Miracle-dz/UniFAR-FTUs huggingface dataset:
Vulnerabilities and Attacks of Inter-device Coordination in Programmable Networks

In programmable networks, some networking systems coordinate data plane switches to realize in-network functions (e.g., in-band network telemetry). However, the vulnerabilities of inter-device coordination are still largely unknown and neglected, which is highly concerning given the increasing popularity of this paradigm. In this paper, we identify three attack scenarios built upon such vulnerabilities, where attackers mislead the behaviors of networking systems that exploit inter-device coordination to execute in-network functions. We implement 20 existing networking systems on Tofino-based switches and a simulator, and attack these systems with the identified attacks. The experimental results indicate that our attacks significantly interfere with the normal operations of the selected networking systems, e.g., the cache hit rate of NetCache drops 38%. Our analysis also demonstrates that none of existing methods can fully mitigate our attacks since they fail to verify the packets for inter-device coordination.
Design Solutions for Reducing AC resistance of High Frequency Windings

Electromagnetic devices operating at high frequencies (hundreds of kHz) are becoming increasingly present in applications that regards switching and data transmission. In this sense, their windings are increasingly made of Litz type wires in several configurations. The present paper reveals a comparative FEMM study between the most used configurations. The numerical analysis of the skin and proximity effect influence at frequencies up to 500 kHz was pursued. The Litz#7 configuration proves to have the best performance. Afterwards, a comparative study is carried out between the values of alternating current resistance values, determined numerically and those analytically calculated with formulas known from the specialized literature.
MARS: An Adaptive Multi-Agent DRL-based Scheduler for Multipath QUIC in Dynamic Networks

The multipath extension of the Quick UDP Internet Connection (QUIC) protocol, also called MPQUIC, is currently attracting increasing attention from both industry and academia. The multipath scheduler of MPQUIC determines how to distribute the packets onto different paths. However, our experimental results show that they fail to adapt to various receive buffer sizes and Quality of Service (QoS) requirements while applying current multipath schedulers into MPQUIC due to the diversity of devices and applications. These problems are especially severe under heterogeneous and dynamic network environments. To tackle these problems, we propose MARS, a Multi-Agent deep Reinforcement learning (MADRL) based Multipath QUIC Scheduler, which is able to promptly adapt to dynamic network environments. It exploits the MADRL method to learn a neural network for each path and generate scheduling policy. Besides, it introduces a novel multi-objective reward function that takes out-of-order (OFO) queue size and different QoS metrics into consideration to realize adaptive scheduling optimization. We implement MARS in an MPQUIC prototype and compare it with the state-of-the-art multipath schedulers in both emulated and real-world networks. Experimental results show that MARS outperforms the other schedulers with better adaptive capability regarding the receive buffer sizes and QoS.
Beyond Code Generation: The Need for Type-Aware Language Models

Type systems and type inference systems can be used to help text and code generation models like GPT-3 produce more accurate and appropriate results. These systems provide information about the types of variables, functions, and other elements in a program or codebase, which can be used to guide the generation of new code or text. For example, a code generation model that is aware of the types of variables and functions being used in a program can generate code that is more likely to be syntactically correct and semantically meaningful. We argue for the specialization of language models such as GPT-3 for automatic program repair tasks, incorporating type information in the model's learning process. A trained language model is expected to perform better by understanding the nuances of type systems and using them for program repair, instead of just relying on the general structure of programs.
Improvement of Channel Mobility of GaN‐MOSFETs With Thermal Treatment for Recess Surface

In this work, a thermal treatment technique under NH3 ambient for the recess surface of GaN‐MOSFETs is developed. Immediately following the fabrication process of the recess, the bottoms of these structures have rough surfaces and step‐terrace structures cannot be observed because of the plasma‐induced damage. However, clear step‐terrace structures are formed by the thermal treatment, and the RMS values of the surface roughness decrease from 0.26 to 0.14 nm, which is comparable to those of the as‐grown epitaxial film. In addition, it is confirmed that concentrations of impurity atoms introduced during the recess fabrication process are decreased by the thermal treatment. The channel mobility of the GaN‐MOSFETs without the thermal treatment is 118 cm2 Vs−1, whereas that of the device whose fabrication process includes the thermal treatment increased to 149 cm2 Vs−1. These results indicate that the thermal treatment under NH3 ambient is effective for reducing on‐state resistance of GaN‐MOSFETs.
Minimum Delay Optimization for Message Scheduling in In-Vehicle Applications Based on Pheromone Resetting Strategy

In-vehicle applications generally have latency requirements; serious safety accidents will likely be caused due to the applications failing to take the correct actions within a specified time frame. In this study, a Pheromone Resetting Ant Colony Optimization (PRACO) algorithm is proposed to address the calculation of the minimum response time of an application whose messages are transmitted by using the Controller Area Network with flexible data rates (CAN FD) bus. A Random Popup (RP) algorithm is equipped in PRACO to quickly obtain the valid message sequence, followed by resetting the pheromones on all paths if ants find a new optimal valid message sequence path. The minimum response delay of an in-vehicle application can be further got through a continuous iterative search and pheromone update. A Directed Acyclic Graph (DAG) workflow scheduling example and an Adaptive Cruise Control (ACC) application are used to conduct the simulation experiment. The results show that our PRACO algorithm significantly outperforms other static scheduling algorithms in obtaining the lowest response latency.
Design of Autonomous Driving Verification Platform Based on Udacity Vehicle Simulator

Various traffic problems are due to human factors such as the decreased ability of drivers to perceive, recognize, and control vehicles. Based on road images, we have designed an end-to-end neural network model that can predict driving behavior, on which we have based an end-to-end modeling application. The model is designed as a decision model on the basis of the MobileNet V2 model, which learns and predicts driving behavior through behavioral cloning. The model is capable of predicting steering angles more accurately and uses a lightweight network that is small and efficient, offering significant advantages in applications for embedded devices. Simulation results demonstrate that the model and driver can perform tasks such as straight ahead, turning, acceleration and deceleration on the road.
Numerical analysis of blazed wire-grid polarizer for plasmonic enhancement

We analyze the characteristics of plasmonics-based enhancement of a wire-grid polarizer (WGP) by rigorous coupledwave analysis (RCWA). We consider blazed WGP (bWGP) for improvement of polarimetric performance based on plasmonic momentum-matching in the metal/dielectric interface. The analysis used a model of triangular wire-grids approximated with five graded layers of identical thickness. We have compared the performance to that of a conventional WGP (cWGP) with a corresponding lamellar grating shape profile. As a performance measure, we calculated transmittance (TR) and extinction ratio (ER). It was found that TR in both cases tends to decrease monotonically with a longer period (Λ). The maximum TR of bWGPs is lower than cWGPs. On the other hand, maximum ER of bWGPs is much higher than that of cWGPs, particularly at a longer period, with an extinction peak peaked at Λ = 800 nm. For cWGPs, an extinction peak is observed at Λ = 200 nm with comparable enhancement (~42 dB). We have also computed relative TR (RTR) and relative ER (RER) for assessment of performance relative to cWGP. RTR decreases slowly in a manner similar to TR, however, RER increases exponentially with a longer wire-grid period. The results suggest that strong localization of near-fields observed with bWGPs can be used to improve polarimetric performance of a WGP.
The Role of Nonlinear Cout in Continuous Class F PAs

This paper provides a comprehensive analysis of the role played by the equivalent nonlinear output capacitor (Cout), seen at the transistor’s intrinsic drain, in the efficiency of continuous class F (CCF) power amplifiers (PAs). New design equations of the CCF PA are derived to incorporate the effect of this capacitance. It is noted that the nonlinearity of (Cout) can impose both active and passive second harmonic terminations at the current-source reference plane (CRP). A design methodology is proposed to obtain the optimum, passive, second harmonic terminations that maximize efficiency at the load reference plane (LRP). Finally, this improved design methodology is validated with experimental tests made on a CCF PA prototype.
Reputation-based User Vehicle Assignment in Intelligent and Connected Vehicle Platoons

Vehicle platooning is a promising and emerging framework in intelligent transportation systems. Recent works consider reputation-based approaches for head selection in single platoons, in order to optimize safety and security. When a large number of user vehicles having the same destination want to benefit from platooning, several platoons need to be formed. To this end, user vehicles are allocated to different platoons, with each platoon being led by a platoon head. To ensure necessary network bandwidth and latency, the number of user vehicles between the newly formed platoons must be balanced. However, an arbitrary assignment of user vehicles in platoons can bias the future selection of the platoon heads in such reputation-based approaches. This work considers reputation-based platooning systems and proposes an optimal approach to balance the number of user vehicles in platoons while at the same time ensures fairness in the reputation score of platoon heads. A mixed-linear integer programming formulation is proposed, which provides an optimal allocation of the user vehicles in platoons based on the above objectives. The approach is validated using multiple synthetically generated datasets using 14 different input parameters. The obtained results demonstrate the optimality of the proposed method while achieving significant time performance speedups $(\sim 140\mathrm{x}$ on average) when compared to a brute-force exhaustive method.
A Fast Rank-Revealing Method for Solving High-Dimensional Global Optimization Problems

We develop a fast rank-revealing method for solving generic high-dimensional global optimization problems, suitable for nonlinear, non-convex systems in addition to linear and convex ones. The method is shown to significantly outperform state-of-the-art optimization techniques such as the Machine Learning (ML)-based Bayesian Optimization (BO) in run time as well as in the capability of finding a global optimum. It has been successfully applied to solve real-world floorplanning and placement problems in heterogeneously integrated system design.
Polarimetric Image Classification using Texture Features based on the Gray Level Co-occurrence Matrix and Local Binary Patterns

The paper assesses the use of two of the most common texture information extraction techniques for the classification of polarimetric Synthetic Aperture Radar (PolSAR) images and proposes a traditional machine learning framework for the classification step, using a fused feature vector. We consider both features extracted from the Gray Level Co-occurrence Matrix (GLCM) and features extracted using Local Binary Patterns (LBP) - based approaches. The feature vector integrating both types of information is computed on a pre-smoothed version of the input data and classification is done using Support Vector Machines (SVM). We evaluate our approach on synthetic data and on real polarimetric images. The experimental results show that the proposed solution provides good accuracy figures for high resolution images even when the number of training samples is low.
A 21 Gb/s Arbitrary Binary Sequence Generator for PMCW Radar based on a TSPC Serializer in 22 nm FDSOI

This work presents an arbitrarily programmable pseudo-random binary sequence (PRBS) generator for phase-modulated continuous-wave (PMCW) radar transmitters. It incorporates a mixed-signal architecture with a digitally implemented shift register connected to a novel serializer architecture based on true single-phase-clock (TSPC) flip-flops. Unlike static, non-configurable linear-feedback shift registers (LFSRs), this circuit can generate more complex sequences, like the almost perfect auto-correlation sequence (APAS), that exhibit a better radar performance. The circuit is realized on a 22nm fully-depleted silicon on insulator (FDSOI) technology and consumes a total chip area of 600×530μm2. On-wafer measurements show a maximum chip rate of 21Gb/s at a 16.9mW power consumption. To the best of the authors’ knowledge, the proposed circuit marks the first programmable PRBS generator operating at this high chip rate. It paves the way for on-chip generation of complex broadband sequences for millimeter-wave digital MIMO radar systems for consumer or automotive applications.
Polysilicon near-infrared photodetector with performance comparable to crystalline silicon devices

We demonstrate high responsivity in a polysilicon (Poly-Si) near-infrared photodetector by reducing the adverse effects of grain boundaries. The presence of grain boundaries is a serious problem in polysilicon since they act as traps and degrade the electrical characteristics. However, the relation between grain boundaries and photodetector performance has not yet been reported. Here, the effect of grain boundary on the resistivity and responsivity of Poly-Si devices is investigated. The resistivity of Poly-Si is higher than that of crystalline Si (c-Si) at the same doping concentration due to the potential barrier of the grain boundary, and its degradation of electrical characteristics is mitigated here by increasing the doping concentration. The photodetector with a highly-doped polysilicon layer exhibits a responsivity of 0.48 A/W at 900 nm, which is almost the same as that of c-Si photodetectors. In contrast, polysilicon devices with the same doping concentration as c-Si devices show degraded responsivity due to grain boundary traps. In addition, to improve the responsivity at target wavelengths of 900 nm and 1,064 nm, we deposited an indium-tin-oxide layer, which reduces the surface reflectance on the Poly-Si photodetector. The responsivity is improved by 22.9% and 50.0% at 900 nm and 1,064 nm respectively compared to devices without the ITO. The results here confirm the ability to fabricate low cost Poly-Si photodetectors with high responsivity which show similar or superior optical response compared to commercial c-Si devices.
Uncertainty Aware Task Allocation for Human-Automation Cooperative Recognition in Autonomous Driving Systems

Cooperative recognition, a method to achieve human-automation cooperation in the recognition phase of the autonomous driving system, has been proposed to address the challenges in the conventional control phase cooperation, e.g., taking over vehicle control. In cooperative recognition, the operator intervenes in recognition tasks that are difficult for the automated system alone to improve driving efficiency and safety. The challenge is the integration of both human and automated systems while both participants have different characteristics, processing capabilities, and uncertainty in the decisions (recognition results). The objectives of this study are task allocation (i.e., when and for which targets the operator should intervene) taking into account the intervention efficiency and human state. And also combine the human intervention and recognition result of the automated systems to solve the uncertainties in both participants. We formulated this problem with a Partially Observable Markov Decision Process (POMDP). The simulator experiment indicated that the recognition result of the automated system and the operator’s intervention were stochastically combined. The intervention requests to the operator adapted to the operator state and could be reduced while maintaining driving efficiency and minimizing risk omissions.
Dynamic Aperture Studies of the nuSTORM FFAG Ring

FFAG rings with a racetrack configuration are very promising as their flexible design allow for dedicated spaces for injection/extraction, RF cavities etc. A racetrack FFAG is considered as an option for the nuSTORM facility, which aims to deliver neutrino beams produced from the decay of muons stored in a ring with long sections pointing towards detectors. In this paper we discuss the definition of dynamic aperture in these machines and use the PyZgoubi framework to compute the many turn motion in the nuSTORM ring. The roles of machine imperfections and symmetry are discussed.
HPCR-VI: Heterogeneous point cloud registration for vehicle-infrastructure collaboration

The perceptual information acquired by a single vehicle-side LiDAR in autonomous driving is limited, and this phenomenon is more prominent at intersections where vehicles are turning. Existing solutions improve vehicle perception by designing complex systems to match homogeneous point clouds acquired by the same type of sensors. In this study, we propose a heterogeneous point cloud registration for vehicle-infrastructure collaboration (HPCR-VI) that supplements the missing sensory information of the vehicle-side mechanical LiDAR with the point cloud information acquired by the infrastructure-side solid-state LiDAR. The HPCR-VI framework proposed in this paper breaks the limitation of homogeneous point cloud registration and can quickly obtain alignment results from two frames of heterogeneous point clouds, whose densities and viewing angles differ greatly, solving the heterogeneous point cloud registration problem where traditional point cloud alignment methods fail. Our proposed method is tested on the DAIR-V2X dataset, and the success rate of alignment is 40-50 points higher than that of the baseline method.
Low-Voltage Operation AlInN/GaN HEMTs on Si with High Output Power at sub-6 GHz

This paper describes the DC and RF performance of AlInN/GaN HEMTs on Si substrates for low-voltage operation to meet the demand of user equipment. By adapting the regrowth ohmic structure, low on-resistance of 0.6 Ω-mm was achieved due to the reduction of the contact resistance. Even with the relatively long gate length of 0.3 μm, lower voltage operation was achieved compared to other reported work. The obtained fmax of 53.8 GHz exceeds the requirements of 5G mobile communication applications for the sub-6 GHz frequency range. Large-signal measurements on a gate width of 10x100 μm device exhibited a saturated power density of 1.52 W/mm and power added efficiency of 62.7 % at 5 GHz biased at Vds of 5 V. The device also exhibited excellent performance between Vds of 1.5 V and 12 V. To the best of our knowledge, this represents the highest RF performance with low-voltage operation for a GaN HEMT in the sub-6 GHz region.
Variational Convolutional Sparse Coding with Learned Thresholding

In this paper we extend the variational sparse coding framework to the case of convolutional sparse coding. This approach shares the same explainability advantage of sparse coding, but also benefits from shift-invariance of the atoms, since the placement of the features is now encoded in the sparse latent variable. The decoder is built using a depthwise convolution layer followed by a sum operation. Results show that the learned atoms are representative and they are indeed the location-free counterparts of the atoms learned in the non-convolutional setting.
An Approach for Traffic Flow Prediction Combining 3D Convolution and Attention Mechanism

Highly reliable traffic flow forecasting is essential to improve the management efficiency of intelligent transportation systems. However, traffic flow data contains complex spatio-temporal relationships, while this complex relationship is an organic whole. Therefore, improving traffic flow prediction performance is a challenging task. Some existing works have demonstrated the existence of spatio-temporal correlation in traffic flow data, but not all the features in the data are fully mined, mainly because the spatio-temporal diversity of traffic flow data is not taken into account, and the modeling approach of multi-network structure is used for separate modeling, which destroys the overall principle of traffic flow and eventually makes it difficult to achieve the expected prediction effect of the model. Within this paper, we propose a deep learning model ST-3DANet with a single network structure to model the spatio-temporal raster data features of traffic flow to improve the prediction effect.ST-3DANet combines 3D convolution and residual structure, and introduces an attention mechanism to automatically capture the spatio-temporal correlation of traffic flow data. Specifically, the spatio-temporal data for traffic flow prediction is taken as a whole, and the spatio-temporal diversity in traffic data is automatically mined by 3D convolution, and the model’s ability to discover spatiotemporal diversity and long-range spatial correlation is enhanced by using the attention mechanism. Experiments were conducted on a real dataset in New York, and the results showed that our model outperformed other benchmark models in terms of predictive effectiveness.
Spectral phonon mean free path and thermal conductivity accumulation in defected graphene: The effects of defect type and concentration

The spectral phonon properties in defected graphene were unclear due to the lack of advanced techniques on predicting the phonon-defect scattering rate without ﬁtting parameters. Taking ad-vantages of the extended phonon normal mode analysis, we obtained the spectral phonon relaxation time and mean free path (MFP) in defected graphene and studied the impacts of three common types of defects, Stone-Thrower-Wales (STW) defect, double vacancy (DV), and mono vacancy (MV). Phonon-STW defect scattering rate is found to have no signiﬁcant frequency dependence, and as a result, the relative contribution of long-wavelength phonons sharply decreases. In con-trast, the phonon scattering by DVs or MVs exhibits a frequency dependence of τ − 1 p − d ∼ ω 1 . 1 − 1 . 3 except a few long-wavelength phonons, providing a critical revisit to the traditionally used ∼ ω 4 dependence. We note that although MV-defected graphene has the lowest thermal conductivity as compared to the other two defected graphene samples at the same defect concentration, it has a portion of phonons with the longest MFP. The contribution from the long-MFP and long-wavelength phonons does not decrease much as the vacancy concentration increases. STW defect and MV block more out-of-plane modes than in-plane modes while DV has less bias on which mode to block. As the MV concentration increases from 0 to 1.1%, the relative contribution from out-of-plane modes decreases from 30% to 18%, while that of the transverse acoustic mode remains at around 30%. These ﬁndings of spectral phonon properties can provide more insights than the eﬀective properties, and beneﬁt the prospective phononic engineering.