Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedestrian-to-Vehicle Communications in an Urban Environment: Channel Measurements and Modeling

As wireless connectivity becomes increasingly ubiquitous, a greater emphasis will be placed upon the seamless integration of dissimilar networking technologies. One such example of this will occur in urban environments, where wearable devices and vehicular networks will operate in close proximity to one another. Clearly, a natural extension to both types of network is their interconnectivity through vehicle-to-pedestrian (V2P) or equivalently pedestrian-to-vehicle (P2V) communications as part of a much greater vehicle-to-X (V2X)-based intelligent transportation system. To this end, we empirically investigate the P2V communications channel at 5.8 GHz for the case of a moving vehicle when a person positioned by the edge of a road was either stationary or walking parallel to the side of the highway. The measurements considered a chest-mounted transmitter and four receiver locations on the vehicle covering the front wing mirrors and two positions on the roof, which simultaneously recorded the received signal power. To characterize the propagation mechanisms which are responsible for shaping the received signal in the P2V channel, we decomposed it into its path loss (PL), large-scale, and small-scale fading components. We first show that although there was evidence of interference caused by multiple rays interacting with one another, the popular two-ray ground-reflection PL model was unable to adequately describe the compounded effects of the vehicle and pedestrian’s body on the signal attenuation in the majority of the considered scenarios. Instead, we found that the overall PL was well characterized using a dual-slope log-distance model, with lognormal large-scale fading. Due to the often severe small-scale fading that was observed in the P2V channel, we have been able to utilize the $\kappa $ - $\mu $ extreme distribution with considerable success to characterize the worse than Rayleigh fading conditions which were encountered.
8.7 A 2MHz 4-to-60VIN Buck-Boost Converter for Automotive Use Achieving 95% Efficiency and CISPR 25 Class 5 Standard

High-voltage highly efficient switching power converters have gained high popularity in automotive applications. Powered by the car battery $(V_{IN})$, the converters need to experience a wide $V_{IN}$ range from 4V to 60V under harsh cold crank and load dump environments. This requires the converter to accomplish a fine output voltage $( V_{OUT})$ regulation, seamless mode transition for $V_{IN}$ above, below or equal to $V_{OUT}$. Conventional buck or boost converters can only achieve monotonic step-down or step-up conversion. Other solutions such as standalone boost and buck, or SEPIC converters [1, 2] either require multiple cascaded power stages or suffer from lower system efficiency, larger footprint and higher bill-of material cost. The buck-boost converter thusly becomes a promising topology by its rather simple power-stage structure and wide conversion ratio [3–5]. However, the electromagnetic interference (EMI), power loss, and complicated control scheme are challenging with its inherent four-switch operation.
Toward a Multi-Representation Persian Treebank

In this paper, we describe our project of building a phrase structure treebank for Persian. The treebank consists of approximately 30000 sentences. With the help of this treebank, the researcher can investigate syntactic phenomena, extract grammars, train and test parsers, etc. In addition to these motivations, as another advantage of it we can refer to the fact that the sentences of this treebank are selected from an available dependency treebank. So the final treebank has two syntactic representations: phrase structure and dependency structure. The treebank is built using a bootstrapping approach, which converts a dependency structure tree to a phrase structure tree and the annotations are corrected manually. Using the new phrase structure treebank, we train models for constituency parsers. The treebank is freely available for educational purposes1.
Filling the Gap: Silicon Terahertz Integrated Circuits Offer Our Best Bet

The definition for the frequency range of a terahertz wave varies in the literature. Some refer to terahertz as the spectrum between 300 GHz (&lambda; = 1 mm) and 3 THz (&lambda; = 0.1 mm) to distinguish it from its millimeter-wave counterpart (30-300 GHz). Others adopt a decimal pattern and a loosely defined frequency range from 100 GHz to 10 THz. Based on the latter definition, probably the earliest documented endeavor to reach terahertz frequency was published in a paper in Physical Review in 1923 [1]. In this nearly century-old work, authors Nichols and Tear generated a 0.17-THz signal using a spark-gap oscillator, detected it with a radiometer vane, and analyzed the spectrum through a Boltzmann interferometer.
The C-terminal region of A-kinase anchor protein 350 (AKAP350A) enables formation of microtubule-nucleation centers and interacts with pericentriolar proteins

Microtubules in animal cells assemble (nucleate) from both the centrosome and the cis-Golgi cisternae. A-kinase anchor protein 350 kDa (AKAP350A, also called AKAP450/CG-NAP/AKAP9) is a large scaffolding protein located at both the centrosome and Golgi apparatus. Previous findings have suggested that AKAP350 is important for microtubule dynamics at both locations, but how this scaffolding protein assembles microtubule nucleation machinery is unclear. Here, we found that overexpression of the C-terminal third of AKAP350A, enhanced GFP-AKAP350A(2691–3907), induces the formation of multiple microtubule-nucleation centers (MTNCs). Nevertheless, these induced MTNCs lacked “true” centriole proteins, such as Cep135. Mapping analysis with AKAP350A truncations demonstrated that AKAP350A contains discrete regions responsible for promoting or inhibiting the formation of multiple MTNCs. Moreover, GFP-AKAP350A(2691–3907) recruited several pericentriolar proteins to MTNCs, including γ-tubulin, pericentrin, Cep68, Cep170, and Cdk5RAP2. Proteomic analysis indicated that Cdk5RAP2 and Cep170 both interact with the microtubule nucleation-promoting region of AKAP350A, whereas Cep68 interacts with the distal C-terminal AKAP350A region. Yeast two-hybrid assays established a direct interaction of Cep170 with AKAP350A. Super-resolution and deconvolution microscopy analyses were performed to define the association of AKAP350A with centrosomes, and these studies disclosed that AKAP350A spans the bridge between centrioles, co-localizing with rootletin and Cep68 in the linker region. siRNA-mediated depletion of AKAP350A caused displacement of both Cep68 and Cep170 from the centrosome. These results suggest that AKAP350A acts as a scaffold for factors involved in microtubule nucleation at the centrosome and coordinates the assembly of protein complexes associating with the intercentriolar bridge.
Voltage Droop Controls in Power Flow Simulation

Steady state power flow simulation software is widely used across industry and academia for reliable system operation. Voltage regulation implementation in software typically makes use of PQ and PV buses separately resulting in an infinite slope QV curve, while units in industry typically have dead-bands and real sloped responses. In this work, a new technique for modeling power flow generator voltage control is implemented to more accurately model the real-world generator responses. The new technique has been compared with typical power flow control methods using a three-bus case study that portrays the differences in the two response types. The results obtained show that the new technique is more effective at modeling dead-bands and real sloped responses that exist in industry.
A Distributed Model-Free Controller for Enhancing Power System Transient Frequency Stability

The transient stability control of power systems with growing penetration of renewable energy resources is challenging due to inherent small damping of generators and complicated operating conditions. To address the drawbacks of existing control approaches which need accurate systemwide network parameters, a model-free fuzzy controller is proposed to enhance the transient and frequency stability of power systems. Also, an adaptive parameter estimation scheme is developed to eliminate the fuzzy approximation errors and compensate the external disturbances. The proposed strategy is implemented based on the multiagent framework, which enables the sharing of communication and computation burdens among local controllers for fast and coordinated response. The convergence of the proposed distributed control approach is rigorously proved using the Graph theory and Lyapunov stability theory. Simulation studies validate the effectiveness of the proposed distributed control approach.
Electric Powered Wheelchair Command by Information Fusion from Eye Tracking and BCI

Since the first electric version, a lot of arrangements have been done in Electric Powered Wheelchair to attend a wide and increasing types of user’s needs. Many applications have evolved from a single into multimodal input methods that allow one wheelchair to support various types of disability. In cases of severe impairments, Brain-Computer Interface and Eye Tracking are commonly applied. Both input interfaces have weaknesses overcame by combined operation instead of each one independently. This proposal allows the user to command an Electric Power Wheelchair by information fusion from commercial Brain-Computer Interface and Eye Tracker devices, surpassing challenges of non-intentional commands and high workload of the user. Thereby, it is expected this user-wheelchair interface to be simple, safe and afford a better user experience.
Torque-Based Balancing for a Humanoid Robot Performing High-Force Interaction Tasks

Balancing is a critical feature for a robot interacting with an unstructured environment. The balancing control should account for unknown perturbation forces that might destabilize the robot when performing the intended tasks. In the case of humanoid robots this challenge is higher due to the inherent difficulties of balancing a robot on two legs, resulting in a rather small footprint. Approaches for enabling a good balancing behavior on humanoid robots traditionally rely on whole-body balancing approaches. This letter extends a passivity-based whole-body balancing framework to guarantee the equilibrium of a humanoid robot while performing different interaction tasks where the (high) task forces acting on the robot are difficult to foresee. Instead of controlling the center of mass, the proposed controller directly uses information from the gravito-inertial wrench cone to guarantee the feasibility of the balancing forces. The performance of the approach is validated in a number of successful experimental tests.
Sea Ice Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and Interscan Banding

Effective methods of sea ice classification are crucial to regional ice type mapping using spaceborne synthetic aperture radar (SAR), especially in ScanSAR mode. However, in TerraSAR-X ScanSAR images of ocean scenes, the scalloping and interscan banding (ISB) artifacts are usually visible. Though these two artifacts could be reduced to a certain degree where residual artifacts do not impede the visual interpretation of SAR images, this is not the case when it comes to sea ice classification. The difficulty mainly comes from the involvement of texture features in sea ice classification. Texture features, especially gray level co-occurrence matrix (GLCM) are useful bases for sea ice classification using SAR data in single or dual polarization. When GLCM is applied to TerraSAR-X ScanSAR data, however, scalloping and ISB artifacts are found to be intensified, further affecting the classification result. How to largely eliminate the effects of scalloping and ISB on sea ice classification has not been studied thoroughly yet. In this paper, an approach combining Kalman filter, GLCM, and support vector machine is proposed. An independent testing shows that this approach is effective at the removal of scalloping and ISB's effects on sea ice classification using TerraSAR-X ScanSAR data, with the overall accuracy of 88.26%. Besides, convolutional neural network is implemented to compare with the proposed approach.
The molecular basis for peptide repertoire selection in the human leukocyte antigen (HLA) C*06:02 molecule

Human leukocyte antigen (HLA)-C*06:02 is identified as the allele associated with the highest risk for the development of the autoimmune skin disease psoriasis. However, the diversity and mode of peptide presentation by the HLA-C*06:02 molecule remains unclear. Here, we describe the endogenous peptide repertoire of ∼3,000 sequences for HLA-C*06:02 that defines the peptide-binding motif for this HLA allomorph. We found that HLA-C*06:02 predominantly presents nonamer peptides with dominant arginine anchors at the P2 and P7 positions and a preference for small hydrophobic residues at the C terminus (PΩ). To determine the structural basis of this selectivity, we determined crystal structures of HLA-C*06:02 in complex with two self-peptides (ARTELYRSL and ARFNDLRFV) and an analogue of a melanocyte autoantigen (ADAMTSL5, VRSRR-abu-LRL) implicated in psoriasis. These structures revealed that HLA-C*06:02 possesses a deep peptide-binding groove comprising two electronegative B- and E-pockets that coincide with the preference for P2 and P7 arginine anchors. The ADAMTSL5 autoantigen possessed a P7-Leu instead of the P7-Arg residue, but nevertheless was accommodated within the HLA-C*06:02 antigen-binding cleft. Collectively, our results provide the structural basis for understanding peptide repertoire selection in HLA-C*06:02.
Dual-Layer SIW Multibeam Pillbox Antenna With Reduced Sidelobe Level

In this letter, a dual-layer substrate integrated waveguide (SIW) multibeam pillbox antenna with reduced sidelobe level (SLL) is presented. To avoid the problem of aperture blocking, the feeding network and an antenna array are placed in different layers connected through an SIW parabolic quasi-optical system. Aiming to reduce the SLLs, two extra parabolic reflectors are introduced into the parabolic quasi-optical system placed on both sides of the central parabolic reflector. Moreover, by adding some metal posts in the antenna array section, a further reduction in SLLs can be achieved. The proposed antenna, operating at 28 GHz, has been verified by measurements, showing good performance over a coverage angle of ±45°, and the SLLs of all the beams are lower than −12 dB.
Dual-Linear Polarized Phased Array With 9:1 Bandwidth and 60° Scanning Off Broadside

We present, for the first time, a novel dual-linear polarized tightly coupled dipole array with integrated balun operating across a 9:1 bandwidth, from 2 to 18 GHz. This dual-linear polarized array employs a tightly coupled dipole topology and scans down to 60° from boresight. The overall geometry consists of cross-located tightly coupled dipoles integrated with a folded Marchand balun that serves as an impedance transformer to achieve a wideband feeding network. Notably, the traditional dielectric superstrate is replaced with double layers of frequency selective surfaces to enable scanning down to 60° in both E-, H-, and diagonal (D)-planes across the entire band. The design achieves VSWR < 3 across 2–18 GHz at broadside. This dual-polarized array is also demonstrated to have a good polarization isolation of −20 dB for most of the band. The array’s total height is less than 12 mm (<inline-formula> <tex-math notation="LaTeX">$\lambda _{low}$ </tex-math></inline-formula>/10). The design is validated through fabrication and testing of an <inline-formula> <tex-math notation="LaTeX">$11 \times 11$ </tex-math></inline-formula> prototype. The measurements of this prototype are presented and are in good agreement with simulations.
1-Day Learning, 1-Year Localization: Long-Term LiDAR Localization Using Scan Context Image

In this letter, we present a long-term localization method that effectively exploits the structural information of an environment via an image format. The proposed method presents a robust year-round localization performance even when learned in just a single day. The proposed localizer learns a point cloud descriptor, named Scan Context Image (SCI), and performs robot localization on a grid map by formulating the place recognition problem as place classification using a convolutional neural network. Our method is faster than existing methods proposed for place recognition because it avoids a pairwise comparison between a query and scans in a database. In addition, we provide thorough validations using publicly available long-term datasets, the NCLT dataset and the Oxford RobotCar dataset, and show that the Scan Context Image (SCI) localization attains consistent performance over a year and outperforms existing methods.
Video Completion and Simultaneous Moving Object Detection for Extreme Surveillance Environments

Since automated cleaning systems are less common in extreme surveillance environments, the accumulations of combustion fuels, dust, dirt, spider webs, etc., affect the visibility and clarity of the captured video data to different degrees resulting in incomplete (missing) video sequences. Grounded on this significant practical scenario, this letter proposes a scheme to concurrently complete the missing entries and detect moving objects with efficient background separation by formulating a single convex optimization problem developed and implemented in the tensor framework. The work is implemented as a unified scheme for concurrent video completion and moving object detection using twist spatio-temporal total variation to enhance the detection performance of the foreground while fitting the tensor nuclear norm minimization for efficient background separation with half thresholding applied on the tensor singular values. Moreover, the sparse variations that are part of the dynamic background are addressed using $l_{1/2}$ regularization. The formulated minimization problem is solved using the augmented Lagrangian method with an alternating direction strategy. The work also has computational benefits and the excellence of this method is revealed in the quantified performance evaluation against the compared approaches.
Analysis of the Joint Viterbi Detector/Decoder (JVDD) Over a Coded AWGN/ISI System ss an LDPC Alternative

The joint Viterbi detector/decoder (JVDD) has been proposed as an alternative to the powerful and ubiquitous low-density parity check (LDPC) coding schemes, that are used in the majority of channel coding applications today. At shorter block lengths, the JVDD is able to outperform the state-of-the-art LDPC decoder that can be found in many of today’s systems, from deep-space communications to the digital video broadcasting standard, to the advanced television standard committee 3.0. Researchers have developed the tools to analyze the performance of the LDPC decoder, which is known as the sum-product algorithm or message passing algorithm. These tools include density evolution (DE) and extrinsic information transfer (EXIT) charts. To date, no similar such analysis exists for the JVDD. In the current paper we perform a similar preliminary analysis on the JVDD algorithm akin to what DE and EXIT charts are for the LDPC decoder. The current new analysis of the JVDD allows prediction of its probability of error based on the probability distribution functions of the metrics used within the algorithm. Our analysis plays a similar role to the abovementioned tools developed for the LDPC decoder: it allows us to predict the performance of the JVDD under various conditions (such as varying SNR’s, thresholds, and block lengths), it improves our understanding of the JVDD algorithm and the match against Monte-Carlo simulations verifies our assumptions on the main error-causing mechanisms within the JVDD.
Novel Multifunctional Reconfigurable Active Frequency Selective Surface

In this paper, a multifunctional active frequency selective surface (MAFSS) is presented based on the switching responses of active components. The proposed structure comprises periodic arrays of metallic patterns imprinted on both sides of an FR4 substrate on which p-i-n diodes are embedded with parallel feed networks. The novelty of the design lies in its multifunctional characteristics with independent control of the biasing states (ON/OFF) of the diodes mounted on opposite layers. This leads to four different working states (dual bandpass for OFF–OFF state, single-band absorber for OFF–ON state, single-bandpass for ON–OFF state, and reflector for ON–ON state) being facilitated by the proposed reconfigurable AFSS. Furthermore, the effects of the design parameters and active components on multifunctional responses are extensively analyzed by deriving equivalent circuit models. Even though the topology is polarization sensitive, multiple dividable working functions (transmission, absorption, and reflection) have been realized in single geometry, unlike the earlier reported designs. The proposed structure has also been fabricated, and the measured responses show reasonable agreement with the simulated results under normal incidence. An MAFSS with such diverse characteristics is an essential element for regulating electromagnetic wave propagation in manifold applications.
Online Primal-Dual Methods With Measurement Feedback for Time-Varying Convex Optimization

This paper addresses the design and analysis of feedback-based online algorithms to control systems or networked systems based on performance objectives and engineering constraints that may evolve over time. The emerging time-varying convex optimization formalism is leveraged to model optimal operational trajectories of the systems, as well as explicit local and network-level operational constraints. Departing from existing batch and feed-forward optimization approaches, the design of the algorithms capitalizes on an online implementation of primal-dual projected-gradient methods; the gradient steps are, however, suitably modified to accommodate feedback from the system in the form of measurements, hence, the term “online optimization with feedback. ” By virtue of this approach, the resultant algorithms can cope with model mismatches in the algebraic representation of the system states and outputs, they avoid pervasive measurements of exogenous inputs, and they naturally lend themselves to a distributed implementation. Under suitable assumptions, analytical convergence claims are established in terms of dynamic regret. Furthermore, when the synthesis of the feedback-based online algorithms is based on a regularized Lagrangian function, $\boldsymbol{Q}$-linear convergence to solutions of the time-varying optimization problem is shown.
Clinical Investigation : Lymphoma and Leukemia Survival and Neurocognitive Outcomes After Cranial or Craniospinal Irradiation Plus Total-Body Irradiation Before Stem Cell Transplantation in Pediatric Leukemia Patients With Central Nervous System Involvement

Survival and Neurocognitive Outcomes After Cranial or Craniospinal Irradiation Plus Total-Body Irradiation Before Stem Cell Transplantation in Pediatric Leukemia Patients With Central Nervous System Involvement Susan M. Hiniker, MD,* Rajni Agarwal, MD,y Leslie A. Modlin, BA,* Christine C. Gray, PhD,z Jeremy P. Harris, MPhil,* Lynn Million, MD,* Eileen F. Kiamanesh, MS,x and Sarah S. Donaldson, MD* *Department of Radiation Oncology, ySection of Stem Cell Transplantation, Department of Pediatrics, zDivision of Child and Adolescent Psychiatry, Department of Psychiatry, and xCancer Clinical Trials Office, Stanford Cancer Institute, Stanford University, Stanford, California