Miracle-dz/UniFAR-FTUs huggingface dataset:
Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask

Depth estimation from a single image is a well-known challenge in computer vision. With the advent of deep learning, several approaches for monocular depth estimation have been proposed, all of which have inherent limitations due to the scarce depth cues that exist in a single image. Moreover, these methods are very demanding computationally, which makes them inadequate for systems with limited processing power. In this paper, a phase-coded aperture camera for depth estimation is proposed. The camera is equipped with an optical phase mask that provides unambiguous depth-related color characteristics for the captured image. These are used for estimating the scene depth map using a fully convolutional neural network. The phase-coded aperture structure is learned jointly with the network weights using backpropagation. The strong depth cues (encoded in the image by the phase mask, designed together with the network weights) allow a much simpler neural network architecture for faster and more accurate depth estimation. Performance achieved on simulated images as well as on a real optical setup is superior to the state-of-the-art monocular depth estimation methods (both with respect to the depth accuracy and required processing power), and is competitive with more complex and expensive depth estimation methods such as light-field cameras.
Lateral Stability Control of Autonomous Ground Vehicles Considering Stability Margins and State Estimation Errors

This paper introduces a new vehicle lateral stability control method, which involves both stability margin design and state estimation error analysis. To achieve the objective of always keeping vehicle states within vehicle lateral stability regions, the corresponding stability margins are designed and analytically formulated. In addition, to promote the fidelity of the stability analysis due to the limited estimation accuracy of vehicle lateral velocity and yaw rate, the estimation errors are considered in the stability analysis and their impacts on the stability control are discussed. The effectiveness of the proposed vehicle stability margin design and state estimation error analysis is validated by CarSim®/Simulink co-simulation under different conditions based on a sliding mode controller.
Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Taxi behavior is a spatial–temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent’s parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent’s parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique.
Silicon Carbide Power MOSFET Model: An Accurate Parameter Extraction Method Based on the Levenberg–Marquardt Algorithm

This letter proposes an accurate parameter extraction method based on the Levenberg–Marquardt algorithm for a silicon carbide (SiC) power mosfet model. An improved compact model uses this method to study the static behavior of SiC power mosfets according to the temperature and the input voltage. The simulation results obtained with this proposed method fit perfectly the measurements and accurately describe the static behavior of 1200 V Gen 2 SiC mosfets. The extracted model parameters (threshold voltage, saturation region transconductance, and transverse electric field parameter) are evaluated to analyze the temperature impact and understand the physical behavior of 1200 V Gen 2 SiC power mosfet.
Safe Reinforcement Learning: Learning with Supervision Using a Constraint-Admissible Set

Despite recent advances in Reinforcement Learning (RL), its applications in real-world engineering systems are still rare. The primary reason is that RL algorithms involve exploratory actions that can lead to system constraint violations. These violations can damage physical systems and even cause safety issues, e.g., battery overheat, robot breakdown, and car crashes, hindering RL deployment in many engineering applications. In this paper, we develop a novel safe RL framework that guarantees safety during learning by exploiting a constraint-admissible set for supervision. System knowledge and recursive feasibility techniques are exploited to construct a state-dependent constraint-admissible set. We develop a new learning scheme where the constraint-admissible set regulates the exploratory actions from the RL agent and simultaneously guides the agent to learn the system constraints with a penalty for control regulation. The proposed safe RL algorithm is demonstrated in an adaptive cruise control example where a nonlinear fuel economy cost function is optimized without violating system constraints. We demonstrate that the safe RL agent is able to learn the system constraints to gradually fade out the control supervisor.
Channel-Punctured Large MIMO Detection

Low-complexity data detectors targeted for large multiple-input multiple-output (MIMO) systems are considered. By systematically puncturing the channel matrix to have a specific structure, the complexity of standard non-linear detectors can be significantly reduced. The performance of these detectors is characterized and analyzed mathematically, and bounds on the achievable diversity gain and probability of bit error are derived. It is shown that puncturing does not negatively impact the receive diversity gain in hard-output detectors. Moreover, in soft-output detection, significant performance gains are attainable by ordering the layer of interest to be at the root when puncturing the channel. The proposed schemes scale up efficiently both in the number of antennas and constellation size.
Adaptive Estimation of Generalized Distance to Uniformity

In this paper, we reconsider the problem of distance to uniformity estimation of discrete distributions. As a fundamental problem in distribution property estimation, the problem with known alphabet size has been addressed in [1] [3] and is fairly well understood. In particular, let $k$ be the alphabet size and $\epsilon$ be the error tolerance parameter, people have shown that the corresponding $\epsilon$ -minimax sample complexity, i.e., the minimum sample size that is sufficient for achieving an estimation error of $\epsilon$ even in the worst case, is $\Theta(k/(\epsilon^{2}\log k))$. Surprisingly, the natural setting where the distribution is over an alphabet of unknown size has not been studied. In this work, we propose and study the well-motivated yet unexplored problem of estimating the generalized distance to uniformity, i.e., the distance of an unknown distribution to the closest uniform distribution. We provide both upper and lower bounds for its $(S,\epsilon)$ -minimax sample complexity. Specifically, let $p$ be the underlying distribution and $S(p)$ be the support size of the closest uniform distribution to $p.\mathbf{For}\ \epsilon\in(4/\sqrt{\log S(p)},\ 1]$, we present an estimator, that takes $\mathcal{O}(S(p)/(\epsilon^{3}\log\check{S}(p))$ independent samples from the underlying distribution, with probability 2/3, estimates its generalized distance to uniformity up to an additive error of $\epsilon$ without knowing the alphabet $\Omega$ or the support size $S(p)$. In addition, the estimator can be computed in nearly linear time in the sample size. In the typical high precision regime where $\epsilon\in(0,0.15)$, we show that the existence of an $\epsilon$ -adaptive estimator implies a lower bound of $\Omega_{\epsilon}(S/\log S)$ on the maximum $(S^{\prime},\epsilon)$ -minimax sample complexity over $[S/2,2S]$.
Concussion in Adolescents Impairs Heart Rate Response to Brief Handgrip Exercise.

OBJECTIVE Test the hypotheses that (1) concussion in adolescents impairs autonomic neural control of heart rate (HR), and (2) HR reactivity improves with symptom resolution. DESIGN Observational, case-control. PARTICIPANTS Nineteen concussed adolescents (8 female adolescents; age 15 ± 2 years) and 16 healthy controls (6 female adolescents, age 15 ± 2 years). INTERVENTION All participants performed an isometric handgrip (IHG) at 30% maximum voluntary contraction lasting 30 seconds. Heart rate (electrocardiogram) and hemodynamic responses (photoplethysmographic Finometer) were recorded from 30 seconds of baseline and the last 10 seconds of handgrip. MAIN OUTCOME MEASURES The HR response (ΔHR) at the onset of moderate-intensity IHG using a mixed 1-way analysis of variance. RESULTS A group × time interaction (P < 0.005) indicated that handgrip evoked a greater ΔHR among control participants (13 ± 10 beats/min) compared with concussed (6.4 ± 6.3 beats/min; group P = 0.63; time P < 0.001; d = 0.77). CONCLUSION These preliminary results suggest that a concussion impairs the ability to elevate HR at the exercise onset and, given the nature of the task, this could be interpreted to reflect reduced ability to withdraw cardiovagal control. Therefore, the data support the hypothesis of neural cardiac dysregulation in adolescents diagnosed with concussion. CLINICAL RELEVANCE The IHG test could aid concussion diagnosis and support return-to-play decisions.
Independent Effects of Eye and Hand Movements on Visual Working Memory

Both eye and hand movements have been shown to selectively interfere with visual working memory. We investigated working memory in the context of simultaneous eye-hand movements to approach the question whether the eye and the hand movement systems independently interact with visual working memory. Participants memorized several locations and performed eye, hand, or simultaneous eye-hand movements during the maintenance interval. Subsequently, we tested spatial working memory at the eye or the hand motor goal, and at action-irrelevant locations. We found that for single eye and single hand movements, memory at the eye or hand target was significantly improved compared to action-irrelevant locations. Remarkably, when an eye and a hand movement were prepared in parallel, but to distinct locations, memory at both motor targets was enhanced—with no tradeoff between the two separate action goals. This suggests that eye and hand movements independently enhance visual working memory at their goal locations, resulting in an overall working memory performance that is higher than that expected when recruiting only one effector.
A virtual bug planning technique for 2D robot path planning

We present a path planning technique inspired by the bug algorithm to quickly compute paths in an obstacle rich environment (or to report that no such path exists). In our approach, we simulate virtual bugs that upon sensing an obstacle splits into two bugs exploring the obstacle boundary in opposite directions, until the bugs find the goal in the line-of-sight. Then the bug leaves the obstacle and proceeds towards the goal. The process of splitting a bug into two continues until all the bugs reach the goal. The algorithm is simple to implement and it rapidly finds a solution if one exists. We provide worst case bounds on the path length with provable guarantees on convergence and develop heuristics to minimize the number of active bugs in the environment. We compare the performance of our algorithm with different planners from Open Motion Planning Library (OMPL) and visibility graph methods. The results show that the proposed algorithm delivers lower cost paths compared to other planners with lower computational time and rapidly indicates if a path does not exist.
Firm Responses to Targeted Consumer Incentives: Evidence from Reference Pricing for Surgical Services

This paper examines how health care providers respond to a reference pricing insurance program that increases consumer cost sharing when consumers choose high-priced surgical providers. We use geographic variation in the population covered by the program to estimate supply-side responses. We find limited evidence of market segmentation and price reductions for providers with baseline prices above the reference price. Finally, approximately 75% of the reduction in provider prices is in the form of a positive externality that benefits a population not subject to the program.
On the Maximal Code Length of Optimal Linear Locally Repairable Codes

A code symbol in an $[n,\ k,\ d]$ linear code is said to have locality $r$ if it can be repaired from at most $r$ other code symbols. An $(n,\ k,\ r)$ locally repairable code (LRC) in which every code symbol has locality $r$ is said to be optimal if its minimum distance achieves the Singleton-like bound derived by Gopalan et al. In this paper, we study the maximal code length of a q-ary optimal $(n,\ k,\ r)$ -LRC. Firstly, we give an upper bound on the code length of q-ary optimal LRCs, and then derive some structural properties and the weight hierarchy of optimal LRCs with maximal code length. Finally, we give some constructions of optimal q-ary LRCs with maximal code length.
Organic Photo‐Electrochemical Transistor‐Based Biosensor: A Proof‐of‐Concept Study toward Highly Sensitive DNA Detection

Organic bioelectronics have shown promising applications for various sensing purposes due to their significant advantages in term of high flexibility, portability, easy fabrication, and biocompatibility. Here, a new type of organic device, organic photo‐electrochemical transistor (OPECT), is reported, which is the combination of an organic electrochemical transistor and a photo‐electrochemical gate electrode modified with CdS quantum dots (QDs). Thanks to the inherent amplification function of the transistor, the OPECT‐based biosensor exhibits much higher sensitivity than that of a traditional biosensor. The sensing mechanism of the OPECT is attributed to the charge transfer between the photosensitive semiconductor CdS QDs and the gate electrode. In an OPECT‐based DNA sensor, target DNA is labeled with Au nanoparticles (NPs) and captured on the gate electrode, which can influence the charge transfer on the gate caused by the exciton–plasmon interactions between CdS QDs and Au NPs. Consequently, a highly sensitive and selective DNA sensor with a detection limit of around 1 × 10−15 m is realized. It is expected that OPECTs can be developed as a high‐performance platform for numerous biological detections in the future.
Evolution of in-vehicle display technology and Innovation of the cockpit system

Display installed in all car, there are special specification requirements for in-vehicle displays not in consumer displays. What is special about specification are three points, improvement of design by blending into car interior, ensure the visibility from the driver's eye-point, safety function which ensure driver's safety as a device. Against these requirements, we will report since we achieved to ensure specific performance by unique technology development. We will show and share our future image of in-vehicle display.
Switching-based Sinusoidal Disturbance Rejection for Uncertain Stable Linear Systems

The problem of rejection of sinusoidal disturbances with known frequencies acting on an unknown single-input single-output linear system is addressed in this note. We present a new approach that does not require knowledge of the frequency response of the transfer function over the frequency of interest. The proposed methodology reposes upon the combination of the classic feedforward control algorithm and logic-based switching. The use of three different switching logics is proposed in this paper, namely: pre-routed, dwell-time and hysteresis switching. A comparative evaluation of the three switching strategies is performed via a simulation study.
Changeability in Engineering

The digital twin is a synchronized digital representation of a production system and a major aspect of the current challenges in industrial digitalization. Once considered from beginning of engineering it can be created aside the production system and can produce benefits in engineering and operations. We present a virtual engineering method that utilizes the digital twin and allows required disciplines to work already in parallel in early phases of engineering. This reduces risks in later phases but also requires more coordination, design matching, and changes traditional processes. Therefor we present a hybrid management concept based on a combination of the V-Model and agile management. The goal of the concept is to support the implementation of virtual engineering and the required change process using virtual engineering as an example of the digital transformation.
Black Hole Entropic Fuzzy Clustering

Clustering is a common approach for finding the intrinsic pattern structure embedded in unlabeled data. In this paper, a new clustering algorithm, called black hole entropic fuzzy clustering (BHEFC), is presented to absorb the merits of three aspects: 1) black hole entropy (BHE)-based information theory; 2) fuzzy clustering; and 3) Bayesian inference model. First, through the link between clustering and the black hole phenomenon in astrophysics, the minimum BHE criterion for fuzzy clustering is proposed. Then, it is revealed that BHE-based fuzzy clustering can be realized by using a <italic>maximum-a-posteriori</italic> (MAP) framework, which in fact indicates that fuzziness and probability can co-jointly work in a collaborative rather than repulsive way. According to the proposed MAP framework for BHEFC, the fuzzifier <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>, which is limited to be less than 1 rather than bigger than 1 in fuzzy <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-means, can be explained as the partition accuracy in the form of (1−<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>) for a dataset, and the fuzzy memberships and clustering centers can be determined in a probabilistic inference way, by means of iterative sampling with the assumption of the Dirichlet distribution of the fuzzy memberships. The incremental version of the proposed fuzzy clustering model is also developed here. Experimental results on synthetic and real datasets and images for segmentation demonstrate the improved clustering results of the proposed algorithms over the comparison algorithms.
What Quality?: Performing Research on Movement and Computing

This article investigates fundamental questions and methodological issues concerning research on movement and computing. Through a process of mapping of the various approaches and phases of research in this domain, it attempts to construct a coherent picture and overview of the research field. A series of questions arise that are discussed with the intent of anchoring and directing future research across different disciplines. In order to better apprehend the complexity of movement, gesture, action, and physical performance, and their role as topic of scientific, scholarly as well artistic research practices, an extension of the disciplinary and methodological framework is proposed. The juxtaposition of the diverse approaches and goals, and the extension of the research can indicate novel axes for generating techniques, methods, and ultimately knowledge. Based on this insight, a reflection on the potential of a wider cross-mediating research practice concludes this article.
Complex Topologies from Simple Building Blocks: Uranium(IV) Thiophosphates.

Six new uranium thiophosphates, Cs5Na6[U(PS4)4](PS4) (1), Rb5Na3[U(PS4)4] (2), CsNa[U(PS4)2] (3), Cs1.67Na0. 52I0.19[U(PS4)2] (4), Cs1.033Na1. 343I0.376[U(PS4)2] (5), and Rb1.35Na0. 93I0.28[U(PS4)2] (6), were obtained from halide flux reactions and characterized using single-crystal X-ray diffraction. The structures of these compounds consist of a pseudotetrahedral uranium unit that in combination with a set of PS4 group coordination modes can result in an isolated [U(PS4)4]8- unit, a [U(PS4)2]2- chain, or a [U(PS4)2]2- slab. Optical and magnetic properties of 3 and 4 were collected. Both compounds exhibit paramagnetic behavior, and CsNa[U(PS4)2] (3) exhibits a magnetic transition at 200-225 K between two paramagnetic states that both have similar effective magnetic moments of 4.13 and 4.19 μB/formula unit, and strong antiferromagnetic interactions with θ = -183 and -172 K, for the high- and low-temperature states, respectively.
DDoS Mitigation: Decentralized CDN Using Private Blockchain

Distributed Denial of Service (DDoS) attacks are intense and are targeted to major infrastructure, governments and military organizations in each country. There are a lot of mitigations about DDoS, and the concept of Content Delivery Network (CDN) has been able to avoid attacks on websites. However, since the existing CDN system is fundamentally centralized, it may be difficult to prevent DDoS. This paper describes the distributed CDN Schema using Private Blockchain which solves the problem of participation of existing transparent and unreliable nodes. This will explain DDoS mitigation that can be used by military and government agencies.
Coming across academic social media content serendipitously

We wanted to find out whether students come across academic social media content serendipitously and, if so, to gain a detailed understanding of their experiences. To achieve this aim, we conducted semi-structured Critical Incident interviews with 15 postgraduate students from various disciplines. We found that the students did indeed come across academic social media content serendipitously - often when undertaking unfocused browsing during a break from other academic work. Time investment was identified as an important over-arching theme: investing time in crearing and sharing social media content led to the creation of opportunities for serendipity for both the person creating/sharing the content and others. The interviews also highlighted a time investment trade-off - where more time spent using social media was perceived to provide greater opportunity for serendipity but, as serendipity can never be guaranteed, it was also perceived to increase the chance that none of the information encountered would contribute to the interviewees' academic research (and therefore would be 'wasted' time). We make a number of suggestions for the design of social media tools that create opportunities for serendipity based on our findings.