Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical Sensor and Biosensor Strategies for ROS/ RNS Detection in Biological Systems

This review summarises the main electrochemical sensors and biosensors developed for assessing the reactive oxygen (ROS) and nitrogen (RNS) species of interest in clinical and physiological analysis. Among these species, a special attention is focused on sensors for the detection of the highly reactive superoxide radicals O2. −, the very stable hydrogen peroxide H2O2, as well as the well-known RNS nitric oxide (NO.). The different methods adopted for the detection of each species are commented, considering both sensor and biosensors detection strategies.
Blockchain networks: Data structures of Bitcoin, Monero, Zcash, Ethereum, Ripple, and Iota

Blockchain is an emerging technology that has enabled many applications, from cryptocurrencies to digital asset management and supply chains. Due to this surge of popularity, analyzing the data stored on blockchains poses a new critical challenge in data science. To assist data scientists in various analytic tasks for a blockchain, in this tutorial, we provide a systematic and comprehensive overview of the fundamental elements of blockchain network models. We discuss how we can abstract blockchain data as various types of networks and further use such associated network abstractions to reap important insights on blockchains' structure, organization, and functionality.
MENYO-20k: A Multi-domain English-Yorùbá Corpus for Machine Translation and Domain Adaptation

Massively multilingual machine translation (MT) has shown impressive capabilities, including zero and few-shot translation between low-resource language pairs. However, these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difﬁculty of evaluating MT models on low-resource pairs is often due the lack of standardized evaluation datasets. In this paper, we present MENYO-20k, the ﬁrst multi-domain parallel corpus for the low-resource Yor ` ub ´ a–English ( yo – en ) language pair with standardized train-test splits for benchmarking. We provide several neural MT (NMT) benchmarks on this dataset and compare to the performance of popular pre-trained (massively multilingual) MT models, showing that, in almost all cases, our simple benchmarks outperform the pre-trained MT models. A major gain of BLEU +9 . 9 and +8 . 6 ( en 2 yo ) is achieved in comparison to Facebook’s M2M-100 and Google multilingual NMT respectively when we use MENYO-20k to ﬁne-tune generic models.
Spin-orbit Energies in Etch-Confined Superconductor-Semiconductor Nanowires

J. D. S. Witt, ∗ G. C. Gardner, 3 C. Thomas, 3 T. Lindemann, 3 S. Gronin, 3 M. J. Manfra, 3 and D. J. Reilly 4, † ARC Centre of Excellence for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia Department of Physics and Astronomy, Purdue University, West Lafayette, Indiana, USA Microsoft Quantum Purdue, Purdue University, West Lafayette, Indiana, USA Microsoft Quantum Sydney, The University of Sydney, Sydney, NSW 2006, Australia (Dated: November 25, 2021)
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss

Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples equally without considering their individual impacts on the model. To address this, for the augmented samples from the same training example, we propose to assign different weights to them. We construct the maximal expected loss which is the supremum over any reweighted loss on augmented samples. Inspired by adversarial training, we minimize this maximal expected loss (MMEL) and obtain a simple and interpretable closed-form solution: more attention should be paid to augmented samples with large loss values (i.e., harder examples). Minimizing this maximal expected loss enables the model to perform well under any reweighting strategy. The proposed method can generally be applied on top of any data augmentation methods. Experiments are conducted on both natural language understanding tasks with token-level data augmentation, and image classification tasks with commonly-used image augmentation techniques like random crop and horizontal flip. Empirical results show that the proposed method improves the generalization performance of the model.
Manipulator-Independent Representations for Visual Imitation

Imitation learning is an effective tool for robotic learning tasks where specifying a reinforcement learning (RL) reward is not feasible or where the exploration problem is particularly difficult. Imitation, typically behavior cloning or inverse RL, derive a policy from a collection of first-person action-state trajectories. This is contrary to how humans and other animals imitate: we observe a behavior, even from other species, understand its perceived effect on the state of the environment, and figure out what actions our body can perform to reach a similar outcome. In this work, we explore the possibility of third-person visual imitation of manipulation trajectories, only from vision and without access to actions, demonstrated by embodiments different to the ones of our imitating agent. Specifically, we investigate what would be an appropriate representation method with which an RL agent can visually track trajectories of complex manipulation behavior -- non-planar with multiple-object interactions -- demonstrated by experts with different embodiments. We present a way to train manipulator-independent representations (MIR) that primarily focus on the change in the environment and have all the characteristics that make them suitable for cross-embodiment visual imitation with RL: cross-domain alignment, temporal smoothness, and being actionable. We show that with our proposed method our agents are able to imitate, with complex robot control, trajectories from a variety of embodiments and with significant visual and dynamics differences, e.g. simulation-to-reality gap.
Identifying Opioid Use Disorder from Longitudinal Healthcare Data using a Multi-stream Transformer

Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime. Analyzing longitudinal healthcare data is critical in addressing many real-world problems in healthcare. Leveraging the real-world longitudinal healthcare data, we propose a novel multi-stream transformer model called MUPOD for OUD identification. MUPOD is designed to simultaneously analyze multiple types of healthcare data streams, such as medications and diagnoses, by attending to segments within and across these data streams. Our model tested on the data from 392,492 patients with long-term back pain problems showed significantly better performance than the traditional models and recently developed deep learning models.
A Large-Scale Dataset for Benchmarking Elevator Button Segmentation and Character Recognition

Human activities are hugely restricted by COVID-19, recently. Robots that can conduct inter-floor navigation attract much public attention since they can substitute human workers to conduct the service work. However, current robots either depend on human assistance or elevator retrofitting, and fully autonomous inter-floor navigation is still not available. As the very first step of inter-floor navigation, elevator button segmentation and recognition hold an important position. Therefore, we release the first large-scale publicly available elevator panel dataset in this work, containing 3,718 panel images with 35,100 button labels, to facilitate more powerful algorithms on autonomous elevator operation. Together with the dataset, a number of deep learning based implementations for button segmentation and recognition are also released to benchmark future methods in the community. The dataset is available at https://github.com/zhudelong/elevator_button_recognition
A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research Beginners

Searching for papers from different academic databases is the most commonly used method by research beginners to obtain cross-domain technical solutions. However, it is usually inefficient and sometimes even useless because traditional search methods neither consider knowledge heterogeneity in different domains nor build the bottom layer of search, including but not limited to the characteristic description text of target solutions and solutions to be excluded. To alleviate this problem, a novel paper recommendation method is proposed herein by introducing"master-slave"domain knowledge graphs, which not only help users express their requirements more accurately but also helps the recommendation system better express knowledge. Specifically, it is not restricted by the cold start problem and is a challenge-oriented method. To identify the rationality and usefulness of the proposed method, we selected two cross-domains and three different academic databases for verification. The experimental results demonstrate the feasibility of obtaining new technical papers in the cross-domain scenario by research beginners using the proposed method. Further, a new research paradigm for research beginners in the early stages is proposed herein.
Estimating California’s Solar and Wind Energy Production using Computer Vision Deep Learning Techniques on Weather Images

In pursuit of a novel forecasting strategy for the energy market, we propose a ResNet-inspired model [6] which estimates solar and wind energy production using weather images. The model is designed to capture high-frequency details while producing realistically smooth energy production proﬁles. To this end, we show the value of including multiple weather images from times preceding the estimation time, and demonstrate that the model outperforms traditional deep learning techniques and alternative state-of-the-art computer vision methods. Training and testing are performed on a novel data set that focuses on the state of California and spans the year 2019. The dataset, which is sourced from NOAA [1] and CAISO [9], is a secondary contribution of this work. Finally, multiple topics in line with the motivation are proposed for future work.
Proposal for practical multidimensional quantum networks

A Quantum Internet, i.e., a global interconnection of quantum devices, is the long term goal of quantum communications, and has so far been based on two-dimensional systems (qubits). Recent years have seen a significant development of high-dimensional quantum systems (qudits). While qudits present higher photon information efficiency and robustness to noise, their use in quantum networks present experimental challenges due to the impractical resources required in high-dimensional quantum repeaters. Here, we show that such challenges can be met via the use of standard quantum optical resources, such as weak coherent states or weak squeezed states, and linear optics. We report a concrete design and simulations of an entanglement swapping scheme for three and four dimensional systems, showing how the network parameters can be tuned to optimize secret key rates and analysing the enhanced noise robustness at different dimensions. Our work significantly simplifies the implementation of high-dimensional quantum networks, fostering their development with current technology.
Predicting hyperlinks via hypernetwork loop structure

While links in simple networks describe pairwise interactions between nodes, it is necessary to incorporate hypernetworks for modeling complex systems with arbitrary-sized interactions. In this study, we focus on the hyperlink prediction problem in hypernetworks, for which the current state-of-art methods are latent-feature based. A practical algorithm via topological features, which can provide understandings of the organizational principles of hypernetworks, is still lacking. For simple networks, local clustering or loop reflects the correlations among nodes; therefore, loop-based link prediction algorithms have achieved accurate performance. Extending the idea to hyperlink prediction faces several challenges. For instance, what is an effective way of defining loops for prediction is not clear yet; besides, directly comparing topological statistics of variable-sized hyperlinks could introduce biases in hyperlink cardinality. In this study, we address the issues and propose a loop-based hyperlink prediction approach. First, we discuss and define the loops in hypernetworks; then, we transfer the loop features into a hyperlink prediction algorithm via a simple modified logistic regression. Numerical experiments on multiple real-world datasets demonstrate superior performance compared to the state-of-the-art methods.
Autotuning Benchmarking Techniques: A Roofline Model Case Study

Peak performance metrics published by vendors often do not correspond to what can be achieved in practice. It is therefore of great interest to do extensive benchmarking on core applications and library routines. Since DGEMM is one of the most used in compute-intensive numerical codes, it is typically highly vendor optimized and of great interest for empirical benchmarks.In this paper we show how to build a novel tool that autotunes the benchmarking process for the Roofline model. Our novel approach can efficiently and reliably find optimal configurations for any target hardware. Results of our tool on a range of hardware architectures and comparisons to theoretical peak performance are included.Our tool autotunes the benchmarks for the target architecture by deciding the optimal parameters through state space reductions and exhaustive search. Our core idea includes calculating the confidence interval using the variance and mean and comparing it against the current optimum solution. We can then terminate the evaluation process early if the confidence interval’s maximum is lower than the current optimum solution. This dynamic approach yields a search time improvement of up to 116.33x for the DGEMM benchmarking process compared to a traditional fixed sample-size methodology. Our tool produces the same benchmarking result with an error of less than 2% for each of the optimization techniques we apply, while providing a great reduction in search time. We compare these results against hand-tuned benchmarking parameters. Results from the memory-intensive TRIAD benchmark, and some ideas for future directions are also included.
SenseRT: A Streaming Architecture for Smart Building Sensors

Building Management Systems (BMSs) have evolved in recent years, in ways that require changes to existing network architectures that follow the store-then-analyse approach. The primary cause is the increasing deployment of a diverse range of cost-effective sensors and actuators in smart buildings that generate real-time streaming data. Any in-building system with a large number of sensors needs a framework for real-time data collection and concurrent stream processing from sensors connected using a range of networks. We present SenseRT, a system for managing and analysing in-building real-time streams of sensor data. SenseRT collects streams of real-time data from sensors connected using a range of network protocols. It supports concurrent modules simultaneously performing stream processing over real-time data, asynchronously and non-blocking, with results made available with minimal latency. We describe a prototype implementation deployed in two University department buildings, demonstrating its effectiveness.
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing

We introduce here the idea of Meta Learning for training EEG BCI decoders. Meta Learning is a way of training machine learning systems so they learn to learn. We apply here meta learning to a simple Deep Learning BCI architecture and compare it to transfer learning on the same architecture. Our Meta learning strategy operates by finding optimal parameters for the BCI decoder so that it can quickly generalise between different users and recording sessions - thereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset. Our approach increased motor imagery classification accuracy between 60 to 80%, outperforming other algorithms under the little-data condition. We believe that establishing the meta learning or learning-to-learn approach will help neural engineering and human interfacing with the challenges of quickly setting up decoders of neural signals to make them more suitable for daily-life.
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition

As Facial Expression Recognition (FER) systems become integrated into our daily lives, these systems need to prioritise making fair decisions instead of only aiming at higher individual accuracy scores. From surveillance systems, to monitoring the mental and emotional health of individuals, these systems need to balance the accuracy versus fairness trade-off to make decisions that do not unjustly discriminate against specific under-represented demographic groups. Identifying bias as a critical problem in facial analysis systems, different methods have been proposed that aim to mitigate bias both at data and algorithmic levels. In this work, we propose the novel use of Continual Learning (CL), in particular, using Domain-Incremental Learning (Domain-IL) settings, as a potent bias mitigation method to enhance the fairness of Facial Expression Recognition (FER) systems. We compare different non-CL-based and CL-based methods for their performance and fairness scores on expression recognition and Action Unit (AU) detection tasks using two popular benchmarks, the RAF-DB and BP4D datasets, respectively. Our experimental results show that CL-based methods, on average, outperform other popular bias mitigation techniques on both accuracy and fairness metrics.
Model Validation Study for Central American Regional Electrical Interconnected System

The Central American Regional Interconnected Power System (SER) connects six countries: Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, and Panama, it is operated by the regional system operator Ente Operador Regional (EOR). Due to its geographical shape and layout of major transmission lines, SER has a weakly meshed grid, where disturbances can easily propagate, challenging its reliability. Having an accurate dynamic model is important for EOR when facing those reliability challenges. This paper describes interconnection-level model validation efforts for the SER and Mexico interconnected system. A detailed equivalent model of Mexico is incorporated in the existing SER planning model used by EOR. The resultant model is then validated using simulated dynamic contingency analysis and real system disturbance data. A fully automated suite of scripts is also developed and shared with EOR engineers. This work helps EOR improve their validation routine practices, to continuously improve SER dynamic model, and hence its reliability.
Inclusive photon production at forward rapidities using PMD in p-Pb collisions at $\sqrt{\it{s}_{\rm{NN}}} = 8.16$ TeV with ALICE

We report on the performance studies of two correction methods, namely the efficiency-purity method and the Bayesian unfolding method applied to the pseudorapidity distribution of photons. The pseudorapidity distribution of inclusive photons at forward rapidities in the range \mbox{\small $(2.3<\eta<3.9)$} in p\textendash Pb collisions at $\sqrt{\it{s}_{\rm{NN}}} = 8.16$ TeV is obtained with the HIJING Monte Carlo event generator. The simulated data samples were obtained from the Photon Multiplicity Detector (PMD) in ALICE.
Accessibility in Software Practice: A Practitioner’s Perspective

Being able to access software in daily life is vital for everyone, and thus accessibility is a fundamental challenge for software development. However, given the number of accessibility issues reported by many users, e.g., in app reviews, it is not clear if accessibility is widely integrated into current software projects and how software projects address accessibility issues. In this article, we report a study of the critical challenges and benefits of incorporating accessibility into software development and design. We applied a mixed qualitative and quantitative approach for gathering data from 15 interviews and 365 survey respondents from 26 countries across five continents to understand how practitioners perceive accessibility development and design in practice. We got 44 statements grouped into eight topics on accessibility from practitioners’ viewpoints and different software development stages. Our statistical analysis reveals substantial gaps between groups, e.g., practitioners have Direct vs. Indirect accessibility relevant work experience when they reviewed the summarized statements. These gaps might hinder the quality of accessibility development and design, and we use our findings to establish a set of guidelines to help practitioners be aware of accessibility challenges and benefit factors. We suggest development teams put accessibility as a first-class consideration throughout the software development process, and we also propose some remedies to resolve the gaps between groups and to highlight key future research directions to incorporate accessibility into software design and development.
On Energy Conversion in Port-Hamiltonian Systems

Port-Hamiltonian systems with two external ports are studied, together with the strategies and obstructions for conversion of energy from one port to the other. Apart from the cyclo-passivity properties, this turns out to be intimately related to the interconnection topology of the system. A prime source of motivation for energy conversion is thermodynamics, in particular the Carnot-Clausius heat engine theory about conversion of thermal into mechanical energy. This classical theory is extended to general port-Hamiltonian systems satisfying structural conditions on their topology. In particular, the operation of Carnot cycles is generalized. This is illustrated by the examples of a precursor to the Stirling engine and an electro-mechanical actuator. Finally, alternative energy conversion schemes for general port-Hamiltonian systems, such as energy-routers, are discussed from the same vantage point.
Screening p-Hackers: Dissemination Noise as Bait

We show that adding noise to data before making data public is effective at screening p-hacked findings: spurious explanations of the outcome variable produced by attempting multiple econometric specifications. Noise creates "baits'' that affect two types of researchers differently. Uninformed p-hackers who engage in data mining with no prior information about the true causal mechanism often fall for baits and report verifiably wrong results when evaluated with the original data. But informed researchers who start with an ex-ante hypothesis about the causal mechanism before seeing any data are minimally affected by noise. We characterize the optimal level of dissemination noise and highlight the relevant trade-offs in a simple theoretical model. Dissemination noise is a tool that statistical agencies (e.g., the US Census Bureau) currently use to protect privacy, and we show this existing practice can be repurposed to improve research credibility. The full paper can be found at https://arxiv.org/abs/2103.09164.