Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders

In this paper, an LSTM autoencoder-based architecture is utilized for drowsiness detection with ResNet-34 as feature extractor. The problem is considered as anomaly detection for a single subject; therefore, only the normal driving representations are learned and it is expected that drowsiness representations, yielding higher reconstruction losses, are to be distinguished according to the knowledge of the network. In our study, the confidence levels of normal and anomaly clips are investigated through the methodology of label assignment such that training performance of LSTM autoencoder and interpretation of anomalies encountered during testing are analyzed under varying confidence rates. Our method is experimented on NTHU-DDD and benchmarked with a state-of-the-art anomaly detection method for driver drowsiness. Results show that the proposed model achieves detection rate of 0.8740 area under curve (AUC) and is able to provide significant improvements on certain scenarios.
Explicit Dynamical Systems on the Sierpi\'nski Curve

We apply Boro\'nski and Oprocha's inverse limit construction of dynamical systems on the Sierpi\'nski carpet by using the initial systems of $n-$Chamanara surfaces and their $n-$baker transformations, $n \geq 2$. We show that all positive real numbers are realized as metric entropy values of dynamical systems on the carpet. We also produce a simplification of Boro\'nski and Oprocha's proof showing that dynamical systems on the carpet do not have the Bowen specification property.
Probing Our Universe’s Past Using Earth’s Geological and Climatological History and Shadows of Galactic Black Holes

In this short review, we discuss how Earth’s climatological and geological history and also how the shadows of galactic black holes might reveal our Universe’s past evolution. Specifically we point out that a pressure singularity that occurred in our Universe’s past might have left its imprint on Earth’s geological and climatological history and on the shadows of cosmological black holes. Our approach is based on the fact that the H0 tension problem may be resolved if some sort of abrupt physics change occurred in our Universe 70–150 Myrs ago, an abrupt change that deeply affected the Cepheid parameters. We review how such an abrupt physics change might have been caused in our Universe by a smooth passage of it through a pressure finite-time singularity. Such finite-time singularities might occur in modified gravity and specifically in F(R) gravity, so we show how modified gravity might drive this type of evolution, without resorting to peculiar cosmic fluids or scalar fields. The presence of such a pressure singularity can distort the elliptic trajectories of bound objects in the Universe, causing possible geological and climatological changes on Earth, if its elliptic trajectory around the Sun might have changed. Also, such a pressure singularity affects directly the circular photon orbits around supermassive galactic black holes existing at cosmological redshift distances, thus the shadows of some cosmological black holes at redshifts z≤0.01, might look different in shape, compared with the SgrA* and M87* supermassive black holes. This feature however can be checked experimentally in the very far future.
Domain Adaptation for Question Answering via Question Classification

Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems are trained in a source domain but deployed in a different target domain. In this work, we investigate the potential benefits of question classification for QA domain adaptation. We propose a novel framework: Question Classification for Question Answering (QC4QA). Specifically, a question classifier is adopted to assign question classes to both the source and target data. Then, we perform joint training in a self-supervised fashion via pseudo-labeling. For optimization, inter-domain discrepancy between the source and target domain is reduced via maximum mean discrepancy (MMD) distance. We additionally minimize intra-class discrepancy among QA samples of the same question class for fine-grained adaptation performance. To the best of our knowledge, this is the first work in QA domain adaptation to leverage question classification with self-supervised adaptation. We demonstrate the effectiveness of the proposed QC4QA with consistent improvements against the state-of-the-art baselines on multiple datasets.
Einstein's model of "the movement of small particles in a stationary liquid" revisited: finite propagation speed

The aforementioned celebrated model, though a breakthrough in Stochastic processes and a great step toward the construction of the Brownian motion leads to a paradox: infinite propagation speed and violation of the 2nd law of thermodynamics. We adapt the model by assuming the diffusion matrix dependent of the concentration of particles, rather than constant it was up to Einstein, and prove a fi- nite propagation speed under the assumption of a qualified decrease of the diffusion for small concentration. The method involves a nonlinear degenerated parabolic PDE in divergent form, a parabolic Sobolev-type inequality and the Ladyzhenskaya-Uraltseva iteration lemma.
A Survey of Near Infrared Nebulosities around Luminous Young Stellar Objects: J, H, and K Imaging

We present J, H, and K′ images of 45 IRAS sources in the recent list by Campbell, Persson, & Matthews, which have been presumed to be luminous young stellar objects (YSOs) because of their infrared colors and their large IRAS fluxes. These objects have been classified into four groups according to two dust features in the 3 μm band—the H2O ice absorption and the unidentified infrared band (UIB) emission. Our near-infrared images reveal that most of them (42/45) have nebulosity in at least one of the three bands. By examining the nebulosities around these YSOs, we find that (1) nebulae around the objects with the ice absorption can be explained by the scattered light of the central stars, (2) nebulae around the objects with the UIB emission cannot be explained by the scattered light alone—additional emission, most likely from very small grains, is necessary to explain the nebular colors and the surface brightness, and (3) the objects with neither the ice absorption nor the UIB emission tend to have faint or no nebulosity. We interpret the variation of nebular brightness and the color as evolutionary phases of circumstellar matter of intermediate- to high-mass YSOs.
Holographic nonlocal rotating observables and their renormalization

We analyse non-local rotating observables in holography corresponding to spinning bound states. To renormalize their energies and momenta we suggest and discuss different holographic renormalization schemes motivated by the static non-local observables. Namely the holographic renormalization and the rotating color singlet mass subtraction scheme. In the holographic renormalization we identify the infinite boundary terms and subtract them. In the mass subtraction scheme we evaluate the energy of a spinning trailing string corresponding to the color charged singlet which experiences dragging phenomena and we subtract it from the energy of the bound state to obtain the renormalized finite energy. Then we apply our generic framework to certain strongly coupled thermal theories with broken rotational symmetry. We find numerical solutions corresponding to spinning bound states with a fixed size while varying their angular frequency. By applying numerically the renormalization schemes, we find that there is a critical frequency where the bound state ceases to exist or dissociates. We also note that bound states require lower angular frequencies to dissociate when the theory has less symmetry.
Improving Model Training via Self-learned Label Representations

Modern neural network architectures have shown remarkable success in several large-scale classification and prediction tasks. Part of the success of these architectures is their flexibility to transform the data from the raw input representations (e.g. pixels for vision tasks, or text for natural language processing tasks) to one-hot output encoding. While much of the work has focused on studying how the input gets transformed to the one-hot encoding, very little work has examined the effectiveness of these one-hot labels. In this work, we demonstrate that more sophisticated label representations are better for classification than the usual one-hot encoding. We propose Learning with Adaptive Labels (LwAL) algorithm, which simultaneously learns the label representation while training for the classification task. These learned labels can significantly cut down on the training time (usually by more than 50%) while often achieving better test accuracies. Our algorithm introduces negligible additional parameters and has a minimal computational overhead. Along with improved training times, our learned labels are semantically meaningful and can reveal hierarchical relationships that may be present in the data.
Bounding the Rademacher Complexity of Fourier Neural Operators

Recently, several types of neural operators have been developed, including deep operator networks, graph neural operators, and Multiwavelet-based operators. Compared with these models, the Fourier neural operator (FNO), a physics-inspired machine learning method, is computationally efficient and can learn nonlinear operators between function spaces independent of a certain finite basis. This study investigated the bounding of the Rademacher complexity of the FNO based on specific group norms. Using capacity based on these norms, we bound the generalization error of the model. In addition, we investigate the correlation between the empirical generalization error and the proposed capacity of FNO. We infer that the type of group norm determines the information about the weights and architecture of the FNO model stored in capacity. The experimental results offer insight into the impact of the number of modes used in the FNO model on the generalization error. The results confirm that our capacity is an effective index for estimating generalization errors.
How Young are Early-type Cluster Galaxies? Quantifying the Young Stellar Component in a Rich Cluster at z=0.41

We present a new method of quantifying the mass fraction of young stars in galaxies by analyzing near-ultraviolet (NUV)-optical colors. We focus our attention on early-type cluster galaxies, whose star formation history is at present undetermined. Rest-frame NUV (F300W) and optical (F702W) images of cluster Abell 851 (z = 0.41) using Hubble Space Telescope/WFPC2 allow us to determine an NUV-optical color-magnitude relation, whose slope is incompatible with a monolithic scenario for star formation at high redshift. A degeneracy between a young stellar component and its fractional mass contribution to the galaxy is found, and a photometric analysis comparing the data with the predictions for a simple two-stage star formation history is presented. The analysis shows that some of the early-type galaxies may have fractions higher than 10% of the total mass content in stars formed at z ~ 0.5. An increased scatter is found in the color-magnitude relation at the faint end, resulting in a significant fraction of faint blue early-type systems. This would imply that less massive galaxies undergo more recent episodes of star formation, and this can be explained in terms of a positive correlation between star formation efficiency and luminosity.
Self-organization phenomena in cold atmospheric pressure plasma slit jet

The radio frequency plasma slit jet, which produces 150 mm wide streaming plasma outside the jet body, exhibits exciting self-organization phenomena that resemble the self-organized patterns of dielectric barrier discharge (DBD) filaments. Similarly, as in DBD, the filaments are surrounded by an inhibition zone that does not allow two filaments to come closer to each other. With fast camera imaging, we observed the filamentary character of the discharge in all the studied gas feeds (Ar, Ar/N2, and Ar/O2). Still, the visual appearance of the filaments in the plasma and their interaction with a dielectric surface depended significantly on the gas feed. As the breakdown voltage in pure Ar is relatively low compared to the applied one, new filaments form frequently. Such newly created filaments disrupted the characteristic inter-filament distance, forcing the system to rearrange. The frequent ignition and decay processes in Ar led to short filament lifetimes (0.020–0.035 s) and their high jitter speed (0.9–1.7 m s−1), as determined with an image processing custom code based on Gwyddion libraries. The number of filaments was lower in the Ar/O2 and Ar/N2 mixtures. It was attributed to a loss of energy in the excitation of rotational and vibrational levels and oxygen electronegativity. Since the probability of low-current side discharges transitioning into the full plasma filaments was limited in these gas mixtures, the self-organized pattern was seldom disrupted, leading to lesser movement and longer lifetimes. Unlike in Ar or Ar/O2, the constricted filaments in Ar/N2 were surrounded by diffuse plasma plumes, likely connected to the presence of long-lived nitrogen species. We demonstrated in the polypropylene treatment that the self-organization phenomena affected the treatment uniformity.
Continuous head-related transfer function representation based on hyperspherical harmonics

Expressing head-related transfer functions (HRTFs) in spherical harmonic (SH) domain has been thoroughly studied as a method of obtaining continuity over space. However, HRTFs are functions not only of direction but also of frequency. This paper presents an extension of the SH-based method, utilizing hyperspherical harmonics (HSHs) to obtain an HRTF representation that is continuous over both space and frequency. The application of the HSH approximation results in a relatively small set of coefficients which can be decoded into HRTF values at any direction and frequency. The paper discusses results obtained by applying the method to magnitude spectra extracted from exemplary HRTF measurements. The HRTF representations based on SHs and HSHs exhibit similar reproduction accuracy, with the latter one featuring continuity over both space and frequency and requiring much lower number of coefficients. The developed HSH-based continuous functional model can serve multiple purposes, such as interpolation, compression or parametrization for machine-learning applications.
On the Utility of Buffers in Pick-n-Swap Based Lattice Rearrangement

We investigate the utility of employing multiple buffers in solving a class of rearrangement problems with pick- n-swap manipulation primitives. In this problem, objects stored randomly in a lattice are to be sorted using a robot arm with k 1 swap spaces or buffers, capable of holding up to $k$ objects on its end-effector simultaneously. On the structural side, we show that the addition of each new buffer brings diminishing returns in saving the end-effector travel distance while holding the total number of pick-n-swap operations at a minimum. This is due to an interesting recursive cycle structure in random m-permutation, where the largest cycle covers over 60% of objects. On the algorithmic side, we propose fast algorithms for 1D and 2D lattice rearrangement problems that can effectively use multiple buffers to boost solution optimality. Numerical experiments demonstrate the efficiency and scalability of our methods, as well as confirm the diminishing return structure as more buffers are employed. Introduction video: https://youtu.be/KtBxoARGaVQ
A 3.3 Gbps SPAD-Based Quantum Random Number Generator

Quantum random number generators are a burgeoning technology used for a variety of applications, including modern security and encryption systems. Typical methods exploit an entropy source combined with an extraction or bit generation circuit in order to produce a random string. In integrated designs there is often little modelling or analytical description of the entropy source, circuit extraction and post-processing provided. In this work, we first discuss theory on the quantum random flip-flop (QRFF), which elucidates the role of circuit imperfections that manifest themselves in bias and correlation. Then, a Verilog-AMS model is developed in order to validate the analytical model in simulation. A novel transistor implementation of the QRFF circuit is presented, which enables compensation of the degradation in entropy inherent to the finite non-symmetric transitions of the random flip-flop. Finally, a full system containing two independent arrays of the QRFF circuit is manufactured and tested in a 55 nm Bipolar-CMOS-DMOS (BCD) technology node, demonstrating bit generation statistics that are commensurate to the developed model. The full chip is able to generate 3.3 Gbps of data when operated with an external LED, whereas an individual QRFF can generate 25 Mbps each of random data while maintaining a Shannon entropy bound>0.997, which is one of the highest per pixel bit generation rates to date. NIST STS is used to benchmark the generated bit strings, thereby validating the QRFF circuit as an excellent candidate for fully-integrated QRNGs.
Multiple Object Tracking in Recent Times: A Literature Review

Multiple object tracking gained a lot of interest from researchers in recent years, and it has become one of the trending problems in computer vision, especially with the recent advancement of autonomous driving. MOT is one of the critical vision tasks for different issues like occlusion in crowded scenes, similar appearance, small object detection difficulty, ID switching, etc. To tackle these challenges, as researchers tried to utilize the attention mechanism of transformer, interrelation of tracklets with graph convolutional neural network, appearance similarity of objects in different frames with the siamese network, they also tried simple IOU matching based CNN network, motion prediction with LSTM. To take these scattered techniques under an umbrella, we have studied more than a hundred papers published over the last three years and have tried to extract the techniques that are more focused on by researchers in recent times to solve the problems of MOT. We have enlisted numerous applications, possibilities, and how MOT can be related to real life. Our review has tried to show the different perspectives of techniques that researchers used overtimes and give some future direction for the potential researchers. Moreover, we have included popular benchmark datasets and metrics in this review.
MiniBooNE and MicroBooNE Combined Fit to a 3+1 Sterile Neutrino Scenario.

This Letter presents the results from the MiniBooNE experiment within a full "3+1" scenario where one sterile neutrino is introduced to the three-active-neutrino picture. In addition to electron-neutrino appearance at short baselines, this scenario also allows for disappearance of the muon-neutrino and electron-neutrino fluxes in the Booster Neutrino Beam, which is shared by the MicroBooNE experiment. We present the 3+1 fit to the MiniBooNE electron-(anti)neutrino and muon-(anti)neutrino data alone and in combination with MicroBooNE electron-neutrino data. The best-fit parameters of the combined fit with the exclusive charged-current quasielastic analysis (inclusive analysis) are Δm^{2}=0.209 eV^{2}(0.033 eV^{2}), |U_{e4}|^{2}=0.016(0.500), |U_{μ4}|^{2}=0.500(0.500), and sin^{2}(2θ_{μe})=0.0316(1.0). Comparing the no-oscillation scenario to the 3+1 model, the data prefer the 3+1 model with a Δχ^{2}/d.o. f. =24.7/3(17.3/3), a 4. 3σ(3.4σ) preference assuming the asymptotic approximation given by Wilks's theorem.
Study of B Meson Decays to ppbarh Final States

B mesons are unique among well-established non-quarkonium mesons in their ability to decay into baryons. Baryonic B decays offer a wide range of interesting areas of study: they can be used to test our theoretical understanding of rare decay processes involving baryons, search for direct CP violation and study low-energy QCD. This thesis presents measurements of branching fractions and a study of the decay dynamics of the charmless three-body decays of B meson into p{bar p}h final states, where h = {pi}{sup +}, K{sup +}, K{sub S}{sup 0}, K*{sup 0} or K*{sup +}. With a sample of 232 million {Upsilon}(4S) {yields} B{bar B} events collected with the BaBar detector, we report the first observation of the B {yields} p{bar p}K*{sup 0} decay, and provide improved measurements of branching fractions of the other modes. The distribution of the three final-state particles is of particular interest since it provides dynamical information on the possible presence of exotic intermediate states such as the hypothetical pentaquark states {Theta}*{sup ++} and {Theta}{sup +}in the m{sub pK{sup +}} and m{sub pK{sub S}{sup 0}} spectra, respectively, or glueball states (such as the tensor glueball f{sub J}(2220)) in the m{sub p{bar p}} spectrum. No evidence for exotic statesmore » is found and upper limits on the branching fractions are set. An enhancement at low p{bar p} mass is observed in all the B {yields} p{bar p}h modes, and its shape is compared between the decay modes and with the shape of the time-like proton form factor. A Dalitz plot asymmetry in B {yields} p{bar p}K{sup +} mode suggests dominance of the penguin amplitude in this decay and disfavors the possibility that the low mass p{bar p} enhancement originates from the presence of a resonance below threshold (such as the recently seen baryonium candidate at 1835 MeV/c{sup 2}). We also identify decays of the type B {yields} X{sub c{bar c}}h {yields} p{bar p}h, where h = K{sup +}, K{sub S}{sup 0}, K*{sup 0} or K*{sup +}, and X{sub c{bar c}} = {eta}{sub c} or J/{psi}. In particular, we report on the evidence of the B {yields} {eta}{sub c}K*{sup +} decay and provide a measurement of the width of {eta}{sub c}. « less
Set-based value operators for non-stationary Markovian environments

This paper analyzes finite state Markov Decision Processes (MDPs) with uncertain parameters in compact sets and re-examines results from robust MDP via set-based fixed point theory. To this end, we generalize the Bellman and policy evaluation operators to contracting operators on the value function space and denote them as \emph{value operators}. We lift these value operators to act on \emph{sets} of value functions and denote them as \emph{set-based value operators}. We prove that the set-based value operators are \emph{contractions} in the space of compact value function sets. Leveraging insights from set theory, we generalize the rectangularity condition in classic robust MDP literature to a containment condition for all value operators, which is weaker and can be applied to a larger set of parameter-uncertain MDPs and contracting operators in dynamic programming. We prove that both the rectangularity condition and the containment condition sufficiently ensure that the set-based value operator's fixed point set contains its own extrema elements. For convex and compact sets of uncertain MDP parameters, we show equivalence between the classic robust value function and the supremum of the fixed point set of the set-based Bellman operator. Under dynamically changing MDP parameters in compact sets, we prove a set convergence result for value iteration, which otherwise may not converge to a single value function. Finally, we derive novel guarantees for probabilistic path-planning problems in planet exploration and stratospheric station-keeping.