Reviewerly/exHarmony huggingface dataset:
DepGraph: A Dependency-Driven Accelerator for Efficient Iterative Graph Processing

Many graph processing systems have been recently developed for many-core processors. However, for iterative graph processing, due to the dependencies between vertices' states, the propagations of new states of vertices are inherently conducted along graph paths sequentially and are also dependent on each other. Despite the years' research effort, existing solutions still severely underutilize many-core processors to quickly propagate the new states of vertices, suffering from slow convergence speed. In this paper, we propose a dependency-driven programmable accelerator, DepGraph, which couples with the core architecture of the many-core processor and can fundamentally alleviate the challenge of dependencies for faster state propagation. Specifically, we propose an effective dependency-driven asynchronous execution approach into novel microarchitecture designs for faster state propagations. DepGraph prefetches the vertices for the core on-the-fly along the dependency chains between their states and the active vertices' new states, aiming to effectively accelerate the propagations of the active vertices' new states and also ensure better data locality. Through transforming the dependency chains along the frequently-used paths into direct ones at runtime and maintaining these calculated direct dependencies as a set of fast shortcuts, called hub index, DepGraph further accelerates most state propagations. Also, many propagations do not need to wait for the completion of other propagations, which enables more propagations to be effectively conducted along the paths with higher degree of parallelism. The experimental results show that for iterative graph processing on a simulated 64-core processor, a cutting-edge software graph processing system can achieve 5.0-22.7 times speedup after integrating with our DepGraph while incurring only 0.6% area cost. In comparison with three state-of-the-art hardware solutions, i.e., HATS, Minnow, and PHI, DepGraph improves the performance by up to 3.0-14.2, 2.2-5.8, and 2.4-10.1 times, respectively.
A Survey on Video Streaming in Multipath and Multihomed Overlay Networks

The focus of this survey is to study the protocols, mechanisms, and the latest standards proposed in the literature for improving the performance and quality of video content in multipath and multihomed overlay networks.Multipath is a broader term, but in the context of this survey, we define multipath as enhancing network routing technique by using various paths that are not necessarily completely disjoint.Multipath can furnish a variety of advantages such as reliability,connection persistence, increased perceived throughput and load balancing.On the other hand, multihoming is the ability to use multiple network interfaces when connecting to the Internet to increase reliability, resilience, and performance.Most existing surveys are specialized in one specific domain area related to multipath or multihoming.This study covers the research proposals at the different layers/sublayers of an overlay network from transport to the application and extends to cover the latest technologies like machine learning, Fog and Mobile Edge computing, VR 360 video, and the Internet of Multimedia Things (IoMT).As such, our work tries to be as comprehensive as possible to relate multipath and multihoming research solutions for video streaming to the current and emerging video streaming technologies.
Improving Land Cover Classification Using Genetic Programming for Feature Construction

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.
Neuro-Inspired Computing with Spin-VCSELs

Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.
A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization

With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algorithm is introduced to schedule applications' tasks and enhance load balancing. It uses Copula function to explore the relation of the random parameters random numbers and defines the local attractor to avoid the fitness function to be trapped into local optimum. The simulation results show that the proposed resource scheduling and allocation model can effectively improve the resource utilization of cloud computing and greatly reduce the completion time of tasks.
Transformers for Clinical Coding in Spanish

Automatic clinical coding is an essential task in the process of extracting relevant information from unstructured documents contained in electronic health records (EHRs). However, most research in the development of computer-based methods for clinical coding focuses on texts written in English due to the limited availability of medical linguistic resources in languages other than English. With nearly 500 million native speakers, there is a worldwide interest in processing healthcare texts in Spanish. In this study, we systematically analyzed transformer-based models for automatic clinical coding in Spanish. Using a transfer-learning-based approach, the three existing transformer architectures that support the Spanish language, namely, multilingual BERT (mBERT), BETO and XLM-RoBERTa (XLM-R), were first pretrained on a corpus of real-world oncology clinical cases with the goal of adapting transformers to the particularities of Spanish medical texts. The resulting models were fine-tuned on three distinct clinical coding tasks, following a multilabel sentence classification strategy. For each analyzed transformer, the domain-specific version outperformed the original general domain model across those tasks. Moreover, the combination of the developed strategy with an ensemble approach leveraging the predictive capacities of the three distinct transformers yielded the best obtained results, with MAP scores of 0.662, 0.544 and 0.884 on CodiEsp-D, CodiEsp-P and Cantemist-Coding shared tasks, which remarkably improved the previous state-of-the-art performance by 11.6%, 10.3% and 4.4%, respectively. We publicly release the mBERT, BETO and XLMR transformers adapted to the Spanish clinical domain at https://github.com/guilopgar/ClinicalCodingTransformerES, providing the clinical natural language processing community with advanced deep learning methods for performing medical coding and other tasks in the Spanish clinical domain.
Tradeoff Relations in Quantum Resource Theory

Abstract Entanglement and coherence are deemed as two unreplaceable and important quantum resources in the regime of quantum physics, which are widely applied to quantum information processing and quantum computation. It is therefore natural to ask if there exists any intrinsic relationship between entanglement and coherence. In this work, the authors originally put forward tradeoff relations between intrinsic concurrence and first‐order coherence in the context of an arbitrary three‐qubit state. To do so, an equality relation related to the quantum entanglement and first‐order coherence for arbitrary three‐qubit pure states is derived first, and then extended to present another tradeoff relation regarding arbitrary three‐qubit states. Notably, physical explanation is offered for how coherence migrates in three‐qubit systems from first‐order coherence of a given subsystem to quantum correlations between subsystems. The result shows that the derived tradeoff relations can reveal the complementarity between intrinsic concurrence and first‐order coherence, which provides reliable theoretical basis of transformation and flow for quantum resources.
CoNotate: Suggesting Queries Based on Notes Promotes Knowledge Discovery

When exploring a new domain through web search, people often struggle to articulate queries because they lack domain-specific language and well-defined informational goals. Perhaps search tools rely too much on the query to understand what a searcher wants. Towards expanding this contextual understanding of a user during exploratory search, we introduce a novel system, CoNotate, which offers query suggestions based on analyzing the searcher's notes and previous searches for patterns and gaps in information. To evaluate this approach, we conducted a within-subjects study where participants (n=38) conducted exploratory searches using a baseline system (standard web search) and the CoNotate system. The CoNotate approach helped searchers issue significantly more queries, and discover more terminology than standard web search. This work demonstrates how search can leverage user-generated content to help people get started when exploring complex, multi-faceted information spaces.
Intent-driven Closed Loops for Autonomous Networks

Closed loops are key enablers for automation that have been successfully used in many industries for long, and more recently for computing and networking applications. The Zero-touch network and service management (ZSM) framework introduced standardized components that allow the creation, execution, and governance of multiple closed loops, enabling zero-touch management of end-to-end services across different management domains. However, the coordinated and optimal instantiation and operation of multiple closed loops is an open question that is left for implementation by the ZSM specifications. In this paper, we propose a methodology that uses intents as a way of communicating requirements to be considered by autonomous management domains to coordinate hierarchies of closed loops. The intent-driven methodology facilitates hierarchical and peer interactions for delegation and escalation of intents. Furthermore, it extends the existing management capabilities of the ZSM framework and facilitates conflict-free integration of closed loops by setting optimal (and non-conflicting) goals that each closed loop in the hierarchy needs to account for. We show an example of the application of the proposed methodology in a network slicing assurance use case. The new capabilities introduced in this paper can be considered as an extension of the ZSM framework to be used in scenarios where multiple intent-driven closed loops exist.
A ResNet-50-Based Convolutional Neural Network Model for Language ID Identification from Speech Recordings

This paper describes the model built for the SIGTYP 2021 Shared Task aimed at identifying 18 typologically different languages from speech recordings.Mel-frequency cepstral coefficients derived from audio files are transformed into spectrograms, which are then fed into a ResNet-50-based CNN architecture.The final model achieved validation and test accuracies of 0.73 and 0.53, respectively.
Mean-Shifted Contrastive Loss for Anomaly Detection

Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets (e.g. ImageNet classification) can significantly improve anomaly detection performance. One approach is outlier exposure, which fails when the external datasets do not resemble the anomalies. We take the approach of transferring representations pre-trained on external datasets for anomaly detection. Anomaly detection performance can be significantly improved by fine-tuning the pre-trained representations on the normal training images. In this paper, we first demonstrate and analyze that contrastive learning, the most popular self-supervised learning paradigm cannot be naively applied to pre-trained features. The reason is that pre-trained feature initialization causes poor conditioning for standard contrastive objectives, resulting in bad optimization dynamics. Based on our analysis, we provide a modified contrastive objective, the Mean-Shifted Contrastive Loss. Our method is highly effective and achieves a new state-of-the-art anomaly detection performance including 98.6% ROC-AUC on the CIFAR-10 dataset.
Measuring Social Biases in Grounded Vision and Language Embeddings

We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings.Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings.This is despite the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both.Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting.We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact.These metrics are used on a new dataset, the first for grounded bias, created by augmenting standard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images.Dataset construction is challenging because vision datasets are themselves very biased.The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society.
ConVEx: Data-Efficient and Few-Shot Slot Labeling

We propose ConVEx (Conversational Value Extractor), an efficient pretraining and finetuning neural approach for slot-labeling dialog tasks.Instead of relying on more general pretraining objectives from prior work (e.g., language modeling, response selection), Con-VEx's pretraining objective, a novel pairwise cloze task using Reddit data, is well aligned with its intended usage on sequence labeling tasks.This enables learning domain-specific slot labelers by simply fine-tuning decoding layers of the pretrained general-purpose sequence labeling model, while the majority of the pretrained model's parameters are kept frozen.We report state-of-the-art performance of ConVEx across a range of diverse domains and data sets for dialog slot-labeling, with the largest gains in the most challenging, few-shot setups.We believe that ConVEx's reduced pretraining times (i.e., only 18 hours on 12 GPUs) and cost, along with its efficient finetuning and strong performance, promise wider portability and scalability for data-efficient sequence-labeling tasks in general.
Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one singleton syntax, such as either syntactic dependency or constituency tree.In this paper, we explore the integration of heterogeneous syntactic representations for SRL.We first consider a TreeLSTM-based integration, collaboratively learning the phrasal boundaries from the constituency and the semantic relations from dependency.We further introduce a labelaware GCN solution for simultaneously modeling the syntactic edges and labels.Experimental results demonstrate that by effectively combining the heterogeneous syntactic representations, our methods yield task improvements on both span-based and dependencybased SRL.Also our system achieves new state-of-the-art SRL performances, meanwhile bringing explainable task improvements.
Fingerprinting fluid evolution by trace elements in epithermal pyrite, Vatukoula Au-Te deposit, Fiji

Tellurium (Te) is an important element for green technologies, including thin film photovoltaic panels. Stagnating supply caused by its by-product dependency together with an increasing demand will likely lead to a shortage in the near future. Pyrite may represent a potential target for the direct recovery of Te, since the concentrations of Te can reach weight percent levels under specific, but poorly constrained hydrothermal conditions. The Vatukoula (formerly Emperor) epithermal Au-Te deposit is currently mined for Au and ~ 75% are recovered from Au-tellurides and pyrite. Tellurium has been processed in the past, but is now sent to tailings despite of concentrations reaching 1.4 wt. % in pyrite, which makes this system a natural laboratory to investigate key Te enrichment processes. The epithermal mineralisation at Vatukoula consists of an early low-grade disseminated host-rock and a later high-grade Ag-Au-Te vein-type stage. Pyrite occurs in both ore-types with diverse As contents including concentrations < 0.5 wt. % in pure pyrite, 0.5 to 13 wt. % in low-As and 23 to 43 wt. % in high-As pyrite, reflecting increasing fluid As with system evolution towards the high-grade mineralisation. Tellurium contents in pyrite increase from pure pyrite (up to 220 ppm) to low-As pyrite (up to 1.4 wt. %), but with a decrease towards high-As pyrite (up to 185 ppm). This is a yet unknown relation, since trace element incorporation in pyrite is usually facilitated by increasing As. Instead, the presented results suggest a threshold between 13 and 23 wt. % As, above which the incorporation of Te and related elements (e.g., Ag, Au) may be less favourable. Mineral stabilities, trace element ratios (e.g., Sb/Pb, Tl/Pb) and LA-ICP-MS mapping of texturally distinct pyrite indicate that fluid boiling was a critical process for the Ag, Au and Te enrichment in the Vatukoula epithermal system. Fluid sulphidation states decreased with proceeding evolution of the epithermal system from initially intermediate- to late low-sulphidation conditions, which led to the diverse chemical character of pyrite. Early pure pyrite primarily formed as a replacement product of magnetite in the altered host rocks, whereas low- and high-As pyrite precipitated in the direct vicinity of Ag-Au-telluride veins, from which most of the As, Ag, Te and Au hosted in pyrite was derived from. Consequently, the trace element composition of pyrite allows the fingerprinting of the fluid evolution of Te-rich ore-forming systems like Vatukoula, Fiji.
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution

We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document.In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking.The used KB entity representations are learned from either (i) hyperlinked text documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear complementary in raising IE performance.Representations of corresponding entity linking (EL) candidates are added to text span representations of the input document, and we experiment with (i) taking a weighted average of the EL candidate representations based on their prior (in Wikipedia), and (ii) using an attention scheme over the EL candidate list.Results demonstrate an increase of up to 5% F1-score for the evaluated IE tasks on two datasets.Despite a strong performance of the prior-based model, our quantitative and qualitative analysis reveals the advantage of using the attention-based approach.
FaceController: Controllable Attribute Editing for Face in the Wild

Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. By simply employing some existing and easy-obtainable prior information, our method can control, transfer, and edit diverse attributes of faces in the wild. The proposed method can consequently be applied to various applications such as face swapping, face relighting, and makeup transfer. In our method, we decouple identity, expression, pose, and illumination by using 3D priors; separate texture and colors by using region-wise style codes. All the information is embedded into adversarial learning by our identity-style normalization module. Disentanglement losses are proposed to enhance the generator to extract information independently from each attribute. Comprehensive quantitative and qualitative evaluations have been conducted. In a single framework, our method achieves the best or competitive scores on a variety of face applications.