Miracle-dz/UniFAR-FTUs huggingface dataset:
earPod: Efficient Hierarchical Eyes-free Menu Selection

The research in this dissertation developed and evaluated a new method for menuing interaction that is intended to be better suited than current methods with respect to mobile eyes-free scenarios. The earPod prototype was developed and then evaluated in a series of four experiments. In the first two experiments, earPod was first compared against an iPod-like (visual) interface and then against a fuller set of competitive techniques that included dual vs. single modality presentations, audio vs. visual modalities, and radial vs. linear mappings. The third experiment consisted of a longitudinal study designed to understand the learning patterns that occurred with these techniques. The fourth experiment examined performance in a conventional (single task) desktop setting and in a driving simulator (i.e., a dual task situation where participants carried out the driving task while interacting with the mobile device). The results of these experiments, comparing earPod with an iPod-like visual linear menu technique on fixed-sized static menus, indicated that earPod is comparable both in terms of speed and accuracy. Thus it seems likely that earPod should be an effective and efficient eyes-free menu selection technique. The comprehensive 3x2 study implemented in Experiment 2 showed that the benefit of earPod was largely due to the radial menu style design. While performance using it was comparable in both speed and accuracy with the visual linear menus, its performance was slower than for a visual radial style menu. In the multi-task simulated driving condition in Experiment 4, where concurrent tasks competed for visual attention, the eyes-free earPod interface was found to be beneficial in improving performance with respect to the safety related driving parameters of following distance and lateral movement in the lane. Thus auditory feedback appears to mitigate some of the risk associated with menu selection while driving. Overall, the results indicated that not only should earPod menuing be able to provide safer interaction in dual task settings, but also that, with sufficient training, audio only menu selection using innovative techniques such as those employed by earPod can be competitive with visual menuing systems even in desktop settings.
End-to-end lossless compression of high precision depth maps guided by pseudo-residual

Facing the explosion of massive amount of high precision depth maps, we pro-pose a novel end-to-end lossless compression method for high precision depth maps. The whole process is comprised of two sub-processes, named pre-processing of depth maps and deep lossless compression of processed depth maps. The deep lossless compression network consists of two sub-networks, named lossy compression net-work and lossless compression network. We leverage the concept of pseudo-residual to guide the generation of distribution for residual and avoid introducing context models. Our end-to-end lossless compression network outperforms tested learned and non-learned codecs with at least 7% reduction on real world datasets (DIODE and SementicKITTI) and has low computational cost. The visualization of the whole operations is shown as Figure 1. More details and code are available at https://git.openi. org.cn/OpenCompression/DeepLosslessCompression.
Bound states spectrum of the nonlinear Schr\"odinger equation with P\"oschl-Teller and square potential wells

We obtain the spectrum of bound states for a modified Pöschl-Teller and square potential wells in the nonlinear Schrödinger equation. For a fixed norm of bound states, the spectrum for both potentials turns out to consist of a finite number of multi-node localized states. We use modulational instability analysis to derive the relation that gives the number of possible localized states and the maximum number of nodes in terms of the width of the potential. Soliton scattering by these two potentials confirmed the existence of the localized states which form as trapped modes. Critical speed for quantum reflection was calculated using the energies of the trapped modes.
A Survey of Plagiarism Detection Systems: Case of Use with English, French and Arabic Languages

In academia, plagiarism is certainly not an emerging concern, but it became of a greater magnitude with the popularisation of the Internet and the ease of access to a worldwide source of content, rendering human-only intervention insufficient. Despite that, plagiarism is far from being an unaddressed problem, as computer-assisted plagiarism detection is currently an active area of research that falls within the field of Information Retrieval (IR) and Natural Language Processing (NLP). Many software solutions emerged to help fulfil this task, and this paper presents an overview of plagiarism detection systems for use in Arabic, French, and English academic and educational settings. The comparison was held between eight systems and was performed with respect to their features, usability, technical aspects, as well as their performance in detecting three levels of obfuscation from different sources: verbatim, paraphrase, and cross-language plagiarism. An indepth examination of technical forms of plagiarism was also performed in the context of this study. In addition, a survey of plagiarism typologies and classifications proposed by different authors is provided.
How to induce protective humoral immunity against Plasmodium falciparum circumsporozoite protein

Antibodies against Plasmodium falciparum circumsporozoite protein (PfCSP) can protect from malaria. However, the efficacy of anti-PfCSP vaccine responses is limited. This review summarizes our molecular and cellular understanding of humoral anti-PfCSP immune responses and discusses implications for vaccine design.
Comparing Cycling and Opportunity Values of Long-Duration Energy Storage in United States

The intermittency and seasonal pattern of renewable energy resources requires the ability to economically store the bulk of energy over long timescales. Long-duration energy storage is a promising solution to enable high renewable penetration in a low-emission electricity system, which can alleviate the longterm temporal mismatch of electricity demand and renewable resources abundant period. This paper analyzes how energy storage duration and location affect the cycling pattern and opportunity value of long-duration storage as a price taker in real-time market arbitrage. We use a dynamic programming approach to optimize the storage operation on an annual basis, and obtain the opportunity value of the energy stored. We perform the analysis over storage duration from one day to one month using historical data from California, New York, and Texas. Our results show significant locational differences among storage utilization that are jointly contributed by different resource mixes and congestion in the considered price zones. Long-duration energy storage is likely to be under-utilized in existing power systems, and deployment of long-duration energy storage must be carefully coordinated with renewable deployments.
A Model-Free and Finite-Population-Exact Framework for Randomized Experiments Subject to Outcome Misclassification via Integer Programming

Results from randomized experiments (trials) can be severely distorted by outcome misclassiﬁcation, such as from measurement error or reporting bias in binary outcomes. All existing approaches to outcome misclassiﬁcation rely on some data-generating (super-population) model and therefore may not be applicable to randomized experiments without additional assumptions. We propose a model-free and ﬁnite-population-exact framework for randomized experiments subject to outcome misclassiﬁcation. A central quantity in our framework is “warning accuracy," deﬁned as the threshold such that the causal conclusion drawn from the measured outcomes may diﬀer from that based on the true outcomes if the outcome measurement accuracy did not surpass that threshold. We show how learning the warning accuracy and related concepts can beneﬁt a randomized experiment subject to outcome misclassiﬁcation. We show that the warning accuracy can be computed eﬃciently (even for large datasets) by adaptively reformulating an integer program with respect to the randomization design. Our framework covers both Fisher’s sharp null and Neyman’s weak null, works for a wide range of randomization designs, and can also be applied to observational studies adopting randomization-based inference. We apply our framework to a large randomized clinical trial for the prevention of prostate cancer.
Interacting with Human Drivers: Human-like Driving and Decision Making for Autonomous Vehicles

—In this paper, a human-like driving and decision-making framework is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the transportation ecology of human driving and eliminate the misunderstanding and incompatibility of human drivers to autonomous driving. Based on the analysis of the INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. Then, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with the two-point preview model. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efﬁciency. Based on the cost function and multi-constraint, the dynamic game algorithm is applied to modelling the interaction and decision making between AV and human driver. Additionally, to guarantee the lane-change safety of AVs, an artiﬁcial potential ﬁeld model is built for collision risk assessment. Finally, based on the driving simulator, the proposed algorithm is evaluated with the human-in-the-loop experiments.
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection

Although modern automatic speech recognition (ASR) systems can achieve high performance, they may produce errors that weaken readers' experience and do harm to downstream tasks. To improve the accuracy and reliability of ASR hypotheses, we propose a cross-modal post-processing system for speech recognizers, which 1) fuses acoustic features and textual features from different modalities, 2) joints a confidence estimator and an error corrector in multi-task learning fashion and 3) unifies error correction and utterance rejection modules. Compared with single-modal or single-task models, our proposed system is proved to be more effective and efficient. Experiment result shows that our post-processing system leads to more than 10% relative reduction of character error rate (CER) for both single-speaker and multi-speaker speech on our industrial ASR system, with about 1.7ms latency for each token, which ensures that extra latency introduced by post-processing is acceptable in streaming speech recognition.
DeHIN: A Decentralized Framework for Embedding Large-Scale Heterogeneous Information Networks

Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous information networks (HINs) has been attracting immense research attention in recent times. Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs. However, in the real world, the size of HINs grow exponentially with the continuous introduction of new nodes and different types of links, making it a billion-scale network. Learning node embeddings on such HINs creates a performance bottleneck for existing HNE methods that are commonly centralized, i.e., complete data and the model are both on a single machine. To address large-scale HNE tasks with strong efficiency and effectiveness guarantee, we present Decentralized Embedding Framework for Heterogeneous Information Network (DeHIN) in this paper. In DeHIN, we generate a distributed parallel pipeline that utilizes hypergraphs in order to infuse parallelization into the HNE task. DeHIN presents a context preserving partition mechanism that innovatively formulates a large HIN as a hypergraph, whose hyperedges connect semantically similar nodes. Our framework then adopts a decentralized strategy to efficiently partition HINs by adopting a tree-like pipeline. Then, each resulting subnetwork is assigned to a distributed worker, which employs the deep information maximization theorem to locally learn node embeddings from the partition it receives. We further devise a novel embedding alignment scheme to precisely project independently learned node embeddings from all subnetworks onto a common vector space, thus allowing for downstream tasks like link prediction and node classification. As shown from our experimental results, DeHIN significantly improves the efficiency and accuracy of existing HNE models as well as outperforms the large-scale graph embedding frameworks by efficiently scaling up to large-scale HINs.
Quantum Distributed Algorithms for Detection of Cliques

The possibilities offered by quantum computing have drawn attention in the distributed computing community recently, with several breakthrough results showing quantum distributed algorithms that run faster than the fastest known classical counterparts, and even separations between the two models. A prime example is the result by Izumi, Le Gall, and Magniez [STACS 2020], who showed that triangle detection by quantum distributed algorithms is easier than triangle listing, while an analogous result is not known in the classical case. In this paper we present a framework for fast quantum distributed clique detection. This improves upon the state-of-the-art for the triangle case, and is also more general, applying to larger clique sizes. Our main technical contribution is a new approach for detecting cliques by encapsulating this as a search task for nodes that can be added to smaller cliques. To extract the best complexities out of our approach, we develop a framework for nested distributed quantum searches, which employ checking procedures that are quantum themselves. Moreover, we show a circuit-complexity barrier on proving a lower bound of the form $\Omega(n^{3/5+\epsilon})$ for $K_p$-detection for any $p \geq 4$, even in the classical (non-quantum) distributed CONGEST setting.
Exposure of Infants to Gradient Fields in a Baby MRI Scanner

In pediatric magnetic resonance imaging (MRI), infants are exposed to rapid, time‐varying gradient magnetic fields, leading to electric fields induced in the body of infants and potential safety risks (e.g. peripheral nerve stimulation). In this numerical study, the in situ electric fields in infants induced by small‐sized gradient coils for a 1.5 T MRI scanner were evaluated. The gradient coil set was specially designed for the efficient imaging of infants within a small‐bore (baby) scanner. The magnetic flux density and induced electric fields by the small x, y, z gradient coils in an infant model (8‐week‐old with a mass of 4.3 kg) were computed using the scalar potential finite differences method. The gradient coils were driven by a 1 kHz sinusoidal waveform and also a trapezoidal waveform with a 250 µs rise time. The model was placed at different scan positions, including the head area (position I), chest area (position II), and body center (position III). It was found that the induced electric fields in most tissues exceeded the basic restrictions of the ICNIRP 2010 guidelines for both waveforms. The electric fields were similar in the region of interest for all coil types and model positions but different outside the imaging region. The y‐coil induced larger electric fields compared with the x‐ and z‐ coils. Bioelectromagnetics. 43:69–80, 2022. © 2021 Bioelectromagnetics Society.
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models

Reward hacking -- where RL agents exploit gaps in misspecified reward functions -- has been widely observed, but not yet systematically studied. To understand how reward hacking arises, we construct four RL environments with misspecified rewards. We investigate reward hacking as a function of agent capabilities: model capacity, action space resolution, observation space noise, and training time. More capable agents often exploit reward misspecifications, achieving higher proxy reward and lower true reward than less capable agents. Moreover, we find instances of phase transitions: capability thresholds at which the agent's behavior qualitatively shifts, leading to a sharp decrease in the true reward. Such phase transitions pose challenges to monitoring the safety of ML systems. To address this, we propose an anomaly detection task for aberrant policies and offer several baseline detectors.
Rotation Control, Interlocking, and Self‐Positioning of Active Cogwheels

Gears and cogwheels are elemental components of machines. They restrain degrees of freedom and channel power into a specified motion. Building and powering small‐scale cogwheels are key steps toward feasible micro and nanomachinery. Assembly, energy injection, and control are, however, a challenge at the microscale. In contrast with passive gears, whose function is to transmit torques from one to another, interlocking and untethered active gears have the potential to unveil dynamics and functions untapped by externally driven mechanisms. Here, it is shown the assembly and control of a family of self‐spinning cogwheels with varying teeth numbers and study the interlocking of multiple cogwheels. The teeth are formed by colloidal microswimmers that power the structure. The cogwheels are autonomous and active, showing persistent rotation. Leveraging the angular momentum of optical vortices, we control the direction of rotation of the cogwheels. The pairs of interlocking and active cogwheels that roll over each other in a random walk and have curvature‐dependent mobility are studied. This behavior is leveraged to self‐position parts and program microbots, demonstrating the ability to pick up, direct, and release a load. The work constitutes a step toward autonomous machinery with external control as well as (re)programmable microbots and matter.
Disk Tearing Leads to Low and High Frequency Quasi Periodic Oscillations in a GRMHD Simulation of a Thin Accretion Disk

Black hole X-ray binaries (BHXRBs) display a wide range of variability phenomena, from long duration spectral state changes to short-term broadband variability and quasi-periodic oscillations (QPOs). A particularly puzzling aspect is the production of QPOs, which -- if properly understood -- could be used as a powerful diagnostic tool of black hole accretion and evolution. In this work we analyse a high resolution three-dimensional general relativistic magnetohydrodynamic simulation of a geometrically thin accretion disk which is tilted by $65^{\circ}$ with respect to the black hole spin axis. We find that the Lense-Thirring torque from the rapidly spinning 10 $M_\odot$ black hole causes several sub-disks to tear off within $\sim 10-20$ gravitational radii. Tearing occurs in cycles on timescales of seconds. During each tearing cycle the inner sub-disk precesses for 1-5 periods before it falls into the black hole. We find a precession frequency of $\sim 3\rm Hz$, consistent with observed low-frequency QPOs. In addition, we find a high frequency QPO (HFQPO) with centroid frequency of $\sim55$Hz in the power spectra of the mass-weighted radius of the inner disk. This signal is caused by radial epicyclic oscillations of a dense ring of gas at the tearing radius, which strongly suggests a corresponding modulation of the X-ray lightcurve and may thus explain some of the observed HFQPOs.
Anorectal Manometry in Children: The Update on the Indications and the Protocol of the Procedure

ABSTRACT Anorectal disorders are common in children. They are related to structural and/or functional abnormalities of the anorectum or pelvic floor with a variety of symptoms. Therefore, diagnostic tests to evaluate anorectal function can help to better understand the underlying pathophysiology and aetiology as well as facilitate patient management. During the past decades, substantial efforts have been made to improve anorectal function testing; however, more advanced investigations might lead to difficulties in interpretation. Additionally, a great diversity of equipment and protocols are used among centres, which may lead to heterogeneous interpretation of results. More studies to standardize methods of testing and validate reference values are strongly recommended in children. This review updates on the current indications and the protocol of anorectal manometry.
Application of perceptual image coding and the neural network method in predicting the optimum Asphalt binder content of open-graded friction course mixtures

Florida Department of Transportation (FDOT) designs open-graded friction course (OGFC) mixtures using a pie plate visual draindown method (FM 5-588). In this method, the optimum asphalt binder content (OBC) is determined based on visual assessment of the superficial asphalt binder draindown (SABD) distribution of three OGFC samples placed in pie plates with pre-determined asphalt binder contents (AC). In order to eliminate the human subjectivity involved in the current visual method, an automated method for quantifying the OBC of OGFC mixtures is developed using digital images of the pie plates and concepts of perceptual image coding and neural networks. Phase I involved the FM-5-588-based OBC testing of OGFC mixture designs consisting of a large set of samples prepared from a variety of granitic and oolitic limestone aggregate sources used by FDOT. Then the digital images of the pie plates containing samples of the above mixtures were acquired using an imaging setup customised by FDOT. The correlation between relevant digital imaging parameters and the corresponding AC was investigated initially using conventional regression analysis. Phase II involved the development of a perceptual image model using human perception metrics considered to be used in the OBC estimation. A General Regression Neural Network (GRNN) was used to uncover the nonlinear correlation between the selected parameters of pie plate images, the corresponding ACs and the visually estimated OBC. GRNN was found to be the most viable method to deal with the multi-dimensional nature of the input test data set originating from each individual OGFC sample that contains AC and imaging parameter information from a set of three pie plates. GRNN was trained by a major part of the database completed in Phase I. Finally, the prediction results from an independent part of the above database demonstrated that the GRNN model provides satisfactory estimations of OBC.