Reviewerly/exHarmony huggingface dataset:
What Are the Minimal Conditions Required to Define a Symmetric Informationally Complete Generalized Measurement?

Symmetric informationally complete (SIC) positive operator valued measures (POVMs) are a class of quantum measurements which, in addition to being informationally complete, satisfy three conditions: (1) every POVM element is rank one, (2) the Hilbert-Schmidt inner product between any two distinct elements is constant, and (3) the trace of each element is constant. The third condition is often overlooked, since it may give the impression that it follows trivially from the second. We show that this condition cannot be removed, as it leads to two distinct values for the trace of an element of the POVM. This observation has led us to define a broader class of measurements which we call semi-SIC POVMs. In dimension two, we show that semi-SIC POVMs exist, and we construct the entire family. In higher dimensions, we characterize key properties and applications of semi-SIC POVMs, and note that the proof of their existence remains open.
Puncturable Encryption: A Generic Construction from Delegatable Fully Key-Homomorphic Encryption

Puncturable encryption (PE), proposed by Green and Miers at IEEE S&P 2015, is a kind of public key encryption that allows recipients to revoke individual messages by repeatedly updating decryption keys without communicating with senders. PE is an essential tool for constructing many interesting applications, such as asynchronous messaging systems, forward-secret zero round-trip time protocols, public-key watermarking schemes and forward-secret proxy re-encryptions. This paper revisits PEs from the observation that the puncturing property can be implemented as efficiently computable functions. From this view, we propose a generic PE construction from the fully key-homomorphic encryption, augmented with a key delegation mechanism (DFKHE) from Boneh et al. at Eurocrypt 2014. We show that our PE construction enjoys the selective security under chosen plaintext attacks (that can be converted into the adaptive security with some efficiency loss) from that of DFKHE in the standard model. Basing on the framework, we obtain the first post-quantum secure PE instantiation that is based on the learning with errors problem, selective secure under chosen plaintext attacks (CPA) in the standard model. We also discuss about the ability of modification our framework to support the unbounded number of ciphertext tags inspired from the work of Brakerski and Vaikuntanathan at CRYPTO 2016.
Re-Consolidating First-Order Masking Schemes

Application of masking, known as the most robust and reliable countermeasure to side-channel analysis attacks, on various cryptographic algorithms has dedicated a lion’s share of research to itself. The difficulty originates from the fact that the overhead of application of such an algorithmic-level countermeasure might not be affordable. This includes the area- and latency overheads and the amount of fresh randomness required to fulfill the resulting design’s security properties. There are already techniques applicable in hardware platforms that consider glitches into account. Among them, classical threshold implementations force the designers to use at least three shares in the underlying masking. The other schemes, which can deal with two shares, often necessitates the use of fresh randomness.Here, in this work, we present a technique allowing us to use two shares to realize the first-order glitch-extended probing secure masked realization of several functions, including the S-box of Midori, PRESENT, PRINCE, and AES ciphers without any fresh randomness.
Delayed transfer of entanglement to initially populated qubits

The transfer of entangled, quantum correlated, and flying photons from a squeezed field to single-mode cavities is investigated. It is shown that while the transfer of photons begins immediately after the input squeezed field is turned on, the time at which quantum correlations start to be transferred to the cavities is strongly dependent on the initial population of the cavities. For the initially empty cavities, the transfer of quantum correlations begins immediately after the squeezed field is turned on but is delayed by a certain time interval when the cavities are initially populated. We find that the transfer of the quantum correlations is postponed until the one-photon states of the system are almost completely depopulated. In other words, the system ``waits'' for the population of the single-photon states to decay out before starting to build up the quantum correlation between the cavities. The delay time interval is independent of the number of photons initially present in the system but is dependent on the decay rates of the cavities and can be varied (controlled) when the cavities decay with different rates. It is shown that the delayed transfer of the quantum correlation is directly related to the presence of quantum jumps which transfer the population from the entangled to incoherent mixture states.
Towards an Automated Exploration of Secure IoT/CPS Design-Variants

The advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) enabled a new class of connected, smart, and interactive devices. With their continuous connectivity and their access to valuable information in both the digital and physical world, they are highly attractive targets for security attackers. Integrating them into the industry and our daily used devices adds new attack surfaces. These potential threats call for special care of security vulnerabilities during the design of IoT devices and CPS. Due to their resource-constrained nature, designing secure IoT devices and CPS poses a complex task, considering the selectable hardware components and task implementation alternatives. Researchers proposed a range of automatic design tools to support system designers in their task of finding the optimal hardware selection and task implementations. Said tools offer a limited way of modeling attack scenarios for a system under design. The framework proposed in this paper aims at closing this gap, offering system designers a way to consider security attacks and security risks during the early phase of system design. It offers designers the possibility to model security constraints from the view of potential attackers, assessing the probability of successful security attacks and the resulting security risk, alike. We demonstrate the framework’s feasibility and performance by revisiting an industry partner’s potential system design of a future IoT device.
Gaussian process inference modelling of dynamic robot control for expressive piano playing

Piano is a complex instrument, which humans learn to play after many years of practice. This paper investigates the complex dynamics of the embodied interactions between a human and piano, in order to gain insights into the nature of humans’ physical dexterity and adaptability. In this context, the dynamic interactions become particularly crucial for delicate expressions, often present in advanced music pieces, which is the main focus of this paper. This paper hypothesises that the relationship between motor control for key-pressing and the generated sound is a manifold problem, with high-degrees of non-linearity in nature. We employ a minimalistic experimental platform based on a robotic arm equipped with a single elastic finger in order to systematically investigate the motor control and resulting outcome of piano sounds. The robot was programmed to run 3125 key-presses on a physical digital piano with varied control parameters. The obtained data was applied to a Gaussian Process (GP) inference modelling method, to train a network in terms of 10 playing styles, corresponding to different expressions generated by a Musical Instrument Digital Interface (MIDI). By analysing the robot control parameters and the output sounds, the relationship was confirmed to be highly nonlinear, especially when the rich expressions (such as a broad range of sound dynamics) were necessary. Furthermore this relationship was difficult and time consuming to learn with linear regression models, compared to the developed GP-based approach. The performance of the robot controller was also compared to that of an experienced human player. The analysis shows that the robot is able to generate sounds closer to humans’ in some expressions, but requires additional investigations for others.
Hammering Acoustic Analysis Using Machine Learning Techniques for Piping Inspection

In Japan, the deterioration of industrial plants built during the period of high economic growth in the middle of the 20th century has recently become a social concern. Corrosion under insulation (CUI) of piping in such plants is a pressing problem. X-ray and ultrasound inspections are conventional methods for detecting CUI; however, these methods are time-consuming and expensive. Therefore, rapid and low-cost screening techniques for CUI are required. We develop a hammering-type inspection robot system that moves inside the piping and records hammering sounds. Furthermore, we propose an acoustic analysis method to identify anomalous parts from the hammering sound using machine learning techniques. Using three testing pipes, we can successfully identify anomalous parts through acoustic analysis using a deep neural network as a supervised learning method. However, in practical piping inspections, the detection of anomalies without training data is required for further applications. Therefore, we investigate unsupervised learning anomaly detection using an autoencoder and a variational autoencoder and report the results.
Developing Checkpointing and Recovery Procedures with the Storage Services of Amazon Web Services

In recent years, cloud computing has grown in popularity as they give users easy and almost instantaneous access to different computational resources. Some cloud providers, like Amazon, took advantage of the growing popularity and offered their VMs in some different hiring types: on-demand, reserved, and spot. The last type is usually offered at lower prices but can be terminated by the provider at any time. To deal with those failures, checkpoint and recovery procedures are typically used. In this context, we propose and analyze checkpoint and recovery procedures using three different storage services from Amazon: Amazon Simple Storage Service (S3), Amazon Elastic Block Store (EBS) and Amazon Elastic File System (EFS), considering spot VMs. These procedures were built upon the HADS framework, designed to schedule bag-of-tasks applications to spot and on-demand VMs. Our results showed that EBS outperformed the other approaches in terms of time spent on recording a checkpoint. But it required more time in the recovery procedure. EFS presented checkpointing and recovery times close to EBS but with higher monetary costs than the other services. S3 proved to be the best option in terms of monetary cost but required a longer time for recording a checkpoint, individually. However, when concurrent checkpoints were analysed, which can occur in a real application with lots of tasks, in our tests, S3 outperformed EFS in terms of execution time also.
Fully synthetic neuroimaging data for replication and exploration

Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and voxel-level associations with predictor variables (age, sex, verbal IQ, and ICV). The magnitude and spatial distribution of gray matter effects in the observed imaging data were replicated in the pooled results from the synthetic datasets. This approach for generating fully synthetic neuroimaging data has widespread potential for data sharing, including replication, new discovery, and education. Fully synthetic neuroimaging datasets can enable data-sharing because it accurately represents patterns of variance in the original data, while diminishing the risk of privacy disclosures that can accompany neuroimaging data sharing.
Multi-objective Optimization for Guaranteed Delivery in Video Service Platform

Guaranteed-Delivery (GD) is one of the important display strategies for the IP videos in video service platform. Different from the traditional recommendation strategy, GD requires the delivery system to guarantee the exposure amount (also called impressions in some works) for the content, where the amount generally comes from the purchase contract or business consideration of the platform. In this paper, we study the problem of how to maximize certain gains, such as video view (VV) or fairness of different contents (CTR variations between contents) under the GD constraints. We formulate such a problem as a constrained nonlinear programming problem, in which the objectives are to maximize the total VVs of contents and the exposure fairness between contents. In order to capture the trends of VV versus the impression number (page views, PV) for each video content, we propose a parameterized ordinary differential equation (ODE) model, and the parameters of the ODE are fitted by the video historical PV and CLICK datas. To solve the constrained nonlinear programming, we use genetic algorithm (GA) with a specific design of coding scheme considering the ODE constraints. The empirical study based on real-world data and online test on Youku.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practice.
Tissue Segmentation in Nasopharyngeal CT Images Using Two-Stage Learning

Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of computing resources, the structure of the second module is simpler and the number of network layers is less. In addition, our segmentation module is based on U-Net and incorporates a skip structure, which can make full use of the original features of the data and avoid feature loss. We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University. The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method, and can be easily generalized across different tissue types in various organs.
Learning to Hash with Convolutional Network for Multi-label Remote Sensing Image Retrieval

Recently, deep hashing dominated single label image retrieval approaches.However, the complex nature of remote sensing images, which likely contains multi-labels, hardly benefits from the above approaches.To overcome single-label image retrieval limitations in remote sensing domain, we address this problem by proposing a multi-label remote sensing image retrieval (MLRSIR-NET) framework.Specifically, the proposed MLRSIR-NET composed of two main sub-networks: multi-level feature extraction and deep hash.The multi-level feature extraction network predicts multi-level features to exploit different levels of Convolution Neural Network (CNN (characteristics.To suppress discriminative feature representation, the multi-level features are aggregated and feed to Convolutional Block Attention Module (CBAM) to amplify the representation of relevant multi-label features.CBAM is flexibly integrated into our network with end-to-end training.The hash network stacked two fully connected layers aimed to learn multiple hashing functions to encode the feature vector into a compact hash code.Finally, we conduct experiments on two benchmarks for multi-label images: Dense Labelling Remote Sensing Dataset (DLRSD) and Wuhan Dense Labeling Dataset (WHDLD) to systematically assess the performance.The results show that the proposed framework improved the accuracy in terms of Mean Average Precision (MAP) by a considerable margin of 85.4%, 87.2%, 90.8% and 92.9% for 12-bit, 24-bit, 32-bit and 48-bit code lengths respectively on DLRSD.For WHDLD, it can be noted that the proposed framework supers the DCH by 93.8%, 98.7%, 91.9%, and 94.6% on average respectively.
PASMVS: A perfectly accurate, synthetic, path-traced dataset featuring specular material properties for multi-view stereopsis training and reconstruction applications

A Perfectly Accurate, Synthetic dataset for Multi-View Stereopsis (PASMVS) is presented, consisting of 400 scenes and 18,000 model renderings together with ground truth depth maps, camera intrinsic and extrinsic parameters, and binary segmentation masks. Every scene is rendered from 45 different camera views in a circular pattern, using Blender's path-tracing rendering engine. Every scene is composed from a unique combination of two camera focal lengths, four 3D models of varying geometrical complexity, five high definition, high dynamic range (HDR) environmental textures to replicate photorealistic lighting conditions and ten materials. The material properties are primarily specular, with a selection of more diffuse materials for reference. The combination of highly specular and diffuse material properties increases the reconstruction ambiguity and complexity for MVS reconstruction algorithms and pipelines, and more recently, state-of-the-art architectures based on neural network implementations. PASMVS serves as an addition to the wide spectrum of available image datasets employed in computer vision research, improving the precision required for novel research applications.
An Incremental Learning Framework to Enhance Teaching by Demonstration Based on Multimodal Sensor Fusion

Though a robot can reproduce the demonstration trajectory from the human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multi-modal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demonstration task accurately. The multi-modal demonstration data is collected from two different kinds of sensors in the demonstration phase. Then the Kalman filter (KF) and dynamic time warping (DTW) algorithm are used to data preprocessing for the multiple sensor signals. The KF algorithm is mainly used to fuse sensor data of different modalities, and the DTW algorithm is used to align the data in the same timeline. The preprocessed demonstration data is further trained and learned by the incremental learning network and sent to a Baxter robot for reproducing the task demonstrated by the human. Comparative experiments have been performed to verify the effectiveness of the proposed method.
Quantum random access memory via quantum walk

Abstract A novel concept of quantum random access memory (qRAM) employing a quantum walk is provided. Our qRAM relies on a bucket brigade scheme to access the memory cells. Introducing a bucket with chirality left and right as a quantum walker, and considering its quantum motion on a full binary tree, we can efficiently deliver the bucket to the designated memory cells, and fill the bucket with the desired information in the form of quantum superposition states. Our procedure has several advantages. First, we do not need to place any quantum devices at the nodes of the binary tree, and hence in our qRAM architecture, the cost to maintain the coherence can be significantly reduced. Second, our scheme is fully parallelized. Consequently, only O ( n ) steps are required to access and retrieve O (2 n ) data in the form of quantum superposition states. Finally, the simplicity of our procedure may allow the design of qRAM with simpler structures.
Evaluation of Deep Learning Accelerators for Object Detection at the Edge

Deep learning is moving more and more from the cloud towards the edge. Therefore, embedded devices are needed that are reasonably cheap, energy-efficient and fast enough. In this paper we evaluate the performance and energy consumption of popular, off-the-shelf commercial devices for deep learning inferencing. We compare the Intel Neural Compute Stick 2, the Google Coral Edge TPU and the Nvidia Jetson Nano with the Raspberry Pi 4 for their suitability as a central controller in an autonomous vehicle for the formula student driverless.
On speeding up factoring with quantum SAT solvers

Abstract There have been several efforts to apply quantum SAT solving methods to factor large integers. While these methods may provide insight into quantum SAT solving, to date they have not led to a convincing path to integer factorization that is competitive with the best known classical method, the Number Field Sieve. Many of the techniques tried involved directly encoding multiplication to SAT or an equivalent NP-hard problem and looking for satisfying assignments of the variables representing the prime factors. The main challenge in these cases is that, to compete with the Number Field Sieve, the quantum SAT solver would need to be superpolynomially faster than classical SAT solvers. In this paper the use of SAT solvers is restricted to a smaller task related to factoring: finding smooth numbers, which is an essential step of the Number Field Sieve. We present a SAT circuit that can be given to quantum SAT solvers such as annealers in order to perform this step of factoring. If quantum SAT solvers achieve any asymptotic speedup over classical brute-force search for smooth numbers, then our factoring algorithm is faster than the classical NFS.