inference, we derive a structured variational inference algorithm that jointly minimize KL objective and the model's calibration score (i.e. Continuous Ranked Probability Score (CRPS)). We illustrate the utility of our method on both a synthetic nonlinear function regression task, and on the real-world application of spatio-temporal integration of particle pollution prediction models in New England.
Analyzing Glide-Symmetric Holey Metasurfaces Using a Generalized Floquet Theorem

In this paper, we present a general mode matching formulation to analyze the wave propagation in glide-symmetric metallic surfaces drilled with periodic holes with an arbitrary cross section. A generalized Floquet theorem is applied to reduce the computational cost by imposing boundary conditions only on one of the two surfaces. With a small modification, the formulation can be used also for dispersion analysis of a holey metallic surface with or without a metal plane above it. The method is fast and efficient, and it provides physical insight on the specific symmetry properties of Floquet harmonics in glide-symmetric structures. The formulation is applied to obtain dispersion diagrams of glide-symmetric structures with circular holes as this kind of hole is usually used in practical applications to realize gap waveguides or wideband planar lenses. The results agree well with the reference results from commercial software CST Microwave Studio.
Structural Design for Dimensional Stability of Thermocouples in Thermoelectric Energy Harvester

The design of thermoelectric energy harvester in hybrid configuration by CMOS process has been known desirable. Thermal isolation cavity on the top and/or bottom of a thermocouple to increase its thermal resistance is necessary to achieve better harvester performance. However, the thermocouple becomes suspended and may suffer from poor dimension stability on wearable device applications. This paper aims at improving the dimension stability of thermoelectric energy harvester by a structural support design. A harvester with a thermal isolation cavity of 10 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> in depth and a structural support of 4 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> in length is developed to minimize heat leakage and to provide the dimension stability of thermocouples. Compared with many previous works, the harvester design implemented by BiCMOS standard process (TSMC 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SiGe 3P6M) is shown to achieve a superior power factor of 0.125 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>/cm<sup>2</sup>K<sup>2</sup> and a voltage factor of 23.53 V/cm<sup>2</sup>K.
Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother

We consider the problem of robust estimation involving filtering and smoothing for nonlinear state space models which are disturbed by heavy-tailed impulsive noises. To deal with heavy-tailed noises and improve the robustness of the traditional nonlinear Gaussian Kalman filter and smoother, we propose in this work a general framework of robust filtering and smoothing, which adopts a new maximum correntropy criterion to replace the minimum mean square error for state estimation. To facilitate understanding, we present our robust framework in conjunction with the cubature Kalman filter and smoother. A half-quadratic optimization method is utilized to solve the formulated robust estimation problems, which leads to a new maximum correntropy derivative-free robust Kalman filter and smoother. Simulation results show that the proposed methods achieve a substantial performance improvement over the conventional and existing robust ones with slight computational time increase.
Hardware Trojan in FPGA CNN Accelerator

Maliciously manipulating prediction results of Convolutional Neural Network (CNN) is a severe security threat. Previous works studied this threat from the aspects of dataset and model. However, with the increasing developments of CNN accelerators nowadays, the role of hardware in this threat lacks attentions. This paper inserts a hardware Trojan into the convolutional operations of a FPGA CNN accelerator. The experiments on ImageNet show that, with only 0.0051% hardware overhead to the accelerator and 0.000356% modification to an image, the hardware Trojan can be triggered to 100% precisely control the CNN classification result of the image.
Frequent Itemsets as Meaningful Events in Graphs for Summarizing Biomedical Texts

In this paper, we introduce a method using graph modeling for summarizing biomedical texts. We address the challenges of identifying meaningful topics of the input document, modeling the relations between the sentences, and selecting the most relevant sentences. Our summarizer utilizes an itemset mining method to discover the topics from the concepts extracted from the input document. It uses a meaningfulness measure to identify the meaningful events, i.e. the essential topics of the input document. Theses meaningful topics are used to construct a small-world network that models the relations between the sentences within the text. Those sentences identified as highly-informative and relevant are selected to generate the final summary. Conducting a set of evaluations, we assess the efficiency of the graph-based approach for summarization of biomedical documents. The evaluations demonstrate that our method can achieve a better performance in comparison with a number of available methods with respect to some widely-used metrics. The summaries produced by the graph-based summarizer contain more informative content compared to the summaries generated by the other methods. This shows that the combination of itemset mining on the concepts, the meaningfulness measure, and modeling the text as a small-world network can perform well in biomedical text summarization.
A User-Centered Storytelling Approach to Design a Language Companion Robotic Agent

Through a user-centered design approach, we explored how a robotic agent can be designed to support students practicing Swedish at home and in class. The process started with design workshops and user-feedback that helped to create a cardboard prototype of a language companion robotic agent. The prototype was used in semi-structured interviews where eight participants created scenarios on how to use the agent, both at home and in a classroom. Our result presents design impressions for a language companion robotic agent categorized into embodiment, emotions, dialogue, and personality. The findings also suggest tasks for the agent at home and in a classroom.
Acute Effects of Instructed and Self-Created Variable Rope Skipping on EEG Brain Activity and Heart Rate Variability

The influence of physical activity on brain and heart activity dependent on type and intensity of exercise is meanwhile widely accepted. Mainly cyclic exercises with longer duration formed the basis for showing the influence on either central nervous system or on heart metabolism. Effects of the variability of movement sequences on brain and heart have been studied only sparsely so far. This study investigated effects of three different motor learning approaches combined with a single bout of rope skipping exercises on the spontaneous electroencephalographic (EEG) brain activity, heart rate variability (HRV) and the rate of perceived exertion (RPE). Participants performed repetitive learning (RL) and two extremely variable rope skipping schedules according to the differential learning approach. Thereby one bout was characterized by instructed variable learning (DLi) and the other by self-created variable learning (DLc) in randomized order each on three consecutive days. The results show higher RPE after DLi and DLc than after RL. HRV analysis demonstrates significant changes in pre–post exercise comparison in all training approaches. No statistically significant differences between training schedules were identified. Slightly greater changes in HRV parameters were observed in both DL approaches indicating a higher activation of the sympathetic nervous system. EEG data reveals higher parietal alpha1 and temporal alpha2 power in RL compared to both DL schedules immediately post exercise. During the recovery of up to 30 min, RL shows higher temporal and occipital theta, temporal, parietal and occipital alpha, temporal and occipital beta and frontal beta3 power. In conclusion, already a single bout of 3 min of rope skipping can lead to brain states that are associated with being advantageous for cognitive learning. Combined with additional, cognitively demanding tasks in form of the DL approach, it seems to lead to an overload of the mental capacity, at least on the short term. Further research should fathom the reciprocal influence of cardiac and central-nervous strain in greater detail.