Reviewerly/exHarmony huggingface dataset:
Proactive autoscaling for edge computing systems with kubernetes

With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing applications do not utilize heterogeneous resources of edge systems efficiently, leaving scope for performance improvement. In this work, we propose a Proactive Pod Autoscaler (PPA) for edge computing applications on Kubernetes. The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications up and down correspondingly. The PPA is optimized and evaluated on an example CPU-intensive edge computing application further. It can be concluded that the proposed PPA outperforms the default pod autoscaler of Kubernetes on both efficiency of resource utilization and application performance. The article also highlights future possible improvements on the proposed PPA.
Backpropagation Neural Tree

We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological properties, BNeuralT is a single neuron neural tree model with its internal sub-trees resembling dendritic nonlinearities. BNeuralT algorithm produces an ad hoc neural tree which is trained using a stochastic gradient descent optimizer like gradient descent (GD), momentum GD, Nesterov accelerated GD, Adagrad, RMSprop, or Adam. BNeuralT training has two phases, each computed in a depth-first search manner: the forward pass computes neural tree's output in a post-order traversal, while the error backpropagation during the backward pass is performed recursively in a pre-order traversal. A BNeuralT model can be considered a minimal subset of a neural network (NN), meaning it is a "thinned" NN whose complexity is lower than an ordinary NN. Our algorithm produces high-performing and parsimonious models balancing the complexity with descriptive ability on a wide variety of machine learning problems: classification, regression, and pattern recognition.
Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features

Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.
Fast Bayesian Tomography of a Two-Qubit Gate Set in Silicon

Benchmarking and characterising quantum states and logic gates is essential in the development of devices for quantum computing. We introduce a Bayesian approach to self-consistent process tomography, called fast Bayesian tomography (FBT), and experimentally demonstrate its performance in characterising a two-qubit gate set on a silicon-based spin qubit device. FBT is built on an adaptive self-consistent linearisation that is robust to model approximation errors. Our method offers several advantages over other self-consistent tomographic methods. Most notably, FBT can leverage prior information from randomised benchmarking (or other characterisation measurements), and can be performed in real time, providing continuously updated estimates of full process matrices while data is acquired.
Remarks on fixed point assertions in digital topology

Several recent papers in digital topology have sought to obtain fixed point results by mimicking the use of tools from classical topology, such as complete metric spaces and homotopy invariant fixed point theory. We show that some of the published assertions based on these tools are incorrect or trivial; we offer improvements on others.
Fine tuning attribute weighted naive Bayes

Naive Bayes (NB) is one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy. However, both the unrealistic attribute conditional independence assumption and the unreliable conditional probability estimation limit its performance. Of numerous improved approaches, attribute weighting only focuses on alleviating the unrealistic attribute conditional independence assumption, while fine tuning devotes all the efforts to finding a more reliable conditional probability estimation. In this study, we argue that both of them are equally important to enhance the performance of NB and propose a novel model called fine tuned attribute weighted NB (FTAWNB) by combining fine tuning with attribute weighting into a uniform framework. In FTAWNB, we first exploit correlation-based attribute weighting to initialize the conditional probabilities, then for each misclassified training instance, the conditional probabilities are fine tuned iteratively to make them more reliable, and the fine tuning process will stop once the training classification accuracy no longer improves. Extensive experimental results show that FTAWNB significantly outperforms all the other existing state-of-the-art competitors.
A 65nm Systolic Neural CPU Processor for Combined Deep Learning and General-Purpose Computing with 95% PE Utilization, High Data Locality and Enhanced End-to-End Performance

Despite recent progress on building highly efficient deep neural network (DNN) accelerators, few works have targeted improving the end-to-end performance of deep-learning tasks, where inter-layer pre/post-processing, data alignment and data movement across memory and processing units often dominate the execution time. An improvement to the end-to-end computation requires cohesive cooperation between the accelerator and the CPU with highly efficient data flow management. Figure 15.2.1 shows the most commonly used heterogeneous architecture, containing a CPU core and an accelerator with data communication managed by a DMA engine. However, there remain the challenges of low utilization of PE cores and large latency due to the CPU workload and data movement across processing cores [1]–[4]. As shown in Fig. 15.2.1, in an end-to-end deep learning task, the accelerator is often utilized at only 30-50% with the rest of time waiting for CPU processing and data movement between the CPU and accelerator cores. Some prior works have considered data compression, reduction of data movement or improvement of memory bandwidth. For instance, an accelerator coherency port (ACP) was designed to request data directly from the last level cache of the CPU instead of using the DMA engine to improve the efficiency of data transfer [3], [5]. In this work, we propose a new architecture, a systolic neural CPU (SNCPU), which fuses the operation of a conventional CPU and a systolic CNN accelerator in a single core. The contributions of this work include: 1) The proposed SNCPU architecture can be flexibly reconfigured into a multi-core RISC-V CPU or a systolic CNN accelerator, leading to PE utilization of over 95% for end-to-end operation; 2) with an overhead of less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , the CNN accelerator can be reconfigured into a 10-core RISC-V CPU to improve throughput significantly compared with a conventional heterogeneous architecture having a CPU and an accelerator; 3) with a special bi-directional dataflow, expensive data movement for inter-layer pre/post-processing across cores can be avoided; 4) we demonstrate the SNCPU through a 65nm test chip with 39-to-64 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> latency improvement and 0.65-to-1.8TOPS/W energy efficiency on end-to-end image-classification tasks.
Near-optimal sparse allreduce for distributed deep learning

Communication overhead is one of the major obstacles to train large deep learning models at scale. Gradient sparsification is a promising technique to reduce the communication volume. However, it is very challenging to obtain real performance improvement because of (1) the difficulty of achieving an scalable and efficient sparse allreduce algorithm and (2) the sparsification overhead. This paper proposes O$k$-Top$k$, a scheme for distributed training with sparse gradients. O$k$-Top$k$ integrates a novel sparse allreduce algorithm (less than 6$k$ communication volume which is asymptotically optimal) with the decentralized parallel Stochastic Gradient Descent (SGD) optimizer, and its convergence is proved. To reduce the sparsification overhead, O$k$-Top$k$ efficiently selects the top-$k$ gradient values according to an estimated threshold. Evaluations are conducted on the Piz Daint supercomputer with neural network models from different deep learning domains. Empirical results show that O$k$-Top$k$ achieves similar model accuracy to dense allreduce. Compared with the optimized dense and the state-of-the-art sparse allreduces, O$k$-Top$k$ is more scalable and significantly improves training throughput (e.g., 3.29x-12.95x improvement for BERT on 256 GPUs).
Deep Learning Approaches on Image Captioning: A Review

Image captioning is a research area of immense importance, aiming to generate natural language descriptions for visual content in the form of still images. The advent of deep learning and more recently vision-language pre-training techniques has revolutionized the field, leading to more sophisticated methods and improved performance. In this survey paper, we provide a structured review of deep learning methods in image captioning by presenting a comprehensive taxonomy and discussing each method category in detail. Additionally, we examine the datasets commonly employed in image captioning research, as well as the evaluation metrics used to assess the performance of different captioning models. We address the challenges faced in this field by emphasizing issues such as object hallucination, missing context, illumination conditions, contextual understanding, and referring expressions. We rank different deep learning methods' performance according to widely used evaluation metrics, giving insight into the current state of the art. Furthermore, we identify several potential future directions for research in this area, which include tackling the information misalignment problem between image and text modalities, mitigating dataset bias, incorporating vision-language pre-training methods to enhance caption generation, and developing improved evaluation tools to accurately measure the quality of image captions.
Domain Adaptation for Underwater Image Enhancement via Content and Style Separation

Underwater image suffer from color cast, low contrast and hazy effect, which degraded the high-level vision application. Recent learning-based methods demonstrate astonishing performance on underwater image enhancement, however, most of these works use synthetic pair data for supervised learning and ignore the domain gap to real-world data. Although some work leverage transfer learning and domain adaptation to alleviate this problem, they target to minimize the latent discrepancy of synthesis and real-world data, and make the latent space hard to interpret and cannot manipulate. To solve this problem, we propose a domain adaptation framework for underwater image enhancement via content and style separation, we aim to separate encoded feature into content and style latent and distinguish style latent from different domains, and process domain adaptation and image enhancement in latent space. Our model provide a user interact interface to adjust different enhanced level for continuous change by latent manipulation. Experiment on various public real-world underwater benchmarks demonstrate that the proposed framework is capable to perform domain adaptation for underwater image enhancement and outperform various state-of-the-art underwater image enhancement algorithms in quantity and quality. The model and source code will be available at https://github.com/fordevoted/UIESS.
Improved Optimal Testing Results from Global Hypercontractivity

The problem of testing low-degree polynomials has received significant attention over the years due to its importance in theoretical computer science, and in particular in complexity theory. The problem is specified by three parameters: field size $q$, degree $d$ and proximity parameter $\delta$, and the goal is to design a tester making as few as possible queries to a given function, which is able to distinguish between the case the given function has degree at most $d$, and the case the given function is $\delta$-far from any degree $d$ function. A tester is called optimal if it makes $O(q^d+1/\delta)$ queries (which are known to be necessary). For the field of size $q$, the natural $t$-flat tester was shown to be optimal first by Bhattacharyya et al. for $q=2$, and later by Haramaty et al. for all prime powers $q$. The dependency on the field size, however, is a tower-type function. We improve the results above, showing that the dependency on the field size is polynomial. Our approach also applies in the more general setting of lifted affine invariant codes, and is based on studying the structure of the collection of erroneous subspaces. i.e. subspaces $A$ such that $f|_{A}$ has degree greater than $d$. Towards this end, we observe that these sets are poorly expanding in the affine version of the Grassmann graph and use that to establish structural results on them via global hypercontractivity. We then use this structure to perform local correction on $f$.
Narain CFTs and error-correcting codes on finite fields

A bstract We construct Narain CFTs from self-dual codes on the finite field F p through even self-dual lattices for any prime p &gt; 2. Using this correspondence, we can relate the spectral gap and the partition function of the CFT to the error correction capability and the extended enumerator polynomial of the code. In particular, we calculate specific spectral gaps of CFTs constructed from codes and compare them with the largest spectral gap among all Narain CFTs.
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved. In this paper, we attempt to solve this problem from the perspective of network architecture design and training data synthesis. Specifically, for the network architecture design, we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block, and then plug it as the main building block into the widely-used image-to-image translation UNet architecture. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and processed camera sensor noises) and resizing, and also involves a random shuffle strategy and a double degradation strategy. Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability. We believe our work can provide useful insights into current denoising research.
Stabilizing two-qubit entanglement by mimicking a squeezed environment

It is well known that qubits immersed in a squeezed vacuum environment exhibit many exotic phenomena, including dissipative entanglement stabilization. Here we show that these effects only require interference between excitation and decay processes, and can be faithfully mimicked without nonclassical light using a simple classical temporal modulation. We present schemes that harness this idea to stabilize entanglement between two remote qubits coupled via a transmission line or waveguide, where either the qubit-waveguide coupling is modulated, or the qubits are directly driven. We analyze the resilience of these approaches against various imperfections and also characterize the trade-off between the speed and quality of entanglement stabilization. Our protocols are compatible with state-of-the-art cavity QED systems.
The evaluation method for distance learning engagement of college English under the mixed teaching mode

In order to overcome the problems of low input and poor effectiveness of evaluation, this paper puts forward the evaluation method of distance learning input. This method constructs a learning behaviour input model under the mixed teaching mode of college English. We determine the evaluation standard of learning engagement as the comparison standard of evaluation indicators, collect the learning information of students in the process of distance English learning, and determine the specific indicators of learning input evaluation. Combined with the weight of the index, the specific value of the comprehensive evaluation index of learning input is calculated, and the final evaluation result of learning input is obtained by comparing with the evaluation standard. The experimental results show that the evaluation results are all over 85%, the evaluation effectiveness is 90%, and the average English score is improved by 7.8 points.
ML-HGR-Net: A Meta-Learning Network for FMCW Radar Based Hand Gesture Recognition

In recent years, radar-based hand gesture recognition (HGR) has attracted much attention in the field of human-computer interaction (HCI) due to its benefits of high recognition accuracy and independence from lighting conditions. Conventional deep learning (DL) based models for HGR rely on a large amount of labelled data for training to achieve a satisfactory recognition accuracy. However, it is usually time-consuming and labor-intensive for collecting large amounts of radar data and also difficult to cover all possible hand gesture classes. In this paper, we introduce meta-learning to address the few-shot learning problem for frequency modulated continuous wave (FMCW) radar based HGR. We propose a meta-learning network to learn a model that can quickly adapt to unseen hand gesture tasks with few training observations. In particular, we propose a 3D convolutional neural network (CNN) based dual-channel fusion network for feature extraction by exploiting the correlations of multiple features in radar echo signals to improve the recognition accuracy. In addition, we also develop a learnable relation module with neural networks as a non-linear classifier to measure the similarity between the samples of different hand gestures, which can be more applicable for the HGR task. Finally, we evaluate the performance of the proposed model by conducting experiments on an FMCW radar hardware system. Experimental results show that the proposed meta-learning model substantially enhances the recognition accuracy compared with the state-of-the-art methods including DL and meta-learning based models for FMCW radar-based HGR.
Approximation of a Microbiome Composition Shift by a Change in a Single Balance Between Two Groups of Taxa

Linking microbiome composition obtained from metagenomic or 16S rRNA sequencing to various factors poses a real challenge. The compositional approach to such data is well described: a so-called isometric log-ratio (ILR) transform provides correct treatment of relative abundances. Most existing compositional methods differ in the particular choice of the transform. Although this choice does not influence the prediction of a model, it determines the subset of balances between groups of microbial taxa subsequently used for interpreting the composition shifts. We propose a method to interpret these shifts independently of the initial choice of ILR coordinates by the nearest single-balance shift. We describe here application of the method to regression, classification, and principal balance analysis of compositional data. Analytical treatment and cross-validation show that the approach provides the least-squares estimate of a single-balance shift associated with a factor with possible adjustment for covariates. As for classification and principal balance analysis, the nearest balance method provides results comparable to other compositional tools. Its advantages are the absence of assumptions about the number of taxa included in the balance and its low computational cost. The method is implemented in the R package NearestBalance. IMPORTANCE The method proposed here extends the range of compositional methods providing interpretation of classical statistical tools applied to data converted to the ILR coordinates. It provides a strictly optimal solution in several special cases. The approach is universally applicable to compositional data of any nature, including microbiome data sets.
VAD, feature extraction and modelling techniques for speaker recognition: a review

This paper reviews an automatic speaker recognition technology, with an emphasis on state-of-the-art voice activity detection (VAD), feature extraction and speaker-modelling techniques that have emerged during the last few years. Researchers in the field of speaker recognition have made a few attempts to recognise the speaker in the language mismatch environment and limited data condition.To address robustness issues, we also elaborate language mismatch and limited data speaker recognition. Further, this paper identified some issues with the existing speaker recognition systems and also investigated areas of possible improvements in speaker recognition field. We conclude the paper with a discussion on the possible future directions.
Dataset for file fragment classification of audio file formats

File fragment classification of audio file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with audio formats. Therewith, there is no public dataset for file fragments of audio file formats. So, a big research challenge in file fragment classification of audio file formats is to compare the performance of the developed methods over the same datasets.In this study, we present a dataset that contains file fragments of 20 audio file formats: AMR, AMR-WB, AAC, AIFF, CVSD, FLAC, GSM-FR, iLBC, Microsoft ADPCM, MP3, PCM, WMA, A-Law, µ-Law, G.726, G.729, Microsoft GSM, OGG Vorbis, OPUS, and SPEEX. Corresponding to each format, the dataset contains the file fragments of audio files with different compression settings. For each pair of file format and compression setting, 210 file fragments are provided. Totally, the dataset contains 20,160 file fragments.