Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibrated simplex-mapping classification

We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers are important in many applications because, in addition to the prediction of mere class labels, they also yield a confidence level for each of their predictions. In essence, the training of our classifier proceeds in two steps. In a first step, the training data is represented in a latent space whose geometry is induced by a regular (n − 1)-dimensional simplex, n being the number of classes. We design this representation in such a way that it well reflects the feature space distances of the datapoints to their own- and foreign-class neighbors. In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data. With this latent-space representation, our calibrated classifier is readily defined. We rigorously establish its core theoretical properties and benchmark its prediction and calibration properties by means of various synthetic and real-world data sets from different application domains.
Radion dynamics, heavy Kaluza-Klein resonances and gravitational waves

We study the confinement/deconfinement phase transition of the radion field in a warped model with a polynomial bulk potential. The backreaction of the radion on the metric is taken into account by using the superpotential formalism, while the radion effective potential is obtained from a novel formulation which can incorporate the backreaction. The phase transition leads to a stochastic gravitational wave background that depends on the energy scale of the first Kaluza-Klein resonance, $m_{\textrm{KK}}$. This work completes previous studies in the following aspects: i) we detail the evaluation of the radion spectrum; ii) we report on the mismatches between the thick wall approximation and the numerical bounce solution; iii) we include a suppression factor in the spectrum of sound waves accounting for their finite lifetime; and, iv) we update the bound on $m_{\textrm{KK}}$ in view of the O3 LIGO and Virgo data. We find that the forthcoming gravitational wave interferometers can probe scenarios where $m_{\textrm{KK}} \lesssim 10^9$ TeV, while the O3-run bounds rule out warped models with $10^4 \textrm{TeV} \lesssim m_{\textrm{KK}} \lesssim 10^7$ TeV exhibiting an extremely strong confinement/deconfinement phase transition.
An Optimization Approach for a Robust and Flexible Control in Collaborative Applications

In Human-Robot Collaboration, the robot operates in a highly dynamic environment. Thus, it is pivotal to guarantee the robust stability of the system during the interaction but also a high flexibility of the robot behavior in order to ensure safety and reactivity to the variable conditions of the collaborative scenario.In this paper we propose a control architecture capable of maximizing the flexibility of the robot while guaranteeing a stable behavior when physically interacting with the environment. This is achieved by combining an energy tank based variable admittance architecture with control barrier functions. The proposed architecture is experimentally validated on a collaborative robot.
ILoSA: Interactive Learning of Stiffness and Attractors

Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives. This paper studies how to learn variable impedance policies where both the Cartesian stiffness and the attractor can be learned from human demonstrations and corrections with a user-friendly interface. The presented framework, named ILoSA, uses Gaussian Processes for policy learning, identifying regions of uncertainty and allowing interactive corrections, stiffness modulation and active disturbance rejection. The experimental evaluation of the framework is carried out on a Franka-Emika Panda in four separate cases with unique force interaction properties: 1) pulling a plug wherein a sudden force discontinuity occurs upon successful removal of the plug, 2) pushing a box where a sustained force is required to keep the robot in motion, 3) wiping a whiteboard in which the force is applied perpendicular to the direction of movement, and 4) inserting a plug to verify the usability for precision-critical tasks in an experimental validation performed with non-expert users.
FAtiMA Toolkit - Toward an effective and accessible tool for the development of intelligent virtual agents and social robots

More than a decade has passed since the development of FearNot! , an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this paper, we describe FAtiMA Toolkit, a collection of open-source tools that is designed to help researchers, game developers and roboticists incorporate a computational model of emotion and decision-making in their work. The toolkit was developed with the goal of making FAtiMA more accessible, easier to incorporate into different projects and more flexible in its capabilities for human-agent interaction, based upon the experience gathered over the years across different virtual environments and human-robot interaction scenarios. As a result, this work makes several different contributions to the field of Agent-Based Architectures. More precisely, FAtiMA Toolkit's library based design allows developers to easily integrate it with other frameworks, its meta-cognitive model affords different internal reasoners and affective components and its explicit dialogue structure gives control to the author even within highly complex scenarios. To demonstrate the use of FAtiMA Toolkit, several different use cases where the toolkit was successfully applied are described and discussed.
Detect anomalous quartic gauge couplings using isolation forest

The search of new physics (NP) beyond the Standard Model is one of the most important tasks of high energy physics. A common characteristic of the NP signals is that they are usually few and kinematically different. We use a model independent strategy to study the phenomenology of NP by directly picking out and studying the kinematically unusual events. For this purpose, the isolation forest (IF) algorithm is applied, which is found to be efficient in identifying the signal events of the anomalous quartic gauge couplings (aQGCs). The IF algorithm can also be used to constraint the coefficients of aQGCs. As a machine learning algorithm, the IF algorithm shows a good prospect in the future studies of NP. Electronic address: ycguo@lnnu.edu. cn Electronic address: 847495612@qq.com Electronic address: yangjichong@lnnu.edu. cn
Congruence properties of indices of triangular numbers multiple of other triangular numbers

For any non-square integer multiplier \(k\), there is an infinity of triangular numbers multiple of other triangular numbers. We analyze the congruence properties of indices \(\xi\) of triangular numbers multiple of triangular numbers. Remainders in congruence relations \(\xi\) modulo \(k\) come always in pairs whose sum always equal \((k-1)\), always include 0 and \((k-1)\), and only 0 and \((k-1)\) if \(k\) is prime, or an odd power of a prime, or an even square plus one or an odd square minus one or minus two. If the multiplier \(k\) is twice the triangular number of \(n\), the set of remainders includes also \(n\) and \((n^{2}-1)\) and if \(k\) has integer factors, the set of remainders include multiples of a factor following certain rules. Algebraic expressions are found for remainders in function of \(k\) and its factors, with several exceptions. This approach eliminates those \(\xi\) values not providing solutions.
The Impact of COVID-19 on Stock Market Volatility in Pakistan

This paper examines the impact of coronavirus (COVID-19) on stock market volatility (SMV) in Pakistan by controlling the effect of exchange rate, interest rate and government/central bank interventions to combat the pandemic. We used the vector autoregressive (VAR) model over a sample period ranging from February 25, 2020 to December 7, 2020. We find that a shock to total daily coronavirus cases in Pakistan lead to a significant increase in SMV. This result is aligned with a vast literature on pandemics and investors uncertainty and remains robust to several robustness checks applied in our analysis.
The star catalogue of Wilhelm IV, Landgraf von Hessen-Kassel

We analyse a manuscript star catalogue by Wilhem IV, Landgraf von Hessen-Kassel from 1586. From measurements of altitudes and of angles between stars, given in the catalogue, we find that the measurement accuracy averages 26″ for eight fundamental stars, compared to 49″ of the measurements by Brahe. The computation in converting altitudes to declinations and angles between stars to celestial positions is very accurate, with errors that are negligible with respect to the measurement errors. Due to an offset in the position of the vernal equinox, the positional error of the catalogue is slightly worse than that of Brahe’s catalogue; however, when a correction is made for the offset, which was known to 17th century astronomers, the catalogue is more accurate than that of Brahe by a factor of two. We provide machine-readable tables of the catalogue.
The Age of Westerlund 1 Revisited

The cluster Westerlund 1 (Wd1) is host to a large variety of post-main-sequence (MS) massive stars. The simultaneous presence of these stars can only be explained by stellar models if the cluster has a finely tuned age of 4–5 Myr, with several published studies independently claiming ages within this range. At this age, stellar models predict that the cool supergiants (CSGs) should have luminosities of log(L/L⊙)≈5.5 , close to the empirical luminosity limit. Here, we test that prediction using archival data and new photometry from Stratospheric Observatory for Infrared Astronomy to estimate bolometric luminosities for the CSGs. We find that these stars are on average 0.4 dex too faint to be 5 Myr old, regardless of which stellar evolutionary model is used, and instead are indicative of a much older age of 10.4−1.2+1.3 Myr. We argue that neither systematic uncertainties in the extinction law nor stellar variability can explain this discrepancy. In reviewing various independent age estimates of Wd1 in the literature, we first show that those based on stellar diversity are unreliable. Second, we reanalyze Wd1's pre-MS stars employing the Damineli extinction law, finding an age of 7.2−2.3+1.1 Myr; older than that of previous studies, but which is vulnerable to systematic errors that could push the age close to 10 Myr. However, there remains significant tension between the CSG age and that inferred from the eclipsing binary W13. We conclude that stellar evolutionary models cannot explain Wd1 under the single-age paradigm. Instead, we propose that the stars in the Wd1 region formed over a period of several megayears.
Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.
Influence of gravitational waves upon light. Part I. Null geodesics, radar distance and frequency shift

We explore different facets of the action of linearized gravitational waves in Minkowski spacetime background upon light, under the electromagnetic geometrical optics limit, covering the main aspects: light trajectory perturbations, radar distance and light frequency shift. For this purpose, we consider observers comoving with the transverse traceless gauge coordinates. We compute the parametrized null geodesics exchanged between two of these observers, presenting explicitly the constants of motion as functions of observables, determining therefrom both the radar distance between the observers and the electromagnetic round-trip frequency shift caused by the gravitational wave. Also, a comparison is made between these results and what one would obtain by using a frequently adopted hybrid model in which the spatial trajectory of light is unchanged. Finally, we revisit and provide an explanation, resorting to the constancy of the phase along a light ray, to the fundamental puzzling question of how one is able to detect gravitational waves by means of interferometry if both light wavelength and detector arms are stretched.
Large-amplitude Prominence Oscillations following Impact by a Coronal Jet

Observational evidence shows that coronal jets can hit prominences and set them in motion. The impact leads to large-amplitude oscillations (LAOs) in the prominence. In this paper, we attempt to understand this process via 2.5D MHD numerical experiments. In our model, the jets are generated in a sheared magnetic arcade above a parasitic bipolar region located in one of the footpoints of the filament channel (FC) supporting the prominence. The shear is imposed at velocities not far above the observed photospheric values; this leads to a multiple reconnection process, as obtained in previous jet models. Both a fast Alfvénic perturbation and a slower supersonic front preceding a plasma jet are issued from the reconnection site; in the later phase, a more violent (eruptive) jet is produced. The perturbation and jets run along the FC; they are partially reflected at the prominence, and partially transmitted through it. This results in a pattern of counter-streaming flows along the FC, and oscillations in the prominence. The oscillations are LAOs (i.e., with amplitudes above 10 km s−1) in some areas of the prominence, both in the longitudinal and transverse directions. In some field lines, the impact is so strong that the prominence mass is brought out of the dip and down to the chromosphere along the FC. Two cases are studied, with respect to arcades at different heights above the parasitic bipolar region, leading to different heights for the region of the prominence perturbed by the jets. The obtained oscillation amplitudes and periods are in general agreement with the observations.
Pericentric passage-driven star formation in satellite galaxies and their hosts: CLUES from local group simulations

Local Group satellite galaxies show a wide diversity of star formation histories (SFHs) whose origin is yet to be fully understood. Using hydrodynamical simulations from the Constrained Local UniversE project, we study the SFHs of satellites of Milky Way-like galaxies in a cosmological context: while in the majority of the cases the accretion onto their host galaxy causes the satellites to lose their gas, with a subsequent suppression in SF, in about 25$\%$ of our sample we observe a clear enhancement of SF after infall. Peaks in SF clearly correlate with the satellite pericentric passage around its host and, in one case, with a satellite-satellite interaction. We identify two key ingredients that result in enhanced SF after infall: galaxies must enter the host's virial radius with a reservoir of cold gas $M_{\rm gas,inf}/M_{\rm vir,inf}\gtrsim 10^{-2}$ and with a minimum pericentric distance $\gtrsim$10 kpc (mean distance $\sim$50 kpc for the full sample), in order to form new stars due to compression of cold gas at pericentric passage. On the other hand, satellites that infall with little gas or whose pericentric distance is too small, have their gas ram-pressure stripped and subsequent SF quenched. The pericentric passage of satellites likewise correlates with SF peaks in their hosts, suggesting that this mechanism induces bursts of SF in satellites and central galaxies alike, in agreement with recent studies of our Galaxy's SFH. Our findings can explain the recently reported multiple stellar populations observed in dwarf galaxies such as Carina and Fornax, and should be taken into account in semi-analytic models of galaxy formation and satellite quenching.
Small sample spaces for Gaussian processes

It is known that the membership in a given reproducing kernel Hilbert space (RKHS) of the samples of a Gaussian process $X$ is controlled by a certain nuclear dominance condition. However, it is less clear how to identify a"small"set of functions (not necessarily a vector space) that contains the samples. This article presents a general approach for identifying such sets. We use scaled RKHSs, which can be viewed as a generalisation of Hilbert scales, to define the sample support set as the largest set which is contained in every element of full measure under the law of $X$ in the $\sigma$-algebra induced by the collection of scaled RKHS. This potentially non-measurable set is then shown to consist of those functions that can be expanded in terms of an orthonormal basis of the RKHS of the covariance kernel of $X$ and have their squared basis coefficients bounded away from zero and infinity, a result suggested by the Karhunen-Lo\`{e}ve theorem.
Robust market-adjusted systemic risk measures

In this note we consider a system of financial institutions and study systemic risk measures in the presence of a financial market and in a robust setting, namely, where no reference probability is assigned. We obtain a dual representation for convex robust systemic risk measures adjusted to the financial market and show its relation to some appropriate noarbitrage conditions.
Fully Unsupervised Person Re-Identification via Selective Contrastive Learning

Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. Existing fully supervised person ReID methods usually suffer from poor generalization capability caused by domain gaps. Unsupervised person ReID has attracted a lot of attention recently, because it works without intensive manual annotation and thus shows great potential in adapting to new conditions. Representation learning plays a critical role in unsupervised person ReID. In this work, we propose a novel selective contrastive learning framework for fully unsupervised feature learning. Specifically, different from traditional contrastive learning strategies, we propose to use multiple positives and adaptively selected negatives for defining the contrastive loss, enabling to learn a feature embedding model with stronger identity discriminative representation. Moreover, we propose to jointly leverage global and local features to construct three dynamic memory banks, among which the global and local ones are used for pairwise similarity computation and the mixture memory bank are used for contrastive loss definition. Experimental results demonstrate the superiority of our method in unsupervised person ReID compared with the state of the art. Our code is available at https://github.com/pangbo1997/Unsup_ReID. git.
VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials

This review summarizes the recent research efforts and developments in nanomaterials for sensing volatile organic compounds (VOCs). The discussion focuses on key materials such as metal oxides (e.g., ZnO, SnO2, TiO2 WO3), conductive polymers (e.g., polypyrrole, polythiophene, poly(3,4-ethylenedioxythiophene)), and carbon-based materials (e.g., graphene, graphene oxide, carbon nanotubes), and their mutual combination due to their representativeness in VOCs sensing. Moreover, it delves into the main characteristics and tuning of these materials to achieve enhanced functionality (sensitivity, selectivity, speed of response, and stability). The usual synthesis methods and their advantages towards their integration with microsystems for practical applications are also remarked on. The literature survey shows the most successful systems include structured morphologies, particularly hierarchical structures at the nanometric scale, with intentionally introduced tunable “decorative impurities” or well-defined interfaces forming bilayer structures. These groups of modified or functionalized structures, in which metal oxides are still the main protagonists either as host or guest elements, have proved improvements in VOCs sensing. The work also identifies the need to explore new hybrid material combinations, as well as the convenience of incorporating other transducing principles further than resistive that allow the exploitation of mixed output concepts (e.g., electric, optic, mechanic).
Indirect Estimation of Vertical Ground Reaction Force from a Body-Mounted INS/GPS Using Machine Learning

Vertical ground reaction force (vGRF) can be measured by force plates or instrumented treadmills, but their application is limited to indoor environments. Insoles remove this restriction but suffer from low durability (several hundred hours). Therefore, interest in the indirect estimation of vGRF using inertial measurement units and machine learning techniques has increased. This paper presents a methodology for indirectly estimating vGRF and other features used in gait analysis from measurements of a wearable GPS-aided inertial navigation system (INS/GPS) device. A set of 27 features was extracted from the INS/GPS data. Feature analysis showed that six of these features suffice to provide precise estimates of 11 different gait parameters. Bagged ensembles of regression trees were then trained and used for predicting gait parameters for a dataset from the test subject from whom the training data were collected and for a dataset from a subject for whom no training data were available. The prediction accuracies for the latter were significantly worse than for the first subject but still sufficiently good. K-nearest neighbor (KNN) and long short-term memory (LSTM) neural networks were then used for predicting vGRF and ground contact times. The KNN yielded a lower normalized root mean square error than the neural network for vGRF predictions but cannot detect new patterns in force curves.