Reviewerly/exHarmony huggingface dataset:
Blowing pressure stabilization method for the artificial excitation of reed instruments (L)

To investigate the acoustics of reed instruments without the need for a human player, blowing machines are needed, which can generate air pressures up to 8 kPa and flow rates up to 40 liters per minute. Due to reed flexibility and the changing pressure gradient across the reed, the relationship between flow and pressure is highly non-linear. Since the output pressure of ventilators is highly dependent on flow, non-linear pressure regulation is a difficult task that requires a closed-loop approach. Since reed vibration starts suddenly when blowing pressure is gradually increased, an abrupt change in airflow through the instrument is present, resulting in a change in pressure in the artificial mouth. To avoid that, a method is presented to achieve a fast response to abrupt flow changes, which is tested in an existing blowing machine. The enhanced blowing machine exhibits a settling time below 200 ms, which allows for the generation of blowing pressures with linear responses.
Dense Prediction with Attentive Feature Aggregation

Aggregating information from features across different layers is essential for dense prediction models. Despite its limited expressiveness, vanilla feature concatenation dominates the choice of aggregation operations. In this paper, we introduce Attentive Feature Aggregation (AFA) to fuse different network layers with more expressive non-linear operations. AFA exploits both spatial and channel attention to compute weighted averages of the layer activations. Inspired by neural volume rendering, we further extend AFA with Scale-Space Rendering (SSR) to perform a late fusion of multi-scale predictions. AFA is applicable to a wide range of existing network designs. Our experiments show consistent and significant improvements on challenging semantic segmentation benchmarks, including Cityscapes and BDD100K at negligible computational and parameter overhead. In particular, AFA improves the performance of the Deep Layer Aggregation (DLA) model by nearly 6% mIoU on Cityscapes. Our experimental analyses show that AFA learns to progressively refine segmentation maps and improve boundary details, leading to new state-of-the-art results on boundary detection benchmarks on NYUDv2 and BSDS500.
May the Patterns Be with You: A Framework for HCI Patterns Development

By the end of the nineties, the concept of design patterns became a hot topic among the human-computer interaction community and many workshops have been held on the subject within international HCI conferences. After more than twenty years, HCI patterns continue to attract the attention of researchers around the world, but still struggle to be more widely adopted as a practical design tool. To better understand this context, we conducted a systematic literature mapping including papers from the ACM CHI Conference and from other five reputed HCI conference series. Through the analysis of 50 papers, we were able to elicit regional aspects, common terminology, and best practices for the research of patterns in HCI. Finally, based on the findings of the literature mapping, we propose an HCI pattern development framework that can assist researchers and professionals in the process of developing practical and useful pattern languages in a structured way.
SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences

Modern medical imaging techniques, such as ultrasound (US) and cardiac magnetic resonance (MR) imaging, have enabled the evaluation of myocardial deformation directly from an image sequence. While many traditional cardiac motion tracking methods have been developed for the automated estimation of the myocardial wall deformation, they are not widely used in clinical diagnosis, due to their lack of accuracy and efficiency. In this paper, we propose a novel deep learning-based fully unsupervised method, SequenceMorph, for in vivo motion tracking in cardiac image sequences. In our method, we introduce the concept of motion decomposition and recomposition. We first estimate the inter-frame (INF) motion field between any two consecutive frames, by a bi-directional generative diffeomorphic registration neural network. Using this result, we then estimate the Lagrangian motion field between the reference frame and any other frame, through a differentiable composition layer. Our framework can be extended to incorporate another registration network, to further reduce the accumulated errors introduced in the INF motion tracking step, and to refine the Lagrangian motion estimation. By utilizing temporal information to perform reasonable estimations of spatio-temporal motion fields, this novel method provides a useful solution for image sequence motion tracking. Our method has been applied to US (echocardiographic) and cardiac MR (untagged and tagged cine) image sequences; the results show that SequenceMorph is significantly superior to conventional motion tracking methods, in terms of the cardiac motion tracking accuracy and inference efficiency.
Streaming against the Environment

Building on an infrastructural approach, this chapter investigates the environmental impact of video streaming. It clarifies some of the less obvious relationships between media infrastructures, video compression standards, and electronics supply chains and demonstrates how their interactions unfold material effects on the environment. Complicating recent critical research on data centers, we posit that existing models for calculating the ecological footprint of video streaming cannot capture its full extent and advocate for an interdisciplinary approach to data, computation, and infrastructure. This approach informs our argument that the development of new compression standards redistributes environmental responsibility in a way that benefits streaming providers and data centers at the expense of end users and hardware manufacturers.
Assessing time-dependent performance of a feature selection method using correlation sharing T-statistics (corT) for heart failure data classification

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Nurain Ibrahim, Adina Najwa Kamarudin; Assessing time-dependent performance of a feature selection method using correlation sharing T-statistics (corT) for heart failure data classification. AIP Conference Proceedings 8 February 2023; 2500 (1): 020017. https://doi.org/10.1063/5.0109918 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
An improved scheme of practical Byzantine fault tolerant algorithm

Blockchain technology has become the hottest technology in the market because of its characteristics such as decentralization and data tamper resistance. With the development of blockchain, it is widely used in many fields. There are many types of blockchain, such as alliance chain, private chain and public chain. At present, the alliance chain has attracted extensive attention, but the commonly used PBFT algorithm still has many problems. In order to solve the high communication overhead and low efciency of PBFT consensus echanism, an improved PBFT consensus algorithm with a reputation mechanism are proposed in this paper. Diff erent permissions of nodes are made according to their diff erent reputation levels. Only the nodes with high reputation are qualifed to act as master nodes. Nodes with low reputation will be prohibited from participating in the consistency protocol and reduce the probability of becoming master nodes. In our scheme, the security, efciency and throughput of the consensus algorithm are improved, and the communication cost is reduced.
Research on Compression Optimization Algorithm for Super-resolution Reconstruction Network

Under the condition of limited resources of embedded systems, the paper proposes a compression optimization algorithm based on pruning and quantization, so that the computational requirements of the super-resolution reconstruction algorithm based on a Convolutional Neural Network (CNN) can be met. First, a multiple regularization pruning optimization algorithm based on an attention module and a BatchNorm layer is proposed. Then, a coordination optimization algorithm of INT8 training and quantization for FPGA architecture is proposed. The performance of the pruning optimization algorithm was verified for the Super-Resolution CNN (SRCNN), the Fast Super-Resolution CNN (FSRCNN), and the Very Deep Super-resolution CNN (VDSRCNN). As for SRCNN, the performance of the quantization optimization algorithm was verified on the FPGA EC2 hardware simulation platform. The results show that the proposed compression optimization algorithm can achieve a good balance between network accuracy and inference speed.
Salvage of Supervision in Weakly Supervised Object Detection and Segmentation

Weakly supervised vision tasks, including detection and segmentation, have attracted much attention in the vision community recently. However, the lack of detailed and precise annotations in the weakly supervised case leads to a large accuracy gap between weakly- and fully-supervised methods. In this article, we propose a new framework, Salvage of Supervision (SoS), with the key idea being to effectively harness every potentially useful supervisory signal in weakly supervised vision tasks. Starting with weakly supervised object detection (WSOD), we propose SoS-WSOD to shrink the technology gap between WSOD and FSOD, which utilizes the weak image-level labels, the pseudo-labels, and the power of semi-supervised object detection for WSOD. Moreover, SoS-WSOD removes restrictions in traditional WSOD methods, including the reliance on ImageNet pretraining and inability to use modern backbones. The SoS framework also extends to weakly supervised semantic segmentation and instance segmentation. On several weakly supervised vision benchmarks, SoS achieves significant performance boost and generalization ability.
Photonic Computing for Sale

Companies are increasingly being set up to commercialize optical processors�a technology with huge potential benefits, as well as some sticking points.
Neighborhood Constraints Based Bayesian Personalized Ranking for Explainable Recommendation

Model-based Collaborative Filtering (CF) methods such as Matrix Factorization (MF) have achieved promising ranking performance in recent years. However, these methods tend to be a black box that cant not provide any explanation for users. To obtain the trust of users and improve the transparency, recent research starts to focus on the explanation of recommendations. Explainable Bayesian Personalized Ranking (EBPR) leverages the relevant item to provide intuitive explanations for unexplainable model-based CF methods. It relies solely on feedback data and can be trained efficiently. However, EBPR ignores the neighborhood information of users and items in the latent space. In addition, the explainability of EBPR suffers from exposure bias. To address these issues, we propose a novel explainbale loss function and a corresponding Matrix Factorization-based model called Constrained Explainable Bayesian Personalized Ranking (CEBPR), which introduces neighborhood information in the latent space to enhance ranking performance and explainability. Furthermore, we analyze the impact of exposure bias on explainability and propose CEBPR+ to mitigate the bad effect. Finally, We conduct empirical experiments on three real-world datasets that demonstrate the advantages of our proposed methods in terms of accuracy and explainability.
SPAVIS: Mobile Application for Visually Impaired Based on Assistive Software and Volunteerism

Sri Lankan population accounts up to almost one million visually impaired individuals out of which are mostly students and young individuals. As the educational structure for the visually impaired improves with funds, blind schools, and free education, assistance with minute needs for most visually impaired individuals comes at a cost. There are many assistive technologies, such as audio books, screen magnifiers, braille books, and screen readers, prevalent around the island. However, there are several limitations to these technologies, mainly their availability and affordability. In Sri Lanka, many individuals, societies, clubs, and many more are willing to volunteer to help those in need, even those that require physical attention. As much as it is anticipated to aid those in need, there is very little attention to the ways it can be done. Hence, this research provides a way to develop a user-friendly mobile application with assistive software and volunteerism to aid visually impaired students with their daily needs.
Analysis of Need Response to the Development of E - Magazine on Static Fluid Material at Bengkulu City High School

The purpose of this work is to analyze the needs of teachers and students for learning media from electronic magazines for high school students about static fluid material. The research data was taken from physics teachers and also students of class XI of SMA N 1, SMA N. 6, SMA N 7 PLUS Bengkulu city. The data sources used in this study were obtained from physics teachers and 139 XI class students. This research method is a descriptive study included in the Department of Research and Development (RND). The research and development phases used are based on the ADDIE model, i.e. analysis, planning, development, implementation and evaluation. The research phase used the needs analysis phase, where tools such as school observation, interview and needs surveys were used. Data analysis techniques using quantitative data analysis and qualitative analysis. The results of this study indicate that the instrument used is valid and reliable. The results of this study also show that high school students and teachers strongly agree that they need learning environments based on electronic magazines.
Electrofishing Electrocution: Case Study in Forensic Medicine

Electrofishing is a fishing method allowed in our country only for research purposes. Law prohibits the use of this method for fishing. However, many anglers use the method to catch fish from private ponds for marketing and consumption. The method is not so harmful to fish as we will see, but, in case of non-compliance or ignorance of the method of correct use of the device, it can result in the death of the individual or individuals involved in their use for the desired catch. The article presents the case of 2 brothers, aged 47 and 50 respectively, who died few minutes one after each other in close vicinity, due to the use of a partially improvised electrofishing installation and non-compliance with electrical safety measures. Measures well known to researchers practicing this method for scientific purposes, in accordance with the law in force on the territory of our country.
The Josephson junction as a quantum engine

Abstract We treat the Cooper pairs in the superconducting electrodes of a Josephson junction (JJ) as an open system, coupled via Andreev scattering to external baths of electrons. The disequilibrium between the baths generates the direct-current bias applied to the JJ. In the weak-coupling limit we obtain a Markovian master equation that provides a simple dynamical description consistent with the main features of the JJ, including the form of the current–voltage characteristic, its hysteresis, and the appearance under periodic voltage driving of discrete Shapiro steps. For small dissipation, our model also exhibits a self-oscillation of the JJ’s electrical dipole with frequency <?CDATA $\Omega = 2 eV/\hbar$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi mathvariant="normal">Ω</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> <mml:mi>e</mml:mi> <mml:mi>V</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mi>ℏ</mml:mi> </mml:mrow> </mml:math> around mean voltage V . This self-oscillation, associated with ‘hidden attractors’ of the nonlinear equations of motion, explains the observed production of monochromatic radiation with frequency Ω and its harmonics. We argue that this picture of the JJ as a quantum engine resolves open questions about the Josephson effect as an irreversible process and could open new perspectives in quantum thermodynamics and in the theory of dynamical systems.
Multiqubit noise deconvolution and characterization

We present a noise deconvolution technique for obtaining noiseless expectation values of noisy observables at the output of multiqubit quantum channels. For any number of qubits or in the presence of correlations, our protocol applies to any mathematically invertible noise model, even when its inverse map is not physically implementable, i.e., when it is neither completely positive nor trace preserving. For a generic observable affected by Pauli noise it provides a quadratic speedup, always producing a rescaling of its Pauli basis components. We show that it is still possible to achieve the deconvolution while experimentally estimating the noise parameters, whenever these are unknown (bypassing resource-heavy techniques such as quantum process tomography). We provide a simulation, with examples for both Pauli and non-Pauli channels.
Automatic Candidature Selection by Artificial Natural Language Processing

Finding the right person for the job is a daunting task, especially when we have a large number of job applicants. This will reduce the performance of the team looking for a suitable candidate, the process sometimes takes a long time, and the default “Restart Arrangement and Alignment” program can really help the short course of a short shortlist, which can undoubtedly help to select a competitor and a flexible cycle. The proposed model is capable of dealing with a large number of restatements, first combining classes using a different separator and then the expected set of tasks. Now, candidates can be rescheduled using content-based recommendation, using cosine intimacy and using KNN to split resumes close to a set of expected commitments. In this study and proposed work done on the linear support vector classifier with natural language processing and text classification with the help of algorithm and obtained 79.53% accuracy.
Decoupled Mixup for Out-of-Distribution Visual Recognition

Convolutional neural networks (CNN) have demonstrated remarkable performance, when the training and testing data are from the same distribution. However, such trained CNN models often largely degrade on testing data which is unseen and Out-Of-the-Distribution (OOD). To address this issue, we propose a novel “Decoupled-Mixup" method to train CNN models for OOD visual recognition. Different from previous work combining pairs of images homogeneously, our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combine these regions of image pairs to train CNN models. Since the observation is that noise-prone regions such as textural and clutter background are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair. To further improves the generalization ability of trained models, we propose to disentangle discriminative and noise-prone regions in frequency-based and context-based fashions. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts, where our method achieves 85.76% top-1 accuracy in Track-1 and 79.92% in Track-2 in NICO Challenge. The source code is available at https://github.com/HaozheLiu-ST/NICOChallenge-OOD-Classification .
Smoothing distributions for conditional Fleming–Viot and Dawson–Watanabe diffusions

We study the distribution of the unobserved states of two measure-valued diffusions of Fleming–Viot and Dawson–Watanabe type, conditional on observations from the underlying populations collected at past, present and future times. If seen as nonparametric hidden Markov models, this amounts to finding the smoothing distributions of these processes, which we show can be explicitly described in recursive form as finite mixtures of laws of Dirichlet and gamma random measures respectively. We characterize the time-dependent weights of these mixtures, accounting for potentially different time intervals between data collection times, and fully describe the implications of assuming a discrete or a nonatomic distribution for the underlying process that drives mutations. In particular, we show that with a nonatomic mutation offspring distribution, the inference automatically upweights mixture components that carry, as atoms, observed types shared at different collection times. The predictive distributions for further samples from the population conditional on the data are also identified and shown to be mixtures of generalized Pólya urns, conditionally on a latent variable in the Dawson–Watanabe case.
Optimizing Data Transport Efficiency in Datacenters through Traffic Shaping of BBR Congestion Control

Compared to commonly used loss-based congestion control algorithms predominantly used in Transmission Control Protocol (TCP) implementations, congestion-based congestion control called BBR has shown much better performance in resource-abundant modern communication links. However, for a high influx of TCP sessions on the bottleneck switch, clusters in High-Performance Compute (HPC) nodes and data centers face resource constraints because of the immense workload during orchestration and relocation of workflows across the resource pool. This article discusses how to resolve this problem, commonly known as TCP incast, through efficient queue management of the bottleneck link and adding a shaper function in the standard BBR algorithm. We analyzed TCP incast issue for two efficient versions of congestion control i.e., BBR and CUBIC (named after the cubic function used instead of linear function), in a highly overloaded convergent switch of the cluster. It is noticeable that the queuing delay and buffer build-up are two essential parameters in causing TCP Incast. Hence, we used the M/G/1/B queuing model when multiple TCP sessions generate the network traffic and different buffer build-up scenarios are analyzed in the bottleneck node of HPC clusters. Based on the findings of our queuing analysis, we propose an incast recovery BBR algorithm that introduces additional controls like Incast shaping to deal with queue build-up during TCP incast. The effects of these modifications in BBR implementation are studied in terms of performance parameters like flow completion time, throughput, RTT variations, and fairness to other competing flows are significant compared with standard BBR and CUBIC implementations.
Multi proxy anchor family loss for several types of gradients

The deep metric learning (DML) objective is to learn a neural network that maps into an embedding space where similar data are near and dissimilar data are far. However, conventional proxy-based losses for DML have two problems: gradient problem and application of the real-world dataset with multiple local centers. Additionally, the performance metrics of DML also have some issues with stability and flexibility. This paper proposes three multi-proxies anchor (MPA) family losses and a normalized discounted cumulative gain (nDCG@k) metric. This paper makes three contributions. (1) MPA-family losses can learn using a real-world dataset with multi-local centers. (2) MPA-family losses improve the training capacity of a neural network owing to solving the gradient problem. (3) MPA-family losses have data-wise or class-wise characteristics with respect to gradient generation. Finally, we demonstrate the effectiveness of MPA-family losses, and MPA-family losses achieves higher accuracy on two datasets for fine-grained images.