Miracle-dz/UniFAR-FTUs huggingface dataset:
Metric learning: cross-entropy vs. pairwise losses

Recently, substantial research efforts in Deep Metric Learning (DML) focused on designing complex pairwise-distance losses and convoluted sample-mining and implementation strategies to ease optimization. The standard cross-entropy loss for classification has been largely overlooked in DML. On the surface, the cross-entropy may seem unrelated and irrelevant to metric learning as it does not explicitly involve pairwise distances. However, we provide a theoretical analysis that links the cross-entropy to several well-known and recent pairwise losses. Our connections are drawn from two different perspectives: one based on an explicit optimization insight; the other on discriminative and generative views of the mutual information between the labels and the learned features. First, we explicitly demonstrate that the cross-entropy is an upper bound on a new pairwise loss, which has a structure similar to various pairwise losses: it minimizes intra-class distances while maximizing inter-class distances. As a result, minimizing the cross-entropy can be seen as an approximate bound-optimization (or Majorize-Minimize) algorithm for minimizing this pairwise loss. Second, we show that, more generally, minimizing the cross-entropy is actually equivalent to maximizing the mutual information, to which we connect several well-known pairwise losses. These findings indicate that the cross-entropy represents a proxy for maximizing the mutual information -- as pairwise losses do -- without the need for complex sample-mining and optimization schemes. Furthermore, we show that various standard pairwise losses can be explicitly related to one another via bound relationships. Our experiments over four standard DML benchmarks (CUB200, Cars-196, Stanford Online Product and In-Shop) strongly support our findings. We consistently obtained state-of-the-art results, outperforming many recent and complex DML methods.
Tactic Learning and Proving for the Coq Proof Assistant

We present a system that utilizes machine learning for tactic proof search in the Coq Proof Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts appropriate tactics and finds proofs in the form of tactic scripts. To do this, it learns from previous tactic scripts and how they are applied to proof states. The performance of the system is evaluated on the Coq Standard Library. Currently, our predictor can identify the correct tactic to be applied to a proof state 23.4% of the time. Our proof searcher can fully automatically prove 39.3% of the lemmas. When combined with the CoqHammer system, the two systems together prove 56.7% of the library's lemmas.
WearID: Wearable-Assisted Low-Effort Authentication to Voice Assistants using Cross-Domain Speech Similarity

Due to the open nature of voice input, voice assistant (VA) systems (e.g., Google Home and Amazon Alexa) are under a high risk of sensitive information leakage (e.g., personal schedules and shopping accounts). Though the existing VA systems may employ voice features to identify users, they are still vulnerable to various acoustic attacks (e.g., impersonation, replay and hidden command attacks). In this work, we focus on the security issues of the emerging VA systems and aim to protect the users' highly sensitive information from these attacks. Towards this end, we propose a system, WearID, which uses an off-the-shelf wearable device (e.g., a smartwatch or bracelet) as a secure token to verify the user's voice commands to the VA system. In particular, WearID exploits the readily available motion sensors from most wearables to describe the command sound in vibration domain and check the received command sound across two domains (i.e., wearable's motion sensor vs. VA device's microphone) to ensure the sound is from the legitimate user.
Identification of Prebiotic Molecules Containing Peptide-like Bonds in a Hot Molecular Core, G10.47+0.03

After hydrogen, oxygen, and carbon, nitrogen is one of the most chemically active species in the interstellar medium. Nitrogen-bearing molecules are very important as they are actively involved in the formation of biomolecules. Therefore, it is essential to look for nitrogen-bearing species in various astrophysical sources, specifically around high-mass star-forming regions where the evolutionary history is comparatively poorly understood. In this paper, we report on the observation of three potential prebiotic molecules, namely, isocyanic acid (HNCO), formamide (NH2CHO), and methyl isocyanate (CH3NCO), which contain peptide-like bonds (–NH–C(=O)–) in a hot molecular core, G10.47 + 0.03 (hereafter, G10). Along with the identification of these three complex nitrogen-bearing species, we speculate on their spatial distribution in the source and discuss their possible formation pathways under such conditions. A rotational diagram method under local thermodynamic equilibrium has been employed to estimate the excitation temperature and the column density of the observed species. The Markov Chain Monte Carlo method was used to obtain the best-suited physical parameters of G10 as well as line properties of some species. We also determined the hydrogen column density and the optical depth for a different continuum observed in various frequency ranges. Finally, based on these observational results, we have constructed a chemical model to explain the observational findings. We found that HNCO, NH2CHO, and CH3NCO are chemically linked with each other.
Sun–Heliosphere Observation-based Ionization Rates Model

The solar wind (SW) and the extreme ultraviolet (EUV) radiation modulate fluxes of interstellar and heliospheric particles inside the heliosphere both in time and in space. Understanding this modulation is necessary to correctly interpret measurements of particles of interstellar origin inside the heliosphere. We present a revision of heliospheric ionization rates and provide the Sun–Heliosphere Observation-based Ionization Rates model based on the currently available data. We calculate the total ionization rates using revised SW and solar EUV data. We study the in-ecliptic variation of the SW parameters, the latitudinal structure of the SW speed and density, and the reconstruction of the photoionization rates. The revision most affects the SW out of the ecliptic plane during solar maximum and the estimation of the photoionization rates, the latter due to a change of the reference data. The revised polar SW is slower and denser during the solar maximum of solar cycle (SC) 24. The current estimated total ionization rates are higher than the previous ones for H, O, and Ne, and lower for He. The changes for the in-ecliptic total ionization rates are less than 10% for H and He, up to 20% for O, and up to 35% for Ne. Additionally, the changes are not constant in time and vary as a function of time and latitude.
Intermediate-mass Early-type Disk Galaxies in the Virgo Cluster. II. Near-Infrared Spectra and Evidence for Differences in Evolution

We discuss near-infrared (NIR) slit spectra of six early-type disk galaxies in the Virgo Cluster that were examined previously at visible/red wavelengths. Despite having similar intrinsic luminosities, colors, and morphologies, the NIR spectrophotometric properties of these galaxies indicate that they are not a homogeneous ensemble differing only in terms of luminosity-weighted age and metallicity. While the depth of the CO(2,0) band is consistent with the centers of these galaxies having solar or slightly sub-solar luminosity-weighted metallicities, galaxy-to-galaxy variations in the depth of the Na i 2.21 μm doublet are found, with Na i2.21 μm lines in three galaxies (NGC 4491, NGC 4584, and NGC 4620) that are deeper than those predicted for a solar chemical mixture and a solar-neighborhood mass function. In contrast, the Ca i2.26 μm triplet shows good galaxy-to-galaxy agreement but tends to be deeper than the model prediction. Considering the depth of the NaD lines, the deep Na i2.21 μm lines are tentatively attributed to a bottom-heavy mass function. This is counter to observed trends between mass function slope and velocity dispersion, and so the possibility of a super-solar [Na/Fe] is also discussed. Two of the three galaxies with deep Na i 2.21 μm (NGC 4584 and NGC 4620) have Sérsic exponents that are consistent with a classical bulge. As for NGC 4491, its central NIR spectrum contains prominent emission lines. The relative strengths of Brγ and H2S(1), the presence of [Fe ii] emission, and the mid-infrared spectral-energy distribution are all consistent with intense recent star formation near the center of that galaxy. The NIR spectrum of NGC 4584 is devoid of line emission in the NIR, suggesting that star formation does not power the emission detected at visible wavelengths from that galaxy. Wavelengths that contain the Ballick–Ramsey C2 band at 1.76 μm are matched by moderately metal-poor E-MILES model spectra with an age of 2 Gyr. The radial age trends in these galaxies are in the opposite sense to those in late-type disk galaxies, and it is concluded that they have evolved in a cluster environment for at least several Gyr.
Uniform Error Estimates for the Lanczos Method

The Lanczos method is one of the most powerful and fundamental techniques for solving an extremal symmetric eigenvalue problem. Convergence-based error estimates depend heavily on the eigenvalue gap. In practice, this gap is often relatively small, resulting in significant overestimates of error. One way to avoid this issue is through the use of uniform error estimates, namely, bounds that depend only on the dimension of the matrix and the number of iterations. In this work, we prove explicit upper and lower uniform error estimates for the Lanczos method. These lower bounds, paired with numerical results, imply that the maximum error of $m$ iterations of the Lanczos method over all $n \times n$ symmetric matrices does indeed depend on the dimension $n$ in practice. The improved bounds for extremal eigenvalues translate immediately to error estimates for the condition number of a symmetric positive definite matrix. In addition, we prove more specific results for matrices that possess some level of eigenvalue regularity or whose eigenvalues converge to some limiting empirical spectral distribution. Through numerical experiments, we show that the theoretical estimates of this paper do apply to practical computations for reasonably sized matrices.
Interpretable Multi Time-scale Constraints in Model-free Deep Reinforcement Learning for Autonomous Driving

In many real world applications, reinforcement learning agents have to optimize multiple objectives while following certain rules or satisfying a list of constraints. Classical methods based on reward shaping, i.e. a weighted combination of different objectives in the reward signal, or Lagrangian methods, including constraints in the loss function, have no guarantees that the agent satisfies the constraints at all points in time and lack in interpretability. When a discrete policy is extracted from an action-value function, safe actions can be ensured by restricting the action space at maximization, but can lead to sub-optimal solutions among feasible alternatives. In this work, we propose Multi Time-scale Constrained DQN, a novel algorithm restricting the action space directly in the Q-update to learn the optimal Q-function for the constrained MDP and the corresponding safe policy. In addition to single-step constraints referring only to the next action, we introduce a formulation for approximate multi-step constraints under the current target policy based on truncated value-functions to enhance interpretability. We compare our algorithm to reward shaping and Lagrangian methods in the application of high-level decision making in autonomous driving, considering constraints for safety, keeping right and comfort. We train our agent in the open-source simulator SUMO and on the real HighD data set.
Mix&Match: Towards Omitting Modelling Through In-situ Remixing of Model Repository Artifacts in Mixed Reality

The accessibility of tools to model artifacts is one of the core driving factors for the adoption of Personal Fabrication. Subsequently, model repositories like Thingiverse became important tools in (novice) makers' processes. They allow them to shorten or even omit the design process, offloading a majority of the effort to other parties. However, steps like measurement of surrounding constraints (e.g., clearance) which exist only inside the users' environment, can not be similarly outsourced. We propose Mix&Match a mixed-reality-based system which allows users to browse model repositories, preview the models in-situ, and adapt them to their environment in a simple and immediate fashion. Mix&Match aims to provide users with CSG operations which can be based on both virtual and real geometry. We present interaction patterns and scenarios for Mix&Match, arguing for the combination of mixed reality and model repositories. This enables almost modelling-free personal fabrication for both novices and expert makers.
Graphene promotes axon elongation through local stall of Nerve Growth Factor signaling endosomes.

Several works reported increased differentiation of neuronal cells grown on graphene; however, the molecular mechanism driving axon elongation on this material has remained elusive. Here, we study the axonal transport of nerve growth factor (NGF), the neurotrophin supporting development of peripheral neurons, as a key player in the time course of axonal elongation of dorsal root ganglion neurons on graphene. We find that graphene drastically reduces the number of retrogradely transported NGF vesicles in favor of a stalled population in the first two days of culture, in which the boost of axon elongation is observed. This correlates with a mutual charge redistribution, observed via Raman spectroscopy and electrophysiological recordings. Furthermore, ultrastructural analysis indicates a reduced microtubule distance and an elongated axonal topology. Thus, both electrophysiological and structural effects can account for graphene action on neuron development. Unraveling the molecular players underneath this interplay may open new avenues for axon regeneration applications.
Inside the VA: How Workplace Training Evaluation Impacts Employee Performance

Employee performance and patient satisfaction are strong indicators of the current state of a healthcare organization. Workplace training programs are used to teach employees the knowledge and skills necessary to effectively perform on the job. Instructor-led, online, blended and independent learning events are produced to address learning needs and to improve staff performance. These training programs are necessary for preparing staff to meet the demands of daily work expectations. The learning approaches should encourage trainees to apply what they learn to their work assignments. When programs are not evaluated consistently and methodically by the participating employees, their sustainability is uncertain. An evaluation of the training process within a federal government, healthcare organization (VA St. Louis Health Care System) was performed to evaluate gap(s) between on-the-job training and work results. Evaluation when performed appropriately can determine if the intended elements of training are present within the training program. Using the first three levels of the Kirkpatrick training evaluation model, a mixed methods research strategy was applied. There is a notion that evaluation seldom reaches the third level, transfer of training or behavior. The verification of this assumption required an analysis of the workplace training program and its users. The core curriculum, trainers, transfer of content and roles of supervisors during and after training, were assessed. Participants were federal employees who worked as physicians, nurses, training instructors, administrative and support personnel. These healthcare providers voluntarily completed paper surveys which were followed by semi-structured interviews. The study found that merely 52 percent of evaluations extended to the transfer of training level. While employees predominantly
A Correlated Random Coefficient Panel Model with Time-Varying Endogeneity

This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial effect (APE) when fixed-effect techniques cannot be used to control for the correlation between the regressors and the time-varying disturbances. Relying on control variables, we develop a constructive two-step identification argument. The first step identifies nonparametrically the conditional expectation of the disturbances given the regressors and the control variables, and the second step uses "between-group" variations, correcting for endogeneity, to identify the APE. We propose a natural semiparametric estimator of the APE, show its $\sqrt{n}$ asymptotic normality and compute its asymptotic variance. The estimator is computationally easy to implement, and Monte Carlo simulations show favorable finite sample properties. Control variables arise in various economic and econometric models, and we provide variations of our argument to obtain identification in some applications. As an empirical illustration, we estimate the average elasticity of intertemporal substitution in a labor supply model with random coefficients.
On the orbital evolution of meteoroid 2020 CD3, a temporarily captured orbiter of the Earth–Moon system

Any near-Earth object (NEO) following an Earth-like orbit may eventually be captured by Earth's gravity during low-velocity encounters. This theoretical possibility was first attested during the fly-by of 1991 VG in 1991-1992 with the confirmation of a brief capture episode ---for about a month in 1992 February. Further evidence was obtained when 2006 RH120 was temporarily captured into a geocentric orbit from 2006 July to 2007 July. Here, we perform a numerical assessment of the orbital evolution of 2020 CD3, a small NEO found recently that could be the third instance of a meteoroid temporarily captured by Earth's gravity. We confirm that 2020 CD3 is currently following a geocentric trajectory although it will escape into a heliocentric path by early May 2020. Our calculations indicate that it was captured by the Earth in 2016 (median). This episode is longer than that of 2006 RH120. Prior to its capture as a minimoon, 2020 CD3 was probably a NEO of the Aten type, but an Apollo type cannot be excluded; in both cases, the orbit was very Earth-like, with low eccentricity and low inclination, typical of an Arjuna-type meteoroid. A few clone orbits remained geocentric for nearly a century, opening the door to the existence of yet-to-be-detected minimoons that are relatively stable for time-scales comparable to those of unbound quasi-satellites such as (469219) Kamo`oalewa. In addition, nearly 10 per cent of the experiments led to brief moon-moon episodes in which the value of the selenocentric energy of 2020 CD3 became negative.
The changing-type SN 2014C may come from an 11-M⊙ star stripped by binary interaction and violent eruption

SN 2014C was an unprecedented supernova (SN) that displayed a metamorphosis from Type Ib to Type IIn over ∼200 d. This transformation is consistent with a helium star having exploded in a cavity surrounded by a dense shell of the progenitor’s stripped hydrogen envelope. For at least 5 yr post-explosion, the ejecta continued to interact with an outer, extended component of circumstellar medium (CSM) that was ejected even before the dense shell. It is still unclear, however, what kind of progenitor could have undergone such a complicated mass-loss history before it produced this peculiar SN. In this paper, we report a new analysis of SN 2014C’s host star cluster based on data from the Hubble Space Telescope (HST). By carefully fitting its spectral energy distribution (SED), we derive a precise cluster age of 20.0$^{+3.5}_{-2.6}$ Myr, which corresponds to the progenitor’s lifetime assuming coevolution. Combined with binary stellar evolution models, we find that SN 2014C’s progenitor may have been an ∼11-M⊙ star in a relatively wide binary system. The progenitor’s envelope was partially stripped by Case C or Case BC mass transfer via binary interaction, followed by a violent eruption that ejected the last hydrogen layer before terminal explosion. Thus, SN 2014C, in common with SNe 2006jc and 2015G, may be a third example that violent eruptions, with mass-loss rates matching luminous blue variable (LBV) giant eruptions, can also occur in much lower mass massive stars if their envelopes are partially or completely stripped in interacting binaries.
Kinetic Alfvén wave generation by velocity shear in collisionless plasmas

The evolution of a linearly polarized, long-wavelength Alfvén wave propagating in a collisionless magnetized plasma with a sheared parallel-directed velocity flow is here studied by means of two-dimensional hybrid Vlasov–Maxwell (HVM) simulations. The unperturbed sheared flow has been represented by an exact solution of the HVM set of equations of (Malara et al., Phys. Rev. E, vol. 97, 2018, 053212), thus avoiding spurious oscillations that would arise from the non-stationary initial state and inevitably affect the dynamics of the system. We have considered the evolution of both a small and a moderate amplitude Alfvén wave, in order to separate linear wave–shear flow couplings from kinetic effects, the latter being more relevant for larger wave amplitudes. The phase mixing generated by the shear flow modifies the initial perturbation, leading to the formation of small-scale transverse fluctuations at scales comparable with the proton inertial length/Larmor radius. By analysing both the polarization and group velocity of perturbations in the shear regions, we identify them as kinetic Alfvén waves (KAWs). In the moderate amplitude run, kinetic effects distort the proton distribution function in the shear region. This leads to the formation of a proton beam, at the Alfvén speed and parallel to the magnetic field. Such a feature, due to the parallel electric field associated with KAWs, positively compares with solar wind observations of suprathermal ion populations, suggesting that it may be related to the presence of ion-scale KAW-like fluctuations.
Memory effect and BMS symmetries for extreme black holes

We study horizon shells and soldering freedom for extreme black holes and how supertranslation-like Bondi-Metzner-Sachs (BMS) symmetries appear as soldering transformations. Further, for a null shell placed infinitesimally close to the horizon of an extreme Reissner–Nordstrom (RN) black hole, we show superrotation-like symmetries also arise as soldering freedom. Next, considering the interaction of impulsive gravitational waves supported at the horizon shell with test particles, we study how the “memory” (or the imprints) of BMS-like symmetries gets encoded in the geodesics (test particles) crossing the shell. Our study shows, timelike test particles get displaced from their initial plane when they cross the horizon shell. For a null geodesic congruence crossing the horizon shell, the optical tensors corresponding to the congruence suffer jumps. In both cases, the changes are induced by BMS parameters that constitute the gravity wave and matter degrees of freedom of the shell.
Multiview Chirality

A BSTRACT . Given an arrangement of cameras A = { A 1 ,. ,A m } , the chiral domain of A is the subset of P 3 that lies in front it. It is a generalization of the classical deﬁnition of chirality. We give an algebraic description of this set and use it to generalize Hartley’s theory of chiral reconstruction [4] to m ≥ 2 views and derive a chiral version of Triggs’ Joint Image [12, 13].