Miracle-dz/UniFAR-FTUs huggingface dataset:
EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising

Objective. Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising. Approach. Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG. Main results. We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination. Significance. Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available at https://github.com/ncclabsustech/EEGdenoiseNet.
A Study on Energy Resolution of CANDLES Detector

In a neutrinoless double-beta decay (<inline-formula> <tex-math notation="LaTeX">$0\nu \beta \beta $ </tex-math></inline-formula>) experiment, energy resolution is important to distinguish between <inline-formula> <tex-math notation="LaTeX">$0\nu \beta \beta $ </tex-math></inline-formula> and background events. CAlcium fluoride for studies of Neutrino and Dark matters by Low Energy Spectrometer (CANDLES) discerns the <inline-formula> <tex-math notation="LaTeX">$0\nu \beta \beta $ </tex-math></inline-formula> of <sup>48</sup>Ca using a CaF<sub>2</sub> scintillator as the detector and source. Photomultiplier tubes (PMTs) collect scintillation photons. At the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value of <sup>48</sup>Ca, the current energy resolution (2.6%) exceeds the ideal statistical fluctuation of the number of photoelectrons (1.6%). Because of CaF<sub>2</sub>’s long decay constant of 1000 ns, a signal integration within 4000 ns is used to calculate the energy. The baseline fluctuation (<inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm{ baseline}}$ </tex-math></inline-formula>) is accumulated in the signal integration, thus degrading the energy resolution. This article studies <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm{ baseline}}$ </tex-math></inline-formula> in the CANDLES detector, which severely degrades the resolution by 1% at the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value of <sup>48</sup>Ca. To avoid <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm{ baseline}}$ </tex-math></inline-formula>, photon counting can be used to obtain the number of photoelectrons in each PMT; however, a significant photoelectron signal overlapping probability in each PMT causes missing photoelectrons in counting and reduces the energy resolution. “Partial photon counting” reduces <inline-formula> <tex-math notation="LaTeX">$\sigma _{\mathrm{ baseline}}$ </tex-math></inline-formula> and minimizes photoelectron loss. We obtain improved energy resolutions of 4.5%–4.0% at 1460.8 keV (<inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-ray of <sup>40</sup>K) and 3.3%–2.9% at 2614.5 keV (<inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>-ray of <sup>208</sup>Tl). The energy resolution at the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value is estimated to be improved from 2.6% to 2.2%, and the detector sensitivity for the <inline-formula> <tex-math notation="LaTeX">$0\nu \beta \beta $ </tex-math></inline-formula> half-life of <sup>48</sup>Ca can be improved by 1.09 times.
Learning Equality Constraints for Motion Planning on Manifolds

Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN). The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion planner. Learning proceeds by aligning subspaces in the network with subspaces of the data. We combine both learned constraints and analytically described constraints into the planner and use a projection-based strategy to find valid points. We evaluate ECoMaNN on its representation capabilities of constraint manifolds, the impact of its individual loss terms, and the motions produced when incorporated into a planner.
Spatially resolved star formation and fuelling in galaxy interactions

We investigate the spatial structure and evolution of star formation and the interstellar medium (ISM) in interacting galaxies. We use an extensive suite of parsec-scale galaxy-merger simulations (stellar mass ratio = 2.5:1), which employs the ‘Feedback In Realistic Environments-2’ model (fire-2). This framework resolves star formation, feedback processes, and the multiphase structure of the ISM. We focus on the galaxy-pair stages of interaction. We find that close encounters substantially augment cool (H i) and cold-dense (H2) gas budgets, elevating the formation of new stars as a result. This enhancement is centrally concentrated for the secondary galaxy, and more radially extended for the primary. This behaviour is weakly dependent on orbital geometry. We also find that galaxies with elevated global star formation rate (SFR) experience intense nuclear SFR enhancement, driven by high levels of either star formation efficiency (SFE) or available cold-dense gas fuel. Galaxies with suppressed global SFR also contain a nuclear cold-dense gas reservoir, but low SFE levels diminish SFR in the central region. Concretely, in the majority of cases, SFR enhancement in the central kiloparsec is fuel-driven (55 per cent for the secondary, 71 per cent for the primary) – while central SFR suppression is efficiency-driven (91 per cent for the secondary, 97 per cent for the primary). Our numerical predictions underscore the need of substantially larger, and/or merger-dedicated, spatially resolved galaxy surveys – capable of examining vast and diverse samples of interacting systems – coupled with multiwavelength campaigns aimed to capture their internal ISM structure.
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy

The success of machine learning algorithms often relies on a large amount of high-quality data to train well-performed models. However, data is a valuable resource and are always held by different parties in reality. An effective solution to such a data isolation problem is to employ federated learning, which allows multiple parties to collaboratively train a model. In this paper, we propose a Secure version of the widely used Maximum Mean Discrepancy (SMMD) based on homomorphic encryption to enable effective knowledge transfer under the data federation setting without compromising the data privacy. The proposed SMMD is able to avoid the potential information leakage in transfer learning when aligning the source and target data distribution. As a result, both the source domain and target domain can fully utilize their data to build more scalable models. Experimental results demonstrate that our proposed SMMD is secure and effective.
Unifying data for fine-grained visual species classification

Wildlife monitoring is crucial to nature conservation and has been done by manual observations from motion-triggered camera traps deployed in the field. Widespread adoption of such in-situ sensors has resulted in unprecedented data volumes being collected over the last decade. A significant challenge exists to process and reliably identify what is in these images efficiently. Advances in computer vision are poised to provide effective solutions with custom AI models built to automatically identify images of interest and label the species in them. Here we outline the data unification effort for the Wildlife Insights platform from various conservation partners, and the challenges involved. Then we present an initial deep convolutional neural network model, trained on 2.9M images across 465 fine-grained species, with a goal to reduce the load on human experts to classify species in images manually. The long-term goal is to enable scientists to make conservation recommendations from near real-time analysis of species abundance and population health.
Distributed Community Detection for Large Scale Networks Using Stochastic Block Model

With rapid developments of information and technology, large scale network data are ubiquitous. In this work we develop a distributed spectral clustering algorithm for community detection in large scale networks. To handle the problem, we distribute l pilot network nodes on the master server and the others on worker servers. A spectral clustering algorithm is first conducted on the master to select pseudo centers. The indexes of the pseudo centers are then broadcasted to workers to complete distributed community detection task using a SVD type algorithm. The proposed distributed algorithm has three merits. First, the communication cost is low since only the indexes of pseudo centers are communicated. Second, no further iteration algorithm is needed on workers and hence it does not suffer from problems as initialization and non-robustness. Third, both the computational complexity and the storage requirements are much lower compared to using the whole adjacency matrix. A Python package DCD (this http URL) is developed to implement the distributed algorithm for a Spark system. Theoretical properties are provided with respect to the estimation accuracy and mis-clustering rates. Lastly, the advantages of the proposed methodology are illustrated by experiments on a variety of synthetic and empirical datasets.
Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining

Abstract There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all irrelevant responses. Ideally, such models should be trained using multiple relevant and irrelevant responses for any given context. However, no such data is publicly available, and hence existing models are usually trained using a single relevant response and multiple randomly selected responses from other contexts (random negatives). To allow for better training and robust evaluation of model-based metrics, we introduce the DailyDialog++ dataset, consisting of (i) five relevant responses for each context and (ii) five adversarially crafted irrelevant responses for each context. Using this dataset, we first show that even in the presence of multiple correct references, n-gram based metrics and embedding based metrics do not perform well at separating relevant responses from even random negatives. While model-based metrics perform better than n-gram and embedding based metrics on random negatives, their performance drops substantially when evaluated on adversarial examples. To check if large scale pretraining could help, we propose a new BERT-based evaluation metric called DEB, which is pretrained on 727M Reddit conversations and then finetuned on our dataset. DEB significantly outperforms existing models, showing better correlation with human judgments and better performance on random negatives (88.27% accuracy). However, its performance again drops substantially when evaluated on adversarial responses, thereby highlighting that even large-scale pretrained evaluation models are not robust to the adversarial examples in our dataset. The dataset1 and code2 are publicly available.
ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation

Providing personalized recommendations that are also accompanied by explanations as to why an item is recommended is a research area of growing importance. At the same time, progress is limited by the availability of open evaluation resources. In this work, we address the task of scientific literature recommendation. We present arXivDigest, which is an online service providing personalized arXiv recommendations to end users and operates as a living lab for researchers wishing to work on explainable scientific literature recommendations.
Mex-related partitions and relations to ordinary partition and singular overpartitions

In a recent paper, Andrews and Newman introduced certain families of partition functions using the minimal excludant or "mex" function. In this article, we study two of the families of functions Andrews and Newman introduced, namely $p_{t,t}(n)$ and $p_{2t,t}(n)$. We establish identities connecting the ordinary partition function $p(n)$ to $p_{t,t}(n)$ and $p_{2t,t}(n)$ for all $t\geq 1$. Using these identities, we prove that the Ramanujan's famous congruences for $p(n)$ are also satisfied by $p_{t,t}(n)$ and $p_{2t,t}(n)$ for infinitely many values of $t$. Very recently, da Silva and Sellers provide complete parity characterizations of $p_{1,1}(n)$ and $p_{3,3}(n)$. We prove that $p_{t,t}(n)\equiv \overline{C}_{4t,t}(n) \pmod{2}$ for all $n\geq 0$ and $t\geq 1$, where $\overline{C}_{4t,t}(n)$ is the Andrews' singular overpartition function. Using this congruence, the parity characterization of $p_{1,1}(n)$ given by da Silva and Sellers follows from that of $\overline{C}_{4,1}(n)$. We also give elementary proofs of certain congruences already proved by da Silva and Sellers.
N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models

We introduce N-LTP, an open-source Python Chinese natural language processing toolkit supporting five basic tasks: Chinese word segmentation, part-of-speech tagging, named entity recognition, dependency parsing, and semantic dependency parsing. N-LTP adopts the multi-task framework with the pre-trained model to capture the shared knowledge across all Chinese relevant tasks. In addition, we propose to use knowledge distillation where single-task models teach a multi-task model, helping the multi-task model surpass its single-task teachers. Finally, we provide fundamental tasks API and a visualization tool to make users easier to use and view the processing results directly. To the best of our knowledge, this is the first toolkit to support all Chinese NLP fundamental tasks. Source code, documentation, and pre-trained models are available at this https URL.
A Variational Auto-Encoder for Reservoir Monitoring

Carbon dioxide Capture and Storage (CCS) is an important strategy in mitigating anthropogenic CO$_2$ emissions. In order for CCS to be successful, large quantities of CO$_2$ must be stored and the storage site conformance must be monitored. Here we present a deep learning method to reconstruct pressure fields and classify the flux out of the storage formation based on the pressure data from Above Zone Monitoring Interval (AZMI) wells. The deep learning method is a version of a semi conditional variational auto-encoder tailored to solve two tasks: reconstruction of an incremental pressure field and leakage rate classification. The method, predictions and associated uncertainty estimates are illustrated on the synthetic data from a high-fidelity heterogeneous 2D numerical reservoir model, which was used to simulate subsurface CO$_2$ movement and pressure changes in the AZMI due to a CO$_2$ leakage.
Novel Keyword Extraction and Language Detection Approaches

Fuzzy string matching and language classification are important tools in Natural Language Processing pipelines, this paper provides advances in both areas. We propose a fast novel approach to string tokenisation for fuzzy language matching and experimentally demonstrate an 83.6% decrease in processing time with an estimated improvement in recall of 3.1% at the cost of a 2.6% decrease in precision. This approach is able to work even where keywords are subdivided into multiple words, without needing to scan character-to-character. So far there has been little work considering using metadata to enhance language classification algorithms. We provide observational data and find the Accept-Language header is 14% more likely to match the classification than the IP Address.
The Optimal DoF for the Noncoherent MIMO Channel with Generic Block Fading

The high-SNR capacity of the noncoherent MIMO channel has been derived for the case of independent and identically distributed (IID) Rayleigh block fading by exploiting the Gaussianity of the channel matrix. This implies the optimal degrees of freedom (DoF), i.e., the capacity pre-log factor. Nevertheless, as far as the optimal DoF is concerned, IID Rayleigh fading is apparently a sufficient but not necessary condition. In this paper, we show that the optimal DoF for the IID Rayleigh block fading channel is also the optimal DoF for a more general class of generic block fading channels, in which the random channel matrix has finite power and finite differential entropy. Our main contribution is a novel converse proof based on the duality approach.
Unravelling the role of coherence in the first law of quantum thermodynamics

One of the fundamental questions in the emerging field of quantum thermodynamics is the role played by coherence in energetic processes that occur at the quantum level. Here, we address this issue by investigating two different quantum versions of the first law of thermodynamics, derived from the classical definitions of work and heat. By doing so, we find out that there exists a mathematical inconsistency between both scenarios. We further show that the energetic contribution of the dynamics of coherence is the key ingredient to establish the consistency. Some examples involving two-level atomic systems are discussed in order to illustrate our findings.
An analysis of concurrency control protocols for in-memory databases with CCBench

This paper presents yet another concurrency control analysis platform, CCBench. CCBench supports seven protocols (Silo, TicToc, MOCC, Cicada, SI, SI with latch-free SSN, 2PL) and seven versatile optimization methods and enables the configuration of seven workload parameters. We analyzed the protocols and optimization methods using various workload parameters and a thread count of 224. Previous studies focused on thread scalability and did not explore the space analyzed here. We classified the optimization methods on the basis of three performance factors: CPU cache, delay on conflict, and version lifetime. Analyses using CCBench and 224 threads, produced six insights. The performance of optimistic concurrency control protocol for a read-only workload rapidly degrades as cardinality increases even without L3 cache misses. (I2) Silo can outperform TicToc for some write-intensive workloads by using invisible reads optimization. (I3) The effectiveness of two approaches to coping with conflict (wait and no-wait) depends on the situation. (I4) OCC reads the same record two or more times if a concurrent transaction interruption occurs, which can improve performance. (I5) Mixing different implementations is inappropriate for deep analysis. (I6) Even a state-of-the-art garbage collection method cannot improve the performance of multi-version protocols if there is a single long transaction mixed into the workload. On the basis of I4, we defined the read phase extension optimization in which an artificial delay is added to the read phase. On the basis of I6, we defined the aggressive garbage collection optimization in which even visible versions are collected. The code for CCBench and all the data in this paper are available online at GitHub.
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.
Decompositions of the stable module infinity -category

We show that the stable module ∞ -category of a ﬁnite group G decomposesin three diﬀerent ways as a limit of the stable module ∞ -categories of certain subgroups of G . Analo-gously to Dwyer’s terminology for homology decompositions, we call these the centraliser, normaliser, and subgroup decompositions. We construct centraliser and normaliser decompositions and extend the subgroup decomposition (constructed by Mathew) to more collections of subgroups. The key step in the proof is extending the stable module ∞ -category to be deﬁned for any G -space, then showing that this extension only depends on the S -equivariant homotopy type of a G -space. The methods used are not speciﬁc to the stable module ∞ -category, so may also be applicable in other settings where an ∞ -category depends functorially on G .
Complex Convolutional Neural Networks for Ultrasound Image Reconstruction from In-Phase/Quadrature Signal

A wide variety of studies based on deep learning have recently been investigated to improve ultrasound (US) imaging. Most of these approaches were performed on radio frequency (RF) signals. However, inphase/quadrature (I/Q) digital beamformers (IQBF) are now widely used as low-cost strategies. In this work, we leveraged complex convolutional neural networks (CCNNs) for reconstructing ultrasound images from I/Q signals. We recently described a CNN architecture called ID-Net, which exploited an inception layer devoted to the reconstruction of RF diverging-wave (DW) ultrasound images. We derived in this work the complex equivalent of this network, i.e., the complex inception for DW network (CID-Net), operating on I/Q data. We provided experimental evidence that the CID-Net yields the same image quality as that obtained from the RF-trained CNNs; i.e., by using only three I/Q images, the CID-Net produced high-quality images competing with those obtained by coherently compounding 31 RF images. Moreover, we showed that the CID-Net outperforms the straightforward architecture consisting in processing separately the real and imaginary parts of the I/Q signal, indicating thereby the importance of consistently processing the I/Q signals using a network that exploits the complex nature of such signal.