Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation

Optimal performance is desired for decision-making in any field with binary classifiers and diagnostic tests, however common performance measures lack depth in information. The area under the receiver operating characteristic curve (AUC) and the area under the precision recall curve are too general because they evaluate all decision thresholds including unrealistic ones. Conversely, accuracy, sensitivity, specificity, positive predictive value and the F1 score are too specific—they are measured at a single threshold that is optimal for some instances, but not others, which is not equitable. In between both approaches, we propose deep ROC analysis to measure performance in multiple groups of predicted risk (like calibration), or groups of true positive rate or false positive rate. In each group, we measure the group AUC (properly), normalized group AUC, and averages of: sensitivity, specificity, positive and negative predictive value, and likelihood ratio positive and negative. The measurements can be compared between groups, to whole measures, to point measures and between models. We also provide a new interpretation of AUC in whole or part, as balanced average accuracy, relevant to individuals instead of pairs. We evaluate models in three case studies using our method and Python toolkit and confirm its utility.
A Generalized Tunneling Current Formula for Metal/Insulator Heterojunctions under Large Bias and Finite Temperature

The Fowler-Nordheim tunneling current formula has been widely used in the design of devices based on metal/insulator (metal/semiconductor) heterojunctions with triangle potential barriers, such as the flash memory. Here we adopt the model that was used to derive the Landauer formula at finite temperature, the nearly-free electron approximation to describe the electronic states in semi-infinite metal electrode and the Wentzel-Kramers-Brillouin (WKB) approximation to describe the transmission coefficient, and derive a tunneling current formula for metal/insulator heterojunctions under large bias and electric field. In contrast to the Fowler-Nordheim formula which is the limit at zero temperature, our formula is generalized to the finite temperature (with the thermal excitation of electrons in metal electrode considered) and the potential barriers beyond triangle ones, which may be used for the design of more complicated heterojunction devices based on the carrier tunneling.
Continuous Prediction of Lower-Limb Kinematics From Multi-Modal Biomedical Signals

The fast-growing techniques of measuring and fusing multi-modal biomedical signals enable advanced motor intent decoding schemes of lower-limb exoskeletons, meeting the increasing demand for rehabilitative or assistive applications of take-home healthcare. Challenges of exoskeletons’ motor intent decoding schemes remain in making a continuous prediction to compensate for the hysteretic response caused by mechanical transmission. In this paper, we solve this problem by proposing an ahead-of-time continuous prediction of lower-limb kinematics, with the prediction of knee angles during level walking as a case study. Firstly, an end-to-end kinematics prediction network(KinPreNet),1 consisting of a feature extractor and an angle predictor, is proposed and experimentally compared with features and methods traditionally used in ahead-of-time prediction of gait phases. Secondly, inspired by the electromechanical delay(EMD), we further explore our algorithm’s capability of compensating response delay of mechanical transmission by validating the performance of the different sections of prediction time. And we experimentally reveal the time boundary of compensating the hysteretic response. Thirdly, a comparison of employing EMG signals or not is performed to reveal the EMG and kinematic signals’ collaborated contributions to the continuous prediction. During the experiments, EMG signals of nine muscles and knee angles calculated from inertial measurement unit (IMU) signals are recorded from ten healthy subjects. Our algorithm can predict knee angles with the averaged RMSE of 3.98 deg which is better than the 15.95-deg averaged RMSE of utilizing the traditional methods of ahead-of-time prediction. The best prediction time is in the interval of 27ms and 108ms. To the best of our knowledge, this is the first study of continuously predicting lower-limb kinematics in an ahead-of-time manner based on the electromechanical delay (EMD).
Prospects for measuring dark matter microphysics with observations of dwarf spheroidal galaxies

Dark matter annihilation in dwarf spheroidal (dSph) galaxies near the Milky Way has the potential to produce a detectable signature in gamma-rays. The amplitude of this signal depends on the dark matter density in a dSph, the dark matter particle mass, the number of photons produced in an annihilation, and the possibly velocity-dependent dark matter annihilation cross section. We argue that if the amplitude of the annihilation signal from multiple dSphs can be measured, it is possible to determine the velocity-dependence of the annihilation cross section. However, we show that doing so will require improved constraints on the dSph density profiles, including control of possible sources of systematic uncertainty. Making reasonable assumptions about future improvements, we make forecasts for the ability of current and future experiments — including Fermi, CTA and AMEGO — to constrain the dark matter annihilation velocity dependence.
Multi-Transmitter Coded Caching Networks With Transmitter-Side Knowledge of File Popularity

This work presents a new way of exploiting non-uniform file popularity in coded caching networks. Focusing on a fully-connected fully-interfering wireless setting with multiple cache-enabled transmitters and receivers, we show how non-uniform file popularity can be used very efficiently to accelerate the impact of transmitter-side data redundancy on receiver-side coded caching. This approach is motivated by the recent discovery that, under any realistic file-size constraint, having content appear in multiple transmitters can in fact dramatically boost the speed-up factor attributed to coded caching. We formulate an optimization problem that exploits file popularity to optimize the placement of files at the transmitters. Consequently, we propose a search algorithm that solves the problem at hand while reducing the variable search space significantly. We also prove an analytical performance upper bound, which is in fact met by our algorithm in the regime of many receivers. Our work reflects the benefits of allocating higher cache redundancy to more popular files, but also reflects a law of diminishing returns where for example very popular files may in fact benefit from minimum redundancy. In the end, this work reveals that in the context of coded caching, employing multiple transmitters can be a catalyst in fully exploiting file popularity, as it avoids various asymmetry complications that appear when file popularity is used to alter the receiver-side cache placement.
Modifying the service patterns of public transport vehicles to account for the COVID-19 capacity

As public transport operators try to resume their services, they have to operate under reduced capacities due to COVID-19. Because demand can exceed capacity at different areas and across different times of the day, drivers have to refuse passenger boardings at specific stops. Towards this goal, many public transport operators have modified their service routes by avoiding to serve stops with high passenger demand at specific times of the day. Given the urgent need to develop decision support tools that can prevent the overcrowding of vehicles, this study introduces a dynamic integer nonlinear program that proposes service patterns to individual vehicles that are ready to be dispatched. In addition to the objective of satisfying the imposed vehicle capacity due to COVID-19, the proposed service pattern model caters for the waiting time of passengers. Our model is tested in a bus line connecting the university of Twente with its surrounding cities demonstrating the improvement in terms of vehicle overcrowding, and analyzing the potential negative effects related to unserved passenger demand and excessive waiting times.
Examining the Modeling Framework of Crime Hotspot Models in Predictive Policing

Predictive policing has its roots in crime hotspot modeling. In this paper we give an example of what goes into mathematical crime hotspot modeling and show that the modeling assumptions perpetuate systemic racism in policing. The goal of this paper is to raise objections to this field of research, not on its mathematical merit, but on the scope of the problem formation. We do not make any suggestions about how to incorporate race into predictive policing models in order to make them less biased.
Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models

Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in the environment. Existing approaches assume that the possible goals are formalized as a conjunction in deterministic settings. In this paper, we develop a novel approach that is capable of recognizing temporally extended goals in Fully Observable Non-Deterministic (FOND) planning domain models, focusing on goals on finite traces expressed in Linear Temporal Logic (LTLf) and (Pure) Past Linear Temporal Logic (PLTLf). We empirically evaluate our goal recognition approach using different LTLf and PLTLf goals over six common FOND planning domain models, and show that our approach is accurate to recognize temporally extended goals at several levels of observability.
Uncertainty-Aware Self-Supervised Learning of Spatial Perception Tasks

We propose a general self-supervised learning approach for spatial perception tasks, such as estimating the pose of an object relative to the robot, from onboard sensor readings. The model is learned from training episodes, by relying on: A continuous state estimate, possibly inaccurate and affected by odometry drift; and a detector, that sporadically provides supervision about the target pose. We demonstrate the general approach in three different concrete scenarios: a simulated robot arm that visually estimates the pose of an object of interest; a small differential drive robot using 7 infrared sensors to localize a nearby wall; an omnidirectional mobile robot that localizes itself in an environment from camera images. Quantitative results show that the approach works well in all three scenarios, and that explicitly accounting for uncertainty yields statistically significant performance improvements. Videos, datasets, and code to reproduce our results are available at: https://github.com/idsia-robotics/uncertainty-aware-ssl-spatial-perception.
MaskFaceGAN: High-Resolution Face Editing With Masked GAN Latent Code Optimization

Face editing represents a popular research topic within the computer vision and image processing communities. While significant progress has been made recently in this area, existing solutions: <inline-formula> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> are still largely focused on low-resolution images, <inline-formula> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> often generate editing results with visual artefacts, or <inline-formula> <tex-math notation="LaTeX">$(iii)$ </tex-math></inline-formula> lack fine-grained control over the editing procedure and alter multiple (entangled) attributes simultaneously, when trying to generate the desired facial semantics. In this paper, we aim to address these issues through a novel editing approach, called MaskFaceGAN that focuses on local attribute editing. The proposed approach is based on an optimization procedure that directly optimizes the latent code of a pre-trained (state-of-the-art) Generative Adversarial Network (i.e., StyleGAN2) with respect to several constraints that ensure: <inline-formula> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> preservation of relevant image content, <inline-formula> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> generation of the targeted facial attributes, and <inline-formula> <tex-math notation="LaTeX">$(iii)$ </tex-math></inline-formula> spatially–selective treatment of local image regions. The constraints are enforced with the help of an (differentiable) attribute classifier and face parser that provide the necessary reference information for the optimization procedure. MaskFaceGAN is evaluated in extensive experiments on the FRGC, SiblingsDB-HQf, and XM2VTS datasets and in comparison with several state-of-the-art techniques from the literature. Our experimental results show that the proposed approach is able to edit face images with respect to several local facial attributes with unprecedented image quality and at high-resolutions (<inline-formula> <tex-math notation="LaTeX">$1024\times 1024$ </tex-math></inline-formula>), while exhibiting considerably less problems with attribute entanglement than competing solutions. The source code is publicly available from: <uri>https://github.com/MartinPernus/MaskFaceGAN</uri>.
Adversarially Optimized Mixup for Robust Classification

Mixup is a procedure for data augmentation that trains networks to make smoothly interpolated predictions between datapoints. Adversarial training is a strong form of data augmentation that optimizes for worst-case predictions in a compact space around each data-point, resulting in neural networks that make much more robust predictions. In this paper, we bring these ideas together by adversarially probing the space between datapoints, using projected gradient descent (PGD). The fundamental approach in this work is to leverage backpropagation through the mixup interpolation during training to optimize for places where the network makes unsmooth and incongruous predictions. Additionally, we also explore several modifications and nuances, like optimization of the mixup ratio and geometrical label assignment, and discuss their impact on enhancing network robustness. Through these ideas, we have been able to train networks that robustly generalize better; experiments on CIFAR-10 and CIFAR-100 demonstrate consistent improvements in accuracy against strong adversaries, including the recent strong ensemble attack AutoAttack. Our source code would be released for reproducibility.
Monolingual and Parallel Corpora for Kangri Low Resource Language

In this paper we present the dataset of Himachali low resource endangered language, Kangri (ISO 639-3xnr) listed in the United Nations Educational, Scientific and Cultural Organization (UNESCO). The compilation of kangri corpus has been a challenging task due to the non-availability of the digitalized resources. The corpus contains 1,81,552 Monolingual and 27,362 Hindi-Kangri Parallel corpora. We shared pre-trained kangri word embeddings. We also reported the Bilingual Evaluation Understudy (BLEU) score and Metric for Evaluation of Translation with Explicit ORdering (METEOR) score of Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) results for the corpus. The corpus is freely available for non-commercial usages and research. To the best of our knowledge, this is the first Himachali low resource endangered language corpus. The resources are available at (https://github.com/chauhanshweta/Kangri_corp us)
Coexistence of Communications and Cognitive MIMO Radar: Waveform Design and Prototype

New generation of radar systems will need to coexist with other radio frequency (RF) systems, anticipating their behavior and reacting appropriately to avoid interference. In light of this requirement, this paper designs, implements, and evaluates the performance of phase-only sequences (with constant power) for intelligent spectrum utilization using the custom built cognitive Multiple Input Multiple Output (MIMO) radar prototype. The proposed transmit waveforms avoid the frequency bands occupied by narrowband interferers or communication links, while simultaneously have a small cross-correlation among each other to enable their separability at the MIMO radar receiver. The performance of the optimized set of sequences obtained through solving a non-convex bi-objective optimization problem, is compared with the state-of-the-art counterparts, and its applicability is illustrated by the developed prototype. A realistic Long Term Evolution (LTE) downlink is used for the communications, and the real-time system implementation is validated and evaluated through the throughput calculations for communications and the detection performance measurement for the radar system.
From Taylor cones to surface dimples: How droplets modify electrohydrodynamic instabilities

Fluidic interfaces disintegrate under sufficiently strong electric fields, leading to electrohydrodynamic (EHD) tip streaming. Taylor cones, which emit charged droplets from the tip of a conical cusp, are among the most prominent and wellstudied examples of EHD instabilities. In liquid-liquid systems, more complex interface deformation modes than simple Taylor cones can be observed, with the interface being pushed away from the electrode, and additional cone structures emerging from the rim of the dimple. In this article, we investigate the mechanisms behind these deformation modes experimentally and numerically, and demonstrate that the presence of droplets triggers the dimple at the interface. In order to characterize the underlying processes, we replace the pin electrode by a hollow metallic needle with a prescribed electrolyte volume flow. The submerged electrospray introduces droplets of an aqueous KCl solution with varying ion concentrations into silicone oils with varying viscosities. By measuring the corresponding electric current and by optical investigation of the interface deformation, we study the system response to variations of the ionic concentration, viscosity, applied voltage as well as flow rate. The voltage between needle and liquid-liquid interface has a strong influence on the deformation, whereas the electrolyte flow rate only has a small influence. In addition, we observe that both the deflection as well as the current reach limiting values with increasing ion concentration and viscosity, which we explain based on a scaling relationship for the size of the droplets. In addition to the experiments, we use a finite element solver and compute the charge transport due to the droplets in the oil phase as an advection-diffusion process. Further, we compute the electric potential distribution, flow field and interface deformation. After calibration of our model with particle tracking velocimetry data of the flow inside the oil phase, we reproduce the experimentally observed dimple at the liquid-liquid interface. By including a relationship between the electric field at the pin electrode and the emitted electrospray current, we are able to identify the space charge as the cause of the experimentally observed limiting behavior of the interface deflection and the electric current. In total, this work highlights the importance of charged droplets for the complex dynamic modes observed when a liquid-liquid interface is exposed to a local electric field.
High-precision quantum transmittometry of DNA and methylene-blue using a frequency-entangled twin-photon beam in type-I SPDC

Using the coincidence-count (CC) measurement of the generated frequency-entangled twin-photons beam (TWB) via the process of type-I spontaneous parametric-down conversion (SPDC) in BBO nonlinear crystal (NLC), we have precisely measured the transmittance of very diluted Rabbitand Human-DNA, Methylene-Blue (MB), as a disinfectant, and thin-film multilayer at near IR wavelength 810nm with an accuracy in order of %0.01 due to the quantum correlation, while accuracy of classical-like measurement, single-count (SC), is in order of %0.1 in our setup. Moreover, using quantum measurement of the transmittance, the different types of DNA with the same concentration, and also very diluted (in order of pg/μl) different concentrations of DNA and MB solutions are distinguished and detected with high-reliability. Interestingly, in case of Human-DNA samples in contrast to our classical-like measurement we could precisely detect and distinguish two very diluted concentrations 0.01ng/μl and 0.1ng/μl with high reliability while commercial standard spectrometer device of our DNA-manufacturer never could detect and distinguish them. Surprisingly, measurement on the thin-film multilayer illustrates that the introduced method in this work might be performed to cancer/brain tissues or Stem cells for cancer therapy, and may hopefully open a pave and platform for non-invasive quantum diagnosis in future. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Characterising Communities of Twitter Users Who Posted Vaccines Related Tweets by Information Sources

Background: Measuring what and how information generates and propagates in online communities such as Twitter user communities make a distinguishable characteristic over the of demographical region and it helps the public health organization to take appropriate decision to develop the public health system. Objective: We formed community structure based on web page credibility and we measured the types of information for characterizing communities of tweeter users who posted about tweets related to vaccine. Methods: We performed the experiment on only Twitter data (tweets) regarding vaccine. The duration of data collection was between 17 January 2017 and 14 March 2018. We formulated cluster based on the information on its contents and sources it resides (i.e., website domains). We only focused the topics which were related to vaccine. To detect the structure and network of community, we applied Louvain community algorithm along with Random walks called Info map method over vaccines related tweeter user. We defined the communities based on various measures derived from the information shared by Twitter users. Representations and visualizations of the communities based on these derived measures help the public health organization to make understand the possible cause of the rejecting the safety and efficacy of
Doubly charged Higgs production at future ep colliders

The Higgs sector of the standard model can be extended by introducing an Higgs triplet Δ to generate tiny neutrino masses in the framework of the type-II seesaw mechanism. In this paper, we study the pair production of the introduced Higgs triplet at future colliders. The corresponding production cross sections via the vector boson fusion process at the FCC-ep and ILC FCC are predicted, where the production of a pair of doubly charged Higgs is found to be dominant and then used to investigate the collider phenomenology of the Higgs triplet. Depending on the size of the Higgs triplet vacuum expectation value, the doubly charged Higgs may decay into a pair of same-sign charged leptons or a pair of same-sign W bosons. To explore the discovery potential of the doubly charged Higgs at future colliders, we discuss these two decay scenarios in detail and show their detection sensitivity based on the mass of the doubly charged Higgs.