Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault-tolerant quantum computing with three-dimensional surface codes

Quantum computers are far more error-prone than their classical counterparts. Therefore, to build a quantum computer capable of running large-scale quantum algorithms, we must use the techniques of quantum error correction to ensure that the computer produces the correct output even when its components are unreliable. However, the resource requirements of building such a fault-tolerant quantum computer are currently prohibitive. Here, we examine the utility of using three-dimensional (3D) surface codes in a fault-tolerant quantum computer. This family of topological error-correcting codes is a generalization of the well-known 2D surface code to three spatial dimensions. We show that certain 3D surface codes have a transversal logical non-Clifford gate. In a quantum computing architecture, a non-Clifford gate is required to achieve computational universality. Transversal gates do not entangle qubits in different codes, so they are naturally fault tolerant because they do not spread errors. Next, we consider the problem of decoding 3D surface codes. In a quantum error-correcting code, we cannot observe the qubits directly, so we measure parity-check operators to gain information about the state of the code. Decoding is the problem of estimating what error has occurred given a list of unsatisfied parity checks. We observe that 3D surface codes offer asymmetric protection against bit-flip and phase-flip errors, but in both cases, we find that a threshold error rate exists below which we can suppress logical errors by increasing the size of the code. We use our results about logical gates and decoding to propose two fault-tolerant quantum computing architectures that utilize 3D surface codes. Finally, we compare the resource requirements of our architectures with the requirements of leading quantum computing architectures based on topological codes. We find that one of our architectures may be competitive with the leading architectures, depending on the properties of the physical systems used to build the qubits.
Trustworthy Online Controlled Experiments

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions.Learn how toUse the scientific method to evaluate hypotheses using controlled experiments Define key metrics and ideally an Overall Evaluation CriterionTest for trustworthiness of the results and alert experimenters to violated assumptionsBuild a scalable platform that lowers the marginal cost of experiments close to zeroAvoid pitfalls like carryover effects and Twyman's lawUnderstand how statistical issues play out in practice.
Pothole detection system design with proximity sensor to provide motorcycle with warning system and increase road safety driving

Technology in transportation becomes important nowadays and must be developed overtime. In the era of development, there are so many roads extensions to balance the significant additions of motorized vehicles. The increasing number of vehicles caused problems such as damaged roads and lack of maintenance to the road itself. Lack of awareness to repair the damaged roads, especially potholes, make it more dangerous for riders to drive safely. This issues are more concerning right now because the increasing number of accident and mortality. To prevent accident to happen, the pothole detection sensor can be used to on car system. The development of pothole detection sensor in this research is adopted from the proximity sensor system where in that system they use camera and digital imaging process. The advantages from our system that we research and develop are more user friendly from the feasibility and the financial aspect. Low cost sensor with the same quality of the existing system is devlop in this work. Despite the use of low cost sensor, the maintenance also on the lower cost and easier to do. As the result of this research and study, an error was obtained between the distance that detected the pothole should be in less than 4% distance range from the sensor.
Professionals’ support role for survivors of femicide and relatives of victims

The ways in which care professionals’ support is provided to survivors of femicide, as well as to victims’ families (children, parents and siblings), may be crucial for their process of rehabilitation and integration into society, thereby increasing their chances to live a relatively satisfying life in the future. The objective of this article is to analyse the characteristics of this professional response, and suggest recommendations to enhance the care provided to femicide survivors and their families, in order to make it more significant and context-sensitive. Thus, a qualitative research, including 12 in-depth interviews, was conducted among femicide survivors and family members of femicide victims from the provinces of Imbabura and Carchi in the Ecuadorian highlands. Notwithstanding the governmental efforts, both through legislative changes and the development of care plans for the victims, the findings indicate an ambivalent professional response, as well as a significant lack of support networks for survivors and families of femicide victims.
Catena: A Near-Threshold, Sub-0.4-mW, 16-Core Programmable Spatial Array Accelerator for the Ultralow-Power Mobile and Embedded Internet of Things

In this article, we present Catena, a near-threshold voltage 16-core programmable spatial array accelerator supporting workloads for ultralow-power (ULP) mobile and embedded Internet of Things applications. We observe that employing supply voltage scaling alone in a large-scale, massively parallel spatial architecture, such as Catena, results in marginal runtime energy efficiency. The reason is that ultralow-voltage operation magnifies the energy waste of underutilized and always-on hardware in portion to the system’s total energy consumption. Hence, we propose circuit and architecture techniques to minimize such energy waste and extend the energy efficiency of our spatial array accelerator architecture. To demonstrate the effectiveness of the proposed techniques, we design and prototype Catena in a 65-nm low-power CMOS. Our prototype achieves 228 pJ/cycle. As compared to a spatial-like architecture running the same workload, Catena achieves 2.7 $\times $ higher energy efficiency.
An Improved Crow Search Algorithm Based on Spiral Search Mechanism for Solving Numerical and Engineering Optimization Problems

The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well. However, its simple search mechanism also leads to the algorithm’s slow convergence speed and its ease of falling into local optimization when solving complex optimization problems. In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism. By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced. Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization. The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems. The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms. The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance.
Efficient Ridesharing Framework for Ride-matching via Heterogeneous Network Embedding

Ridesharing has attracted increasing attention in recent years, and combines the flexibility and speed of private cars with the reduced cost of fixed-line systems to benefit alleviating traffic pressure. A major issue in ridesharing is the accurate assignment of passengers to drivers, and how to maximize the number of rides shared between people being assigned to different drivers has become an increasingly popular research topic. There are two major challenges facing ride-matching: scalability and sparsity. Here, we show that network embedding drives the optimal matches between drivers and riders. Contrary to existing approaches that merely depend on the proximity between passengers and drivers, we employ a heterogeneous network to learn the latent semantics from different choices in two types of ridesharing, and extract features in terms of user trajectories and sentiment. A novel framework for ridesharing, RShareForm, which encodes not only the objects but also a variety of semantic relationships between them, is proposed. This article extends the existing skip-gram model to incorporate meta-paths over a proposed heterogeneous network. It allows diverse features to be used to search for similar participants and then ranks them to improve the quality of ride-matching. Extensive experiments on a large-scale dataset from DiDi in Chengdu, China show that by leveraging heterogeneous network embedding with meta paths, RShareForm can significantly improve the accuracy of identifying the participants for ridesharing over existing methods, including both meta-path guided similarity search methods and variants of embedding methods.
Kinova Modular Robot Arms for Service Robotics Applications

This article presents Kinova's modular robotic systems, including the robots JACO2 and MICO2, actuators and grippers. Kinova designs and manufactures robotics platforms and components that are simple, sexy and safe under two business units: Assistive Robotics empowers people living with disabilities to push beyond their current boundaries and limitations while Service Robotics empowers people in industry to interact with their environment more efficiently and safely. Kinova is based in Boisbriand, Québec, Canada. Its technologies are exploited in over 25 countries and are used in many applications, including as service robotics, physical assistance, medical applications, mobile manipulation, rehabilitation, teleoperation and in research in different areas such as computer vision, artificial intelligence, grasping, planning and control interfaces. The article describes Kinova's hardware platforms, their different control modes (position, velocity and torque), control features and possible control interfaces. Integration to other systems and application examples are also presented.
Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings

Keyword search is a prominent approach to querying Web data. For graph-structured data, a widely accepted semantics for keywords is based on group Steiner trees. For this NP-hard problem, existing algorithms with provable quality guarantees have prohibitive run time on large graphs. In this paper, we propose practical approximation algorithms with a guaranteed quality of computed answers and very low run time. Our algorithms rely on Hub Labeling (HL), a structure that labels each vertex in a graph with a list of vertices reachable from it, which we use to compute distances and shortest paths. We devise two HLs: a conventional static HL that uses a new heuristic to improve pruned landmark labeling, and a novel dynamic HL that inverts and aggregates query-relevant static labels to more efficiently process vertex sets. Our approach allows to compute a reasonably good approximation of answers to keyword queries in milliseconds on million-scale knowledge graphs.
Sequence Labeling and Transduction with Output-Adjusted Actor-Critic Training of RNNs

Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks. We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling. To do so, we address one of the RNN’s most prominent shortcomings, the fact that it is not exposed to its own errors with the maximum-likelihood training. We frame the prediction of the output sequence as a sequential decision-making process, where the RNN takes a series of actions without being conditioned on the ground-truth labels. We then train the network with an output-adjusted actorcritic algorithm (AC-RNN). We comprehensively compare this strategy with maximum-likelihood training for both RNNs and CRFs on three structuredoutput tasks. The proposed AC-RNN efficiently matches the performance of the CRF on NER and CCG tagging, and outperforms it on machine transliteration. We show that the output-adjusted actor-critic training is significantly better than other techniques for addressing RNN’s exposure bias, such as Scheduled Sampling, and Self-Critical policy training.
Grey Wolf Aware Energy-saving and Load-balancing in Software Defined Networks Considering Real Time Traffic

The energy depletion in Software Defined Network (SDN) is a primary issue for the operators and regulators of the network because of the ubiquitous implementation of SDN to satisfy the demands of bandwidth-consuming applications. While conventional optimization of edge weights technique is used in various load balancing mechanisms in IGP networks. In this paper, we present a new edge weight optimization algorithm known as Grey Wolf Aware Load balancing and Energy saving(GLE) which simultaneously optimizes energy consumption and load balance in SDN networks. This technique can be applied along with the current mechanisms which use the link active/inactive concept to achieve a significant improvement in energy efficiency. The proposed method in this paper is a new aspect to save energy in SDN networks without compromising conventional traffic-engineering realizations in the environment of plain IP routing. To evaluate the energy-saving by the proposed mechanism without loss of generality, we tested it on various currently proposed methods who have used the technique of link sleeping as described in the related works. The evaluation is tested on the GEANT network topology. GLE shows a noteworthy improvement in the energy-saving as compared to the recently proposed algorithms while achieving significant performance in load balance.
Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach

Points-of-interest (POIs; i.e., restaurants, bars, landmarks, and other entities) are common in web-mined data: they greatly explain the spatial distributions of urban phenomena. The conventional modeling approach relies upon feature engineering, yet it ignores the spatial structure among POIs. In order to overcome this shortcoming, the present paper proposes a novel spatial model for explaining spatial distributions based on web-mined POIs. Our key contributions are: (1) We present a rigorous yet highly interpretable formalization in order to model the influence of POIs on a given outcome variable. Specifically, we accommodate the spatial distributions of both the outcome and POIs. In our case, this modeled by the sum of latent Gaussian processes. (2) In contrast to previous literature, our model infers the influence of POIs without feature engineering, instead we model the influence of POIs via distance-weighted kernel functions with fully learnable parameterizations. (3) We propose a scalable learning algorithm based on sparse variational approximation. For this purpose, we derive a tailored evidence lower bound (ELBO) and, for appropriate likelihoods, we even show that an analytical expression can be obtained. This allows fast and accurate computation of the ELBO. Finally, the value of our approach for web mining is demonstrated in two real-world case studies. Our findings provide substantial improvements over state-of-the-art baselines with regard to both predictive and, in particular, explanatory performance. Altogether, this yields a novel spatial model for leveraging web-mined POIs. Within the context of location-based social networks, it promises an extensive range of new insights and use cases.
Machine Learning Computers With Fractal von Neumann Architecture

Machine learning techniques are pervasive tools for emerging commercial applications and many dedicated machine learning computers on different scales have been deployed in embedded devices, servers, and data centers. Currently, most machine learning computer architectures still focus on optimizing performance and energy efficiency instead of programming productivity. However, with the fast development in silicon technology, programming productivity, including programming itself and software stack development, becomes the vital reason instead of performance and power efficiency that hinders the application of machine learning computers. In this article, we propose Cambricon-F, which is a series of homogeneous, sequential, multi-layer, layer-similar, and machine learning computers with same ISA. A Cambricon-F machine has a fractal von Neumann architecture to iteratively manage its components: it is with von Neumann architecture and its processing components (sub-nodes) are still Cambricon-F machines with von Neumann architecture and the same ISA. Since different Cambricon-F instances with different scales can share the same software stack on their common ISA, Cambricon-Fs can significantly improve the programming productivity. Moreover, we address four major challenges in Cambricon-F architecture design, which allow Cambricon-F to achieve a high efficiency. We implement two Cambricon-F instances at different scales, i.e., Cambricon-F100 and Cambricon-F1. Compared to GPU based machines (DGX-1 and 1080Ti), Cambricon-F instances achieve 2.82x, 5.14x better performance, 8.37x, 11.39x better efficiency on average, with 74.5, 93.8 percent smaller area costs, respectively. We further propose Cambricon-FR, which enhances the Cambricon-F machine learning computers to flexibly and efficiently support all the fractal operations with a reconfigurable fractal instruction set architecture. Compared to the Cambricon-F instances, Cambricon-FR machines achieve 1.96x, 2.49x better performance on average. Most importantly, Cambricon-FR computers are able to save the code length with a factor of 5.83, thus significantly improving the programming productivity.
Optimal Path Following for Small Fixed-Wing UAVs Under Wind Disturbances

This article presents a novel path-following algorithm for fixed-wing unmanned aerial vehicles by virtue of a nonlinear optimal control approach and wind disturbance observers. Different from some exiting algorithms, the proposed algorithm formulates the path-following problem into a control problem by introducing auxiliary dynamics for the path parameter. The proposed controller is designed in an optimal and systematic manner where the control action is generated according to a well-defined cost function. This framework does not require any complex geometric coordinate transformation and can be easily tuned to accommodate curved reference paths, making it straightforward to deploy in different flight missions. Moreover, the wind influences on the path-following performance is explicitly compensated by the proposed algorithm based on the wind estimates provided by nonlinear disturbance observers. The closed-loop stability, including the auxiliary dynamics for path parameter and observer dynamics for wind estimation, is also analyzed. The feasibility and effectiveness of the proposed algorithm have been thoroughly validated in simulation studies and realistic flight tests.
Resource Management and Simulation Tools in Fog Computing – A comparative Study

Internet of Things (IoT) evolved as a core technology in building smart, healthcare and industry automation applications. The massive data produced by these applications has to be processed. Present Cloud-IoT models offer cost effectiveness, scalability and reliability. Nevertheless, the large quantity of data and rate at which it is generated by IoT devices challenge the bottleneck between cloud and IoT devices. Therefore, processing the tasks always on cloud may not be an effective solution for latency sensitive IoT applications. Fog computing is a distributed paradigm that brings cloud resources closer to the devices (sensors/smart) that generate data there by addressing the above challenges. Resource management is a significant issue for enhanced utilization of fog resources and to provide best possible service to IoT applications. This survey paper emphasizes on state-of-the-art in resource management and simulation tools in line with comparison of various edge computing paradigms. The study concludes specifying the future challenges that needs to be addressed in the upcoming research.
MEMS based radar sensor for automotive collision avoidance

This dissertation presents the architecture of a new MEMS based 77 GHz frequency modulated continuous wave (FMCW) automotive long range radar sensor. The design, modeling, and fabrication of a novel MEMS based TEio mode Rotman lens. MEMS based Single-pole-triple-throw (SP3T) RF switches and an inset feed type microstrip antenna array that form the core components of the newly developed radar sensor. The novel silicon based Rotman lens exploits the principle of a TEio mode rectangular waveguide that enabled to realize the lens in silicon using conventional microfabrication technique with a cavity depth of 50 um and a footprint area to 27 mm x 36.2 mm for 77 GHz operation. The microfabricated Rotman lens replaces the conventional microelectronics based analog or digital beamformers as used in state-ofthe-art automotive long range radars to results in a smaller form-factor superior performance less complex low cost radar sensor. The developed Rotman lens has 3 beam ports, 5 array ports, 6 dummy ports and HFSS simulation exhibits better than -2 dB insertion loss and better than -20 dB return loss between the beam ports and the array ports. A MEMS based 77 GHz SP3T cantilever type RF switch with conventional ground connecting bridges (GCB) has been designed, modelled, and fabricated to sequentially switch the FMCW signal among the beam ports of the Rotman lens. A new continuous ground (CG) SP3T switch has been designed and modeled that shows a 4 dB improvement in return loss, 0.5 dB improvement in insertion loss and an isolation improvement of 3.5 dB over the conventional GCB type switch. The fabrication of the CG type switch is in progress. Both the switches have a footprint area of 500 um x 500 um. An inset feed type 77 GHz microstrip antenna array has been designed, modelled, and fabricated on a Duroid 5880 substrate using a laser ablation technique. The 12 mm x 35 mm footprint area antenna array consists of 5 sub-arrays with 12 microstrip patches in each of the sub-arrays. HFSS simulation result shows a gain of 18.3 dB, efficiency of 77% and half power beam width of 9°.