Reviewerly/exHarmony huggingface dataset:
REDUCT: Keep it Close, Keep it Cool! : Efficient Scaling of DNN Inference on Multi-core CPUs with Near-Cache Compute

Deep Neural Networks (DNN) are used in a variety of applications and services. With the evolving nature of DNNs, the race to build optimal hardware (both in datacenter and edge) continues. General purpose multi-core CPUs offer unique attractive advantages for DNN inference at both datacenter [60] and edge [71]. Most of the CPU pipeline design complexity is targeted towards optimizing general-purpose single thread performance, and is overkill for relatively simpler, but still hugely important, data parallel DNN inference workloads. Addressing this disparity efficiently can enable both raw performance scaling and overall performance/Watt improvements for multi-core CPU DNN inference.We present REDUCT, where we build innovative solutions that bypass traditional CPU resources which impact DNN inference power and limit its performance. Fundamentally, REDUCT’s "Keep it close" policy enables consecutive pieces of work to be executed close to each other. REDUCT enables instruction delivery/decode close to execution and instruction execution close to data. Simple ISA extensions encode the fixed-iteration count loop-y workload behavior enabling an effective bypass of many power-hungry front-end stages of the wide Out-of-Order (OoO) CPU pipeline. Per core performance scales efficiently by distributing light-weight tensor compute near all caches in a multi-level cache hierarchy. This maximizes the cumulative utilization of the existing architectural bandwidth resources in the system and minimizes movement of data.Across a number of DNN models, REDUCT achieves a 2.3× increase in convolution performance/Watt with a 2× to 3.94× scaling in raw performance. Similarly, REDUCT achieves a 1.8× increase in inner-product performance/Watt with 2.8× scaling in performance. REDUCT performance/power scaling is achieved with no increase to cache capacity or bandwidth and a mere 2.63% increase in area. Crucially, REDUCT operates entirely within the CPU programming and memory model, simplifying software development, while achieving performance similar to or better than state-of-the-art Domain Specific Accelerators (DSA) for DNN inference, providing fresh design choices in the AI era.
Quantum Entanglement Among Multiple Memories for Continuous Variables

Abstract Quantum network is constituted by quantum channels and quantum nodes. The interaction between non‐classical optical modes and quantum nodes, as well as quantum entanglement among multiple distant quantum nodes are the building blocks of quantum network, which enable to achieve a plethora of quantum information protocols, such as distributed quantum computation, quantum state transfer across quantum nodes, and quantum clock network. On one hand, the multipartite non‐classical states of optical modes, which can directly interact with atomic ensembles, are required for the practical applications of quantum network. On the other hand, a crucial goal of quantum network is to unconditionally generate and on‐demand store and retrieve multipartite entangled states in atomic ensembles. This paper presents an up‐to‐date review on recent developments in these areas: multipartite continuous‐variable polarization entangled optical modes have been created by transforming quantum state from quadrature into polarization components; and a scalable quantum network with deterministic entanglement among multiple quantum memories has been constructed by transferring spatially separated entangled optical modes into atomic ensembles.
Design of Multimedia Vocal Music Education Data Integration System Based on Adaptive Genetic Algorithm

In order to solve the problems of low accuracy of data integration results, low integration efficiency, and easy confusion between different types of data in traditional methods, a multimedia vocal education data integration system based on adaptive genetic algorithm was designed. Specifically, the designed system is divided into three parts: data source management module, system administrator module, and database management module. The synchronized multimedia vocal education data are first processed by the synchronous multimedia vocal education data processing and then integrated by an adaptive genetic algorithm. The experimental results show that the longest data transmission time of the system is 2.3 s, which is much lower than that of the traditional method, and the accuracy of the integration result is higher, and the probability of data integration confusion is lower, which all indicate that the designed system has better application performance.
Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging

Dementia is a common term for memory loss, speech, problem-solving, and other cognitive skills that are serious enough to interfere with everyday life, and Alzheimer's is the leading cause of dementia. Alzheimer's disease is presumed to develop 20 years or more before symptoms occur, with degenerative changes that are unapparent to the person affected. The deep learning approach for early detection and Alzheimer's disease classification has recently gained significant attention. This study proposed disease detection trained by utilizing the YOLO v3 algorithm that aims to detect Alzheimer's disease based solely on Magnetic Resonance Imaging (MRI). Pascal VOC format and LabelImg tool are used for annotating the datasets, categorizing the image as non-demented and mild-demented. Model 4 was used in the system having 98.617% training accuracy, 98.8207% validation accuracy, and mAP of 96.17%. To test the accuracy of the used model, images of MRI scans are presented and it recorded 80% testing accuracy.
Variational Quantum Algorithms for the Steady States of Open Quantum Systems

Solving problems related to open quantum systems has attracted many interests. Here, we propose a variational quantum algorithm to find the steady state of open quantum systems. In this algorithm, we employ parameterized quantum circuits to prepare the purification of the steady state and define the cost function based on the Lindblad master equation, which can be efficiently evaluated with quantum circuits. Then we optimize the parameters of the quantum circuit to find the steady state. Numerical simulations are performed on the one-dimensional transverses field Ising model with dissipative channels. The result showed that the fidelity between the optimal mixed state and the true steady state is over 99\%. This algorithm is derived from the natural idea of expressing mixed states with purification and provides a reference for the study of open quantum systems.
Evolving Neural Networks for Text Classification using Genetic Algorithm-based Approaches

Convolutional Neural Networks (CNNs) have been well-known for their promising performance in text classification and sentiment analysis because they can preserve the 1D spatial orientation of a document, where the sequence of words is essential. However, designing the network architecture of CNNs is by no means an easy task, since it requires domain knowledge from both the deep CNN and text classification areas, which are often not available and can increase operating costs for anyone wishing to implement this method. Furthermore, such domain knowledge is often different in different text classification problems. To resolve these issues, this paper proposes the use of Genetic Algorithm to automatically search for the optimal network architecture without requiring any intervention from experts. The proposed approach is applied on the IMDB dataset, and the experimental results show that it achieves competitive performance with the current state-of-the-art and manually-designed approaches in terms of accuracy, and also it requires only a few hours of training time.
Variational quantum algorithms for trace norms and their applications

The trace norm of matrices plays an important role in quantum information and quantum computing. How to quantify it in today's noisy intermediate scale quantum (NISQ) devices is a crucial task for information processing. In this paper, we present three variational quantum algorithms on NISQ devices to estimate the trace norms corresponding to different situations. Compared with the previous methods, our means greatly reduce the requirement for quantum resources. Numerical experiments are provided to illustrate the effectiveness of our algorithms.
Learning discriminative motion feature for enhancing multi-modal action recognition

Video action recognition is an important topic in computer vision tasks. Most of the existing methods use CNN-based models, and multiple modalities of image features are captured from the videos, such as static frames, dynamic images, and optical flow features. However, these mainstream features contain much static information including object and background information, where the motion information of the action itself is not distinguished and strengthened. In this work, a new kind of motion feature is proposed without static information for video action recognition. We propose a quantization of motion network based on the bag-of-feature method to learn significant and discriminative motion features. In the learned feature map, the object and background information is filtered out, even if the background is moving in the video. Therefore, the motion feature is complementary to the static image feature and the static information in the dynamic image and optical flow. A multi-stream classifier is built with the proposed motion feature and other features, and the performance of action recognition is enhanced comparing to other state-of-the-art methods.
Better Fine-Tuning with Extracted Important Sentences for Abstractive Summarization

Recent work in abstractive text summarization using pre-trained transformers has achieved great results. Much of the work has been done on the model architectures and designing pre-training objectives. Models used for NLP tasks have grown bigger and increasing the model’s size doesn’t always improve the performance of the task. In this work we improve the results of an existing model by incorporating a new fine-tuning strategy which closely resembles how a human would summarize by a piece of text i.e. by focusing on important fragments of the text. We used PEGASUS model to work on our fine-tuning strategy. We fine-tune the model with varying ratios of important sentences in a phased manner and achieve improved ROUGE scores with an additional few thousand steps of fine-tuning. We fine-tune the model on AESLC and CNN/DailyMail dataset and achieve better ROUGE scores on both the datasets. With our proposed fine-tuning method, we get state-of-the-art results on the AESLC dataset. We also achieve better performance when fine-tuning with a limited number of examples with our fine-tuning method on AESLC dataset.
Someone Is Wrong on the Internet: Is There an Obligation to Correct False and Oppressive Speech on Social Media?

This paper examines the extent of our obligations with respect to correcting false and oppressive speech on social media. Many of us have the experience of feeling a very strong obligation in this regard, and there are important strands within the philosophical literature that suggest such a strong obligation. There are also significant discussions of very serious problems for counterspeech, but there is surprisingly little discussion in the philosophical literature of counterspeech obligations on social media. Moreover, social media communication is very unlike the paradigm cases of communication that dominate the philosophical literature on counterspeech. I examine some of these differences and argue that there are several special features of social media communication that strongly mitigate any individual obligation to object with counterspeech. In particular, counterspeech on social media may be especially likely to backfire in extremely damaging ways. Finally, I close by briefly pointing to what may be more productive methods for combatting false and oppressive speech on social media: institutional and group approaches. The obligations that so many individuals feel may be leading them astray.
Simultaneous magnification of subtle motions and color variations in videos using Riesz pyramids

Videos often contain subtle motions and color variations that cannot be easily observed. Examples include, for instance, head motion and changes in skin face color due to blood flow controlled by the heart pumping rhythm. A few techniques have been developed to magnify these subtle signals. However, previous techniques were targeted specifically towards magnification of either motion or color variations. Trying to magnify both aspects applying two of these techniques in sequence does not produce good results. We present a method for magnifying subtle motions and color variations in videos, which can be performed either separately or simultaneously. Our approach is based on the Riesz pyramid, which was previously used only for motion magnification. Besides modifying the local phases of the coefficients of this pyramid, we show how altering its local amplitudes and its residue can be used to magnify intensity/color variations. We demonstrate the effectiveness of our technique in multiple videos by magnifying both subtle motion and color variations simultaneously.
Multiclass Multiple-Instance Learning for Predicting Precursors to Aviation Safety Events

In recent years, there has been a rapid growth in applying machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen before the adverse event itself. Therefore, precursor mining provides many benefits, including the identification of factors relevant to the occurrence of an adverse event and their signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning framework, a weakly supervised learning task, combined with a carefully designed binary classifier leveraging a Multi-Head Convolutional Neural Network–Recurrent Neural Network (MHCNN-RNN) architecture. Multiclass classifiers are then created and compared, enabling the prediction of different adverse events for any given flight by combining binary classifiers, and by modifying the MHCNN-RNN to handle multiple outputs. Results obtained showed that the multiple binary classifiers perform better and are able to accurately forecast high-speed and high-path-angle events during the approach phase. Multiple binary classifiers are also capable of determining the aircraft parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.
Vision-Based Sense and Avoid with Monocular Vision and Real-Time Object Detection for UAVs

The use of unmanned aerial vehicles (UAVs) or drones have become ubiquitous in the recent years. Collision avoidance is a critical component of path planning, allowing multi-agent networks of cooperative UAVs to work together towards common objectives while avoiding each other. We implemented, integrated and evaluated the effectiveness of using a low cost, wide angle monocular camera with real-time computer vision algorithms to detect and track other UAVs in local airspace and perform collision avoidance in the event of a communications degradation or the presence of non-cooperative adversaries, through experimental flight tests where the UAVs were set on collision courses.
Noise-Removal from Spectrally-Similar Signals Using Reservoir Computing for MCG Monitoring

Continuous low-rate monitoring is an important IoT application, which requires high-fidelity in observing signals with low frequency. However, most sensors exhibit noise that is inversely-proportional to spectral frequency (1/f noise). Because both the relevant signal and noise share the same spectral properties, standard linear filtering techniques cannot be used. We are looking into a special application for remote healthcare of the magnetic field sensing of cardiac activity, magnetocardiography (MCG). For such an application, we need to develop a noise separation method, that is also resource-efficient. Previously, we demonstrated AI-based removal of 1/f noise in MCG by a convolutional neural network coupled with gated recurrent units. However, it needs a large amount of data for training, requiring significant training time and computational power. In this work, we employ reservoir computing (RC) for noise-removal, while being conservative in computing resources.
Dissipative polynomials

Limited precision floating point computer implementations of large polynomial arithmetic expressions are nonlinear and dissipative. They are not reversible (irreversible, lack conservation), lose information, and so are robust to perturbations (anti-fragile) and resilient to fluctuations. This gives a largely stable locally flat evolutionary neutral fitness search landscape. Thus even with a large number of test cases, both large and small changes deep within software typically have no effect and are invisible externally. Shallow mutations are easier to detect but their RMS error need not be simple.
A comparative study of machine learning methods for ordinal classification with absolute and relative information

The performance of an ordinal classifier is highly affected by the amount of absolute information (labelled data) available for training. In order to make up for a lack of sufficient absolute information, an effective way out is to consider additional types of information. In this work, we focus on ordinal classification problems that are provided with additional relative information. We augment several classical machine learning methods by considering both absolute and relative information as constraints in the corresponding optimization problems. We compare these augmented methods on popular benchmark datasets. The experimental results show the effectivenesses of these methods for combining absolute and relative information.
Multi-view feature fusion for person re-identification

Person re-identification (ReID) suffers from camera view variants. Existing works, which typically learn a feature for each image, share a limitation that the learned features are single-view: each feature only contains information in one camera view. Thus, view bias occurs when matching pedestrians across camera views. In this paper, we seek to mitigate the view bias by generating multi-view features (fusion of features from a fixed number of cameras). To this end, we define the complementary-view features (complementary features to generate multi-view features with single-view features) and perform in-depth analysis. Based on this insight, we alleviate the view bias in testing and training, respectively. In testing, we present Multi-view Message Passing (MVMP), which generates multi-view features by aggregating single-view features from the neighborhood. In training, we propose Multi-view Feature Fusion Network (MFFN), which involves the single-view feature extractor and the complementary-view feature aggregator. MFFN makes the network sensitive to view-specific cues by adding constraints on multi-view features rather than single-view features. In addition, MVMP and MFFN have two key advantages: (1) They are parameter-free. (2) They can be applied to any Convolutional Neural Networks (CNNs) readily without extra supervision. Extensive experiments are conducted to validate the superiority of our method for person ReID over state-of-the-art methods on four benchmark datasets (Market-1501, DukeMTMC-reID, CUHK03, and MSMT17). The code is available at https://github.com/Yinsongxu/MVMP_MFFN.
Considerations for evaluating thermodynamic properties with hybrid quantum-classical computing work flows

Quantum chemistry applications on quantum computers currently rely heavily on the variational quantum eigensolver (VQE) algorithm. This hybrid quantum-classical algorithm aims at finding ground-state solutions of molecular systems based on the variational principle. VQE calculations can be systematically implemented for perturbations to each molecular degree of freedom, generating a Born-Oppenheimer potential-energy surface (PES) for the molecule. The PES can then be used to derive thermodynamic properties, which are often desirable for applications in chemical engineering and materials design. It is clear from this process that quantum chemistry applications contain a substantial classical computing component in addition to steps that can be performed using a quantum computer. In order to design efficient work flows that take full advantage of each hardware type, it is critical to consider the entire process so that the high-accuracy electronic energies possible from quantum computing are not squandered in the process of calculating thermodynamic properties. We present a summary of the hybrid quantum-classical work flow to compute thermodynamic properties. This work flow contains many options that can significantly affect the efficiency and the accuracy of the results, including classical optimizer attributes, number of ansatz repetitions, and how the vibrational Schr\"odinger equation is solved to determine vibrational modes. We also analyze the effects of these options by employing robust statistics along with simulations and experiments on actual quantum hardware. We show that, through careful selection of work flow options, nearly order-of-magnitude increases in accuracy are possible at equivalent computing time.
EAT-NAS: elastic architecture transfer for accelerating large-scale neural architecture search

Neural architecture search (NAS) methods have been proposed to relieve human experts from tedious architecture engineering. However, most current methods are constrained in small-scale search owing to the issue of huge computational resource consumption. Meanwhile, the direct application of architectures searched on small datasets to large datasets often bears no performance guarantee due to the discrepancy between different datasets. This limitation impedes the wide use of NAS on large-scale tasks. To overcome this obstacle, we propose an elastic architecture transfer mechanism for accelerating large-scale NAS (EAT-NAS). In our implementations, the architectures are first searched on a small dataset, e.g., CIFAR-10. The best one is chosen as the basic architecture. The search process on a large dataset, e.g., ImageNet, is initialized with the basic architecture as the seed. The large-scale search process is accelerated with the help of the basic architecture. We propose not only a NAS method but also a mechanism for architecture-level transfer learning. In our experiments, we obtain two final models EATNet-A and EATNet-B, which achieve competitive accuracies of 75.5% and 75.6%, respectively, on ImageNet. Both the models also surpass the models searched from scratch on ImageNet under the same settings. For the computational cost, EAT-NAS takes only fewer than 5 days using 8 TITAN X GPUs, which is significantly less than the computational consumption of the state-of-the-art large-scale NAS methods.