Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Mutual Information based Feature Selection by Boosting Unique Relevance

Mutual Information (MI) based feature selection makes use of MI to evaluate each feature and eventually shortlists a relevant feature subset, in order to address issues associated with high-dimensional datasets. Despite the effectiveness of MI in feature selection, we notice that many state-of-the-art algorithms disregard the so-called unique relevance (UR) of features, and arrive at a suboptimal selected feature subset which contains a non-negligible number of redundant features. We point out that the heart of the problem is that all these MIBFS algorithms follow the criterion of Maximize Relevance with Minimum Redundancy (MRwMR), which does not explicitly target UR. This motivates us to augment the existing criterion with the objective of boosting unique relevance (BUR), leading to a new criterion called MRwMR-BUR. Depending on the task being addressed, MRwMR-BUR has two variants, termed MRwMR-BUR-KSG and MRwMR-BUR-CLF, which estimate UR differently. MRwMR-BUR-KSG estimates UR via a nearest-neighbor based approach called the KSG estimator and is designed for three major tasks: (i) Classification Performance. (ii) Feature Interpretability. (iii) Classifier Generalization. MRwMR-BUR-CLF estimates UR via a classifier based approach. It adapts UR to different classifiers, further improving the competitiveness of MRwMR-BUR for classification performance oriented tasks. The performance of both MRwMR-BUR-KSG and MRwMR-BUR-CLF is validated via experiments using six public datasets and three popular classifiers. Specifically, as compared to MRwMR, the proposed MRwMR-BUR-KSG improves the test accuracy by 2% - 3% with 25% - 30% fewer features being selected, without increasing the algorithm complexity. MRwMR-BUR-CLF further improves the classification performance by 3.8%- 5.5% (relative to MRwMR), and it also outperforms three popular classifier dependent feature selection methods.
Digital cities and the spread of COVID-19: Characterizing the impact of non-pharmaceutical interventions in five cities in Spain

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.
Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data

Even though deep neural networks (DNNs) achieve state-of-the-art results for a number of problems involving genomic data, getting DNNs to explain their decision-making process has been a major challenge due to their black-box nature. One way to get DNNs to explain their reasoning for prediction is via attribution methods which are assumed to highlight the parts of the input that contribute to the prediction the most. Given the existence of numerous attribution methods and a lack of quantitative results on the ﬁdelity of those methods, selection of an attribution method for sequence-based tasks has been mostly done qualitatively. In this work, we take a step towards identifying the most faithful attribution method by proposing a computational approach that utilizes point mutations. Providing quantitative results on seven popular attribution methods, we ﬁnd Layerwise Relevance Propagation (LRP) to be the most appropriate one for translation initiation, with LRP identifying two important biological features for translation: the integrity of Kozak sequence as well as the detrimental effects of premature stop codons.
Interdiffusion in antiferromagnetic/ferromagnetic exchange coupled NiFe/IrMn/CoFe multilayer

Auger electron spectroscopy (AES) and Rutherford backscattering spectroscopy (RBS) analysis were carried out in order to study the extent of interdiffusion during thermal treatment of the pinned electrode (Ta/NiFe/Cu/NiFe/IrMn/CoFe) of the magnetic tunneling junction. From the concentration profile results from RBS and AES, a significant amount of Mn–CoFe interdiffusion was observed when the sample was annealed at 200 °C–400 °C under vacuum. The multilayer was completely intermixed at 400 °C, losing the exchange bias interaction between the IrMn and CoFe layers. It was demonstrated that the migration of Mn was enhanced by the preferential oxidation of Mn on the surface. In fact, when a thin layer of Ta for oxidation protection was deposited on top of the electrode, the Mn diffusion was minimal up to 300 °C. Our experiment suggests that in actual magnetic tunneling junctions, the Mn diffusion to the insulation layer could be enhanced by the presence of the free oxygen radicals in the insulation layer produ.
Capillary instability of a viscous liquid thread in a cylindrical tube

We study axisymmetric breakup and drop formation of a liquid thread confined in a cylindrical tube by using a long wave model. The limit with large viscosity contrast is investigated, consisting of a highly viscous core and a less viscous annulus. Satellite drop formation near pinching is found to be dependent on the location of the tube wall, while inertia is found to have little effect. It is also shown that main drops tend to form so called “plug with collar” structures when the tube wall is close to the thread interface so that rings of fluid are trapped inside the plugs. Finally, our numerical simulations show that a double cone structure occurs on the breakup and the cone angles are fixed, even though self-similar solutions may not exist.
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series

The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentation, etc. For single-event time series Euclidean Distance and Dynamic Time Warping distance are known to be extremely effective. However, for time series containing cyclical behaviors, the semantic meaningfulness of such comparisons is less clear. For example, on two separate days the telemetry from an athlete's workout routine might be very similar. However, on the second day she might have changed the order in which she did push-ups and squats, added a few repetitions of pull-ups, or completely omitted dumbbell curls. Any one of these minor changes would defeat existing time series distance measures. Some “bag-of-features” methods have been proposed to address this problem; however, we argue that in many cases, similarity is intimately tied to the shapes of subsequences within these longer time series. In such cases, summative features will lack discrimination ability. In this work we introduce PRCIS, which stands for Pattern Representation Comparison in Series. PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with “dictionaries”. We will demonstrate the utility of our ideas on diverse tasks and datasets.
Nonlocal correlation effects due to virtual spin-flip processes in itinerant electron ferromagnets

We present an ab initio method for eletcronic structure calculations, which accounts for the interaction of electrons and magnons in ferromagnets. While it is based on a many body perturbation theory we approximate numerically complex quantities with quantities from time dependent density functional theory. This results in a simple and affordable algorithm which allows us to consider more complex materials than those usually studied in this context ($3d$ ferromagnets) while still being able to account for the non-locality of the self energy. Furthermore, our approach allows for a relatively simple way to incorporate self-consistency. Our results are in a good agreement with experimental and theoretical findings for iron and nickel. Especially the experimental exchange splitting of nickel is predicted accurately within our theory. Additionally, we study the halfmetallic ferromagnet NiMnSb concerning its non-qusiparticle states appearing in the bandgap due to spin-flip excitations.
Possible Discrimination of Black Hole Origins from the Lensing Rate of DECIGO and B-DECIGO Sources

In this paper, we forecast the expected detection rates and redshift distributions of gravitationally lensed gravitational waves (GWs) from three different mass distributions of primordial black holes (PBHs) and two stellar formation models of astrophysical black holes (ABHs) in the context of the DECi-hertz Interferometer Gravitational wave Observatory (DECIGO) and its smaller-scale version B-DECIGO. It suggests that DECIGO will be able to detect 104–105 GW signals from such binary black holes each year and the event rate distributions for PBHs will differ from those for ABHs due to their different merger rate with respect to redshift. The large number of event rates makes 5–70 detections of lensed GW signals possible. After considering the gravitational lensing effect, the difference between the detection rates and distributions for PBHs and ABHs will be more significant. Therefore, this can be served as a complementary method to distinguish PBHs from ABHs.
Jointly Learning Visual and Auditory Speech Representations from Raw Data

We present RAVEn, a self-supervised multi-modal approach to jointly learn visual and auditory speech representations. Our pre-training objective involves encoding masked inputs, and then predicting contextualised targets generated by slowly-evolving momentum encoders. Driven by the inherent differences between video and audio, our design is asymmetric w.r. t. the two modalities' pretext tasks: Whereas the auditory stream predicts both the visual and auditory targets, the visual one predicts only the auditory targets. We observe strong results in low- and high-resource labelled data settings when fine-tuning the visual and auditory encoders resulting from a single pre-training stage, in which the encoders are jointly trained. Notably, RAVEn surpasses all self-supervised methods on visual speech recognition (VSR) on LRS3, and combining RAVEn with self-training using only 30 hours of labelled data even outperforms a recent semi-supervised method trained on 90,000 hours of non-public data. At the same time, we achieve state-of-the-art results in the LRS3 low-resource setting for auditory speech recognition (as well as for VSR). Our findings point to the viability of learning powerful speech representations entirely from raw video and audio, i.e., without relying on handcrafted features. Code and models are available at https://github.com/ahaliassos/raven.
The Hateful Memes Challenge Next Move

State-of-the-art image and text classiﬁcation models, such as Convolutional Neural Networks and Transform-ers, have long been able to classify their respective unimodal reasoning satisfactorily with accuracy close to or ex-ceeding human accuracy. However, images embedded with text, such as hateful memes, are hard to classify using unimodal reasoning when difﬁcult examples, such as benign confounders, are incorporated into the data set. We at-tempt to generate more labeled memes in addition to the Hateful Memes data set from Facebook AI, based on the framework of a winning team from the Hateful Meme Challenge. To increase the number of labeled memes, we explore semi-supervised learning using pseudo-labels for newly introduced, unlabeled memes gathered from the Memotion Dataset 7K. We ﬁnd that the semi-supervised learning task on unlabeled data required human intervention and ﬁltering and that adding a limited amount of new data yields no extra classiﬁcation performance.
Relativistic matter bispectrum of cosmic structures on the light cone

Upcoming surveys of cosmic structures will probe scales close to the cosmological horizon, which opens up new opportunities for testing the cosmological concordance model to high accuracy. In particular, constraints on the squeezed bispectrum could rule out the single-field hypothesis during inflation. However, the squeezed bispectrum is also sensitive to dynamical effects of general relativity as well as interactions of matter with residual radiation from the early Universe. In this paper, we present a relativistic simulation pipeline that includes these relativistic effects consistently. We produce light cones and calculate the observed number counts of cold dark matter for five redshift bins between z = 0.55-2.25. We compare the relativistic results against reference Newtonian simulations by means of angular power- and bispectra. We find that the dynamical relativistic effects scale roughly inversely proportional to the multipole in the angular power spectrum, with a maximum amplitude of 10% for ℓ ≲ 5. By using a smoothing method applied to the binned bispectrum we detect the Newtonian bispectrum with very high significance. The purely relativistic part of the matter bispectrum, obtained by subtracting the Newtonian bispectrum from the relativistic one, is detected with a significance of ∼ 3σ, mostly limited by cosmic variance. We find that the pure dynamical relativistic effects accounts for up to 3% and 10% of the total amplitude, respectively in the squeezed and equilateral limits. Our relativistic pipeline for modelling ultra-large scales yields gauge-independent results as we compute observables consistently on the past light cone, while the Newtonian treatment employs approximations that leave some residual gauge dependence. A gauge-invariant approach is required in order to meet the expected level of precision of forthcoming probes of cosmic structures on ultra-large scales.
Electroencephalography (EEG), electromyography (EMG) and eye-tracking for astronaut training and space exploration

The ongoing push to send humans back to the Moon and to Mars is giving rise to a wide range of novel technical solutions in support of prospective astronaut expeditions. Crew capsules for long-duration travel, prospective lunar surface habitats, and extravehicular activity (EVA) suits are a few of the advanced systems set to underpin future human space explorations. Such systems are in need of efficient human-machine control mechanisms and interaction interfaces that would ensure optimal work performance while safeguarding astronauts’ physical and mental well-being. Against this backdrop, the European Space Agency (ESA) has recently launched an investigation into unobtrusive interface technologies as a potential answer to such challenges. In theory, the application of these technologies could enable future crews to initiate actions without requiring any form of manual manipulation, resulting in a more intuitive, safe, and flexible control mechanism in numerous situations. Potential examples range from the control of robotic arms and rovers to the interaction with virtual and augmented reality applications. Three particular technologies have shown promise in this regard: EEG - based brain-computer interfaces (BCI) provide a non-invasive method of utilizing recorded electrical activity of a user’s brain, electromyography (EMG) enables monitoring of electrical signals generated by the user’s muscle contractions, and finally, eye tracking enables, for instance, the tracking of user’s gaze direction via camera recordings to convey commands. Beyond simply improving the usability of prospective technical solutions, our findings indicate that EMG, EEG, and eye-tracking could also serve to monitor and assess a variety of cognitive states, including attention, cognitive load, and mental fatigue of the user, while EMG could furthermore also be utilized to monitor the physical state of the astronaut. Such capabilities may well turn out to be important enablers for the success of future space expeditions. Indeed, monitoring cognitive states could, e.g. help guide adaptive automation, improve crew mental health and concentration via neurofeedback, indicate training success, or help astronauts to optimize the timing of their actions during future missions. In this paper, we elaborate on the key strengths and challenges of these three enabling technologies, and in light of ESA’s latest findings, we reflect on their applicability in the context of human space flight. Furthermore, a timeline of technological readiness is provided. In so doing, this paper feeds into the growing discourse on emerging technology and its role in paving the way for a human return to the Moon and expeditions beyond the Earth’s orbit.
Preliminary Mass Spectrometry of a Xenon Hollow Cathode

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An Approach for Inclusion of a Nonlocal Transition Model in a Parallel Unstructured Computational Fluid Dynamics Code

The implementation of an integral transition model in a parallel unstructured computational fluid dynamics code is described. In particular, an algorithm for gathering the nonlocal boundary layer values (momentum thickness and shape factor) from parallel distributed computational domains is presented. Transition modeling results are presented for a flat plate and for a low pressure turbine, covering a large variation of Reynolds numbers, Mach numbers, turbulence intensities, and incidence angles. Contrary to fully turbulent simulations, transitional predictions are in very good agreement with measurements. Furthermore, the computatiorcal overhead of transitional simulations is only 7% for one multigrid cycle.
Mathematical model of internal temperature profile of GaAs during rapid thermal annealing

In previous studies it was reported that GaAs samples which were rapid thermal annealed exhibit bright bands adjacent to the surfaces in cathodoluminescence images of the cross sections. It is possible that the presence and depth of these bright bands are related to thermal stresses in the GaAs resulting from thermal gradients during heating and cooling. To investigate this possibility, a one‐dimensional mathematical model was developed to predict the temperatures through the thickness of the GaAs. Calculations of the thermal stress field show that the thermal stresses do not correlate with the depth of the bright bands.
The new discontinuous Galerkin methods based numerical relativity program Nmesh

Interpreting gravitational wave observations and understanding the physics of astrophysical compact objects such as black holes or neutron stars requires accurate theoretical models. Here, we present a new numerical relativity computer program, called Nmesh, that has the design goal to become a next generation program for the simulation of challenging relativistic astrophysics problems such as binary black hole or neutron star mergers. In order to efficiently run on large supercomputers, Nmesh uses a discontinuous Galerkin method together with a domain decomposition and mesh refinement that parallelizes and scales well. In this work, we discuss the various numerical methods we use. We also present results of test problems such as the evolution of scalar waves, single black holes and neutron stars, as well as shock tubes. In addition, we introduce a new positivity limiter that allows us to stably evolve single neutron stars without an additional artificial atmosphere, or other more traditional limiters.
One-shot Machine Teaching: Cost Very Few Examples to Converge Faster

Artificial intelligence is to teach machines to take actions like humans. To achieve intelligent teaching, the machine learning community becomes to think about a promising topic named machine teaching where the teacher is to design the optimal (usually minimal) teaching set given a target model and a specific learner. However, previous works usually require numerous teaching examples along with large iterations to guide learners to converge, which is costly. In this paper, we consider a more intelligent teaching paradigm named one-shot machine teaching which costs fewer examples to converge faster. Different from typical teaching, this advanced paradigm establishes a tractable mapping from the teaching set to the model parameter. Theoretically, we prove that this mapping is surjective, which serves to an existence guarantee of the optimal teaching set. Then, relying on the surjective mapping from the teaching set to the parameter, we develop a design strategy of the optimal teaching set under appropriate settings, of which two popular efficiency metrics, teaching dimension and iterative teaching dimension are one. Extensive experiments verify the efficiency of our strategy and further demonstrate the intelligence of this new teaching paradigm.
Egocentric Video Task Translation

Different video understanding tasks are typically treated in isolation, and even with distinct types of curated data (e.g., classifying sports in one dataset, tracking animals in another). However, in wearable cameras, the immersive egocentric perspective of a person engaging with the world around them presents an interconnected web of video understanding tasks—hand-object manipulations, navigation in the space, or human-human interactions—that unfold continuously, driven by the person's goals. We argue that this calls for a much more unified approach. We propose EgoTask Translation (EgoT2), which takes a collection of models optimized on separate tasks and learns to translate their outputs for improved performance on any or all of them at once. Unlike traditional transfer or multi-task learning, EgoT2's “flipped design” entails separate task-specific backbones and a task translator shared across all tasks, which captures synergies between even heterogeneous tasks and mitigates task competition. Demonstrating our model on a wide array of video tasks from Ego4D, we show its advantages over existing transfer paradigms and achieve top-ranked results on four of the Ego4D 2022 benchmark challenges.11Project webpage: https://vision.cs. utexas.edu/projects/egot2/.