Reviewerly/exHarmony huggingface dataset:
Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms

Estimation of Distribution Algorithms (EDAs) are one branch of Evolutionary Algorithms (EAs) in the broad sense that they evolve a probabilistic model instead of a population. Many existing algorithms fall into this category. Analogous to genetic drift in EAs, EDAs also encounter the phenomenon that updates of the probabilistic model not justified by the fitness move the sampling frequencies to the boundary values. This can result in a considerable performance loss. This paper proves the first sharp estimates of the boundary hitting time of the sampling frequency of a neutral bit for several univariate EDAs. For the UMDA that selects $\mu$ best individuals from $\lambda$ offspring each generation, we prove that the expected first iteration when the frequency of the neutral bit leaves the middle range $[\tfrac 14, \tfrac 34]$ and the expected first time it is absorbed in 0 or 1 are both $\Theta(\mu)$. The corresponding hitting times are $\Theta(K^2)$ for the cGA with hypothetical population size $K$. This paper further proves that for PBIL with parameters $\mu$, $\lambda$, and $\rho$, in an expected number of $\Theta(\mu/\rho^2)$ iterations the sampling frequency of a neutral bit leaves the interval $[\Theta(\rho/\mu),1-\Theta(\rho/\mu)]$ and then always the same value is sampled for this bit, that is, the frequency approaches the corresponding boundary value with maximum speed. For the lower bounds implicit in these statements, we also show exponential tail bounds. If a bit is not neutral, but neutral or has a preference for ones, then the lower bounds on the times to reach a low frequency value still hold. An analogous statement holds for bits that are neutral or prefer the value zero.
Leveraging deep graph-based text representation for sentiment polarity applications

Over the last few years, machine learning over graph structures has manifested a significant enhancement in text mining applications such as event detection, opinion mining, and news recommendation. One of the primary challenges in this regard is structuring a graph that encodes and encompasses the features of textual data for the effective machine learning algorithm. Besides, exploration and exploiting of semantic relations is regarded as a principal step in text mining applications. However, most of the traditional text mining methods perform somewhat poor in terms of employing such relations. In this paper, we propose a sentence-level graph-based text representation which includes stop words to consider semantic and term relations. Then, we employ a representation learning approach on the combined graphs of sentences to extract the latent and continuous features of the documents. Eventually, the learned features of the documents are fed into a deep neural network for the sentiment classification task. The experimental results demonstrate that the proposed method substantially outperforms the related sentiment analysis approaches based on several benchmark datasets. Furthermore, our method can be generalized on different datasets without any dependency on pre-trained word embeddings.
Experimental demonstration of sequential quantum random access codes

This work demonstrates that weak measurements can improve quantum random access codes, allowing two sequential decoders to find non-classical correlations with one encoder. Their decoding performance can provide a semi-device-independent bound on some properties of the states and the measurements involved in the code.
Quantum phase estimation for a class of generalized eigenvalue problems

Quantum phase estimation provides a path to quantum computation of solutions to Hermitian eigenvalue problems $Hv=\ensuremath{\lambda}v$, such as those occurring in quantum chemistry. It is natural to ask whether the same technique can be applied to generalized eigenvalue problems $Av=\ensuremath{\lambda}Bv$, which arise in many areas of science and engineering. We answer this question affirmatively. A restricted class of generalized eigenvalue problems could be solved as efficiently as standard eigenvalue problems. A paradigmatic example is provided by Sturm-Liouville problems. Another example comes from linear ideal magnetohydrodynamics, where phase estimation could be used to determine the stability of magnetically confined plasmas in fusion reactors.
$\mu$RAI: Securing Embedded Systems with Return Address Integrity

Embedded systems are deployed in security critical environments and have become a prominent target for remote attacks.Microcontroller-based systems (MCUS) are particularly vulnerable due to a combination of limited resources and low level programming which leads to bugs.Since MCUS are often a part of larger systems, vulnerabilities may jeopardize not just the security of the device itself but that of other systems as well.For example, exploiting a WiFi System on Chip (SoC) allows an attacker to hijack the smart phone's application processor.Control-flow hijacking targeting the backward edge (e.g., Return-Oriented Programming-ROP) remains a threat for MCUS.Current defenses are either susceptible to ROP-style attacks or require special hardware such as a Trusted Execution Environment (TEE) that is not commonly available on MCUS.We present µRAI 1 , a compiler-based mitigation to prevent control-flow hijacking attacks targeting backward edges by enforcing the Return Address Integrity (RAI) property on MCUS.µRAI does not require any additional hardware such as TEE, making it applicable to the wide majority of MCUS.To achieve this, µRAI introduces a technique that moves return addresses from writable memory, to readable and executable memory.It repurposes a single general purpose register that is never spilled, and uses it to resolve the correct return location.We evaluate against the different control-flow hijacking attacks scenarios targeting return addresses (e.g., arbitrary write), and demonstrate how µRAI prevents them all.Moreover, our evaluation shows that µRAI enforces its protection with negligible overhead.
Generalized Sampling on Graphs With Subspace and Smoothness Priors

We propose a framework for generalized sampling of graph signals that parallels sampling in shift-invariant (SI) subspaces. This framework allows for arbitrary input signals, which are not constrained to be bandlimited. Furthermore, the sampling and reconstruction filters may be different. We present design methods of the correction filter that compensate for these differences and lead to closed form expressions in the graph frequency domain. In this study, we consider two priors on graph signals: The first is a subspace prior, where the signal is assumed to lie in a periodic graph spectrum (PGS) subspace. The PGS subspace is proposed as a counterpart of the SI subspace used in standard sampling theory. The second is a smoothness prior that imposes a smoothness requirement on the graph signal. We suggest the use of recovery techniques for when the recovery filter can be optimized and under a setting in which a predefined filter must be used. Sampling is performed in the graph frequency domain, which is a counterpart of "sampling by modulation" used in SI subspaces. We compare our approach with existing sampling techniques on graph signal processing. The effectiveness of the proposed generalized sampling approach is validated numerically through several experiments.
Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images

In recent years, hyperspectral imaging (HSI) has been shown as a promising imaging modality to assist pathologists in the diagnosis of histological samples. In this work, we present the use of HSI for discriminating between normal and tumor breast cancer cells. Our customized HSI system includes a hyperspectral (HS) push-broom camera, which is attached to a standard microscope, and home-made software system for the control of image acquisition. Our HS microscopic system works in the visible and near-infrared (VNIR) spectral range (400 - 1000 nm). Using this system, 112 HS images were captured from histologic samples of human patients using 20× magnification. Cell-level annotations were made by an expert pathologist in digitized slides and were then registered with the HS images. A deep learning neural network was developed for the HS image classification, which consists of nine 2D convolutional layers. Different experiments were designed to split the data into training, validation and testing sets. In all experiments, the training and the testing set correspond to independent patients. The results show an area under the curve (AUCs) of more than 0.89 for all the experiments. The combination of HSI and deep learning techniques can provide a useful tool to aid pathologists in the automatic detection of cancer cells on digitized pathologic images.
MIE: A Medical Information Extractor towards Medical Dialogues

Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.
Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques

The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.
MCMT-GAN: Multi-Task Coherent Modality Transferable GAN for 3D Brain Image Synthesis

The ability to synthesize multi-modality data is highly desirable for many computer-aided medical applications, e.g. clinical diagnosis and neuroscience research, since rich imaging cohorts offer diverse and complementary information unraveling human tissues. However, collecting acquisitions can be limited by adversary factors such as patient discomfort, expensive cost and scanner unavailability. In this paper, we propose a multi-task coherent modality transferable GAN (MCMT-GAN) to address this issue for brain MRI synthesis in an unsupervised manner. Through combining the bidirectional adversarial loss, cycle-consistency loss, domain adapted loss and manifold regularization in a volumetric space, MCMT-GAN is robust for multi-modality brain image synthesis with visually high fidelity. In addition, we complement discriminators collaboratively working with segmentors which ensure the usefulness of our results to segmentation task. Experiments evaluated on various cross-modality synthesis show that our method produces visually impressive results with substitutability for clinical post-processing and also exceeds the state-of-the-art methods.
Breaking adiabatic quantum control with deep learning

In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speed up by pulse control. Here, we select single-component control of qubits, resembling the ubiquitous two-level Landau-Zener problem for gate operation. We aim at obtaining fast and robust digital pulses by combining STA and DRL algorithm. In particular, we find that DRL leads to robust digital quantum control with operation time bounded by quantum speed limits dictated by STA. In addition, we demonstrate that robustness against systematic errors can be achieved by DRL without any input from STA. Our results introduce a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.
Overparameterized neural networks implement associative memory

Significance Development of computational models of memory is a subject of long-standing interest at the intersection of machine learning and neuroscience. Our main finding is that overparameterized neural networks trained using standard optimization methods provide a simple mechanism for implementing associative memory. Remarkably, this mechanism allows for the storage and retrieval of sequences of examples. This finding also sheds light on inductive biases in overparameterized networks: while there are many functions that can achieve zero training loss in the overparameterized regime, our result shows that increasing depth and width in neural networks leads to maps that are more contractive around training examples, thereby allowing for storage and retrieval of more training examples.
Pseudo-dimension of quantum circuits

Abstract We characterize the expressive power of quantum circuits with the pseudo-dimension, a measure of complexity for probabilistic concept classes. We prove pseudo-dimension bounds on the output probability distributions of quantum circuits; the upper bounds are polynomial in circuit depth and number of gates. Using these bounds, we exhibit a class of circuit output states out of which at least one has exponential gate complexity of state preparation, and moreover demonstrate that quantum circuits of known polynomial size and depth are PAC-learnable.
Measurement-device-independent quantum key distribution of multiple degrees of freedom of a single photon

Measurement-device-independent quantum key distribution (MDI-QKD) provides us a powerful approach to resist all attacks at detection side. Besides the unconditional security, people also seek for high key generation rate, but MDI-QKD has relatively low key generation rate. In this paper, we provide an efficient approach to increase the key generation rate of MDI-QKD by adopting multiple degrees of freedom (DOFs) of single photons to generate keys. Compared with other high-dimension MDI-QKD protocols encoding in one DOF, our protocol is more flexible, for our protocol generating keys in independent subsystems and the detection failure or error in a DOF not affecting the information encoding in other DOFs. Based on above features, our MDI-QKD protocol may have potential application in future quantum communication field.
Adversarial multi-source transfer learning in healthcare: Application to glucose prediction for diabetic people

Background and objectives: Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity through the use of transfer learning. Methods: To improve the quality of the transfer between multiple sources of data, we propose a multi-source adversarial transfer learning framework that enables the learning of a feature representation that is similar across the sources, and thus more general and more easily transferable. We apply this idea to glucose forecasting for diabetic people using a fully convolutional neural network. The evaluation is done by exploring various transfer scenarios with three datasets characterized by their high inter and intra variability. Results: While transferring knowledge is beneficial in general, we show that the statistical and clinical accuracies can be further improved by using of the adversarial training methodology, surpassing the current state-of-the-art results. In particular, it shines when using data from different datasets, or when there is too little data in an intra-dataset situation. To understand the behavior of the models, we analyze the learnt feature representations and propose a new metric in this regard. Contrary to a standard transfer, the adversarial transfer does not discriminate the patients and datasets, helping the learning of a more general feature representation. Conclusion: The adversarial training framework improves the learning of a general feature representation in a multi-source environment, enhancing the knowledge transfer to an unseen target. The proposed method can help improve the efficiency of data shared by different health actors in the training of deep models.
Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification

Computer-aided diagnosis (CAD) is becoming a prominent approach to assist clinicians spanning across multiple fields.These automated systems take advantage of various computer vision (CV) procedures, as well as artificial intelligence (AI) techniques, to formulate a diagnosis of a given image, e.g., computed tomography and ultrasound.Advances in both areas (CV and AI) are enabling ever increasing performances of CAD systems, which can ultimately avoid performing invasive procedures such as fine-needle aspiration.In this study, a novel end-to-end knowledge-driven classification framework is presented.The system focuses on multimodal data generated by thyroid ultrasonography, and acts as a CAD system by providing a thyroid nodule classification into the benign and malignant categories.Specifically, the proposed system leverages cues provided by an ensemble of experts to guide the learning phase of a densely connected convolutional network (DenseNet).The ensemble is composed by various networks pretrained on ImageNet, including AlexNet, ResNet, VGG, and others.The previously computed multimodal feature parameters are used to create ultrasonography domain experts via transfer learning, decreasing, moreover, the number of samples required for training.To validate the proposed method, extensive experiments were performed, providing detailed performances for both the experts ensemble and the knowledge-driven DenseNet.As demonstrated by the results, the proposed system achieves relevant performances in terms of qualitative metrics for the thyroid nodule classification task, thus resulting in a great asset when formulating a diagnosis.
An NLP-Based Architecture for the Autocompletion of Partial Domain Models

Domain models capture the key concepts and relationships of a business domain. Typically, domain models are manually defined by software designers in the initial phases of a software development cycle, based on their interactions with the client and their own domain expertise. Given the key role of domain models in the quality of the final system, it is important that they properly reflect the reality of the business. To facilitate the definition of domain models and improve their quality, we propose to move towards a more assisted domain modeling building process where an NLP-based assistant will provide autocomplete suggestions for the partial model under construction based on the automatic analysis of the textual information available for the project (contextual knowledge) and/or its related business domain (general knowledge). The process will also take into account the feedback collected from the designer’s interaction with the assistant. We have developed a proof-of-concept tool and have performed a preliminary evaluation that shows promising results.
A Scoping Review of Integrated Blockchain-Cloud (BcC) Architecture for Healthcare: Applications, Challenges and Solutions

Blockchain is a disruptive technology for shaping the next era of a healthcare system striving for efficient and effective patient care. This is thanks to its peer-to-peer, secure, and transparent characteristics. On the other hand, cloud computing made its way into the healthcare system thanks to its elasticity and cost-efficiency nature. However, cloud-based systems fail to provide a secured and private patient-centric cohesive view to multiple healthcare stakeholders. In this situation, blockchain provides solutions to address security and privacy concerns of the cloud because of its decentralization feature combined with data security and privacy, while cloud provides solutions to the blockchain scalability and efficiency challenges. Therefore a novel paradigm of blockchain-cloud integration (BcC) emerges for the domain of healthcare. In this paper, we provide an in-depth analysis of the BcC integration for the healthcare system to give the readers the motivations behind the emergence of this new paradigm, introduce a classification of existing architectures and their applications for better healthcare. We then review the development platforms and services and highlight the research challenges for the integrated BcC architecture, possible solutions, and future research directions. The results of this paper will be useful for the healthcare industry to design and develop a data management system for better patient care.
Milvus

Recently, there has been a pressing need to manage high-dimensional vector data in data science and AI applications. This trend is fueled by the proliferation of unstructured data and machine learning (ML), where ML models usually transform unstructured data into feature vectors for data analytics, e.g., product recommendation. Existing systems and algorithms for managing vector data have two limitations: (1) They incur serious performance issue when handling large-scale and dynamic vector data; and (2) They provide limited functionalities that cannot meet the requirements of versatile applications.
Zero-shot Event Extraction via Transfer Learning: Challenges and Insights

Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies.In this work, we explore the possibility of zeroshot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g."A city was attacked" entails "There is an attack"), exploiting pretrained TE/QA models for direct transfer.On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain.To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions 1 .