Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and tracking bacteria with quantum light

The field of quantum sensing aims at improving the detection and estimation of classical parameters that are encoded in physical systems by resorting to quantum sources of light and quantum detection strategies. The same approach can be used to improve the current classical measurements that are performed on biological systems. Here we consider the scenario of two bacteria (E. coli and Salmonella) growing in a Luria Bertani broth and monitored by classical spectrophotometers. Their concentration can be related to the optical transmissivity via the Beer-Lambert-Bouguer's law and their growth curves can be described by means of Gompertz functions. Starting from experimental data points, we extrapolate the growth curves of the two bacteria and we study the theoretical performance that would be achieved with a quantum setup. In particular, we discuss how the bacterial growth can in principle be tracked by irradiating the samples with orders of magnitude fewer photons, identifying the clear superiority of quantum light in the early stages of growth. We then show the superiority and the limits of quantum resources in two basic tasks: (i) the early detection of bacterial growth and (ii) the early discrimination between two bacteria species.
Deep Reinforcement Learning for Joint Beamwidth and Power Optimization in mmWave Systems

This letter studies the joint beamwidth and transmit power optimization problem in millimeter wave communication systems. A deep reinforcement learning based approach is proposed. Specifically, a customized deep Q network is trained offline, which is able to make real-time decisions when deployed online. Simulation results show that the proposed approach significantly outperforms conventional approaches in terms of both performance and complexity. Besides, strong generalization ability to different system parameters is also demonstrated, which further enhances the practicality of the proposed approach.
An entanglement enhanced training algorithm for supervised quantum classifiers

We propose an algorithm for faster training in supervised quantum classifiers. In particular, we have harnessed the property of quantum entanglement to build a model that can simultaneously manipulate multiple training samples. We show that this would lead to a quantum speedup in cost evaluation and hence in the training process of supervised classifiers. The results presented in this paper are for binary classification problems. Nevertheless, the analysis can be extended to multi-class classification problems as well.
Vaporization dynamics of a quantum liquid

We study the heating dynamics of a Luttinger liquid, upon suddenly coupling it to a dissipative environment. Within the Lindblad equation, the environment couples to local currents and heats the quantum liquid up to infinite temperatures. The fermionic single particle density matrix, and many other correlators, retain the initial Luttinger liquid correlations in space but decay exponentially in time with a rate that depends on the strength of the interaction. The spectrum of the time evolved density matrix is gapped, which collapses gradually as $-\ln(t)$. The von Neumann entropy crosses over from the early time $-t\ln(t)$ behaviour to a $\ln(t)$ growth for late times. The early time dynamics is captured numerically by performing simulations on spinless interacting fermions, using several numerically exact methods. Our results establish the validity of bosonization for the early time dynamics of 1D lattice models, and could be observed experimentally in bosonic Luttinger liquids.
Tensor Networks for Probabilistic Sequence Modeling

Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time

Robust covariance estimation is the following, well-studied problem in high dimensional statistics: given $N$ samples from a $d$-dimensional Gaussian $\mathcal{N}(\boldsymbol{0}, \Sigma)$, but where an $\varepsilon$-fraction of the samples have been arbitrarily corrupted, output $\widehat{\Sigma}$ minimizing the total variation distance between $\mathcal{N}(\boldsymbol{0}, \Sigma)$ and $\mathcal{N}(\boldsymbol{0}, \widehat{\Sigma})$. This corresponds to learning $\Sigma$ in a natural affine-invariant variant of the Frobenius norm known as the \emph{Mahalanobis norm}. Previous work of Cheng et al demonstrated an algorithm that, given $N = \Omega (d^2 / \varepsilon^2)$ samples, achieved a near-optimal error of $O(\varepsilon \log 1 / \varepsilon)$, and moreover, their algorithm ran in time $\widetilde{O}(T(N, d) \log \kappa / \mathrm{poly} (\varepsilon))$, where $T(N, d)$ is the time it takes to multiply a $d \times N$ matrix by its transpose, and $\kappa$ is the condition number of $\Sigma$. When $\varepsilon$ is relatively small, their polynomial dependence on $1/\varepsilon$ in the runtime is prohibitively large. In this paper, we demonstrate a novel algorithm that achieves the same statistical guarantees, but which runs in time $\widetilde{O} (T(N, d) \log \kappa)$. In particular, our runtime has no dependence on $\varepsilon$. When $\Sigma$ is reasonably conditioned, our runtime matches that of the fastest algorithm for covariance estimation without outliers, up to poly-logarithmic factors, showing that we can get robustness essentially "for free. "
Symmetry Protected Invariant Scattering Properties for Arbitrary Polarizations

Polarization independent Mie scattering of building blocks is foundational for constructions of optical systems with robust functionalities. Conventional studies for such polarization independence are generally restricted to special states of either linear or circular polarizations, widely neglecting elliptically-polarized states that are generically present in realistic applications. Here we present a comprehensive recipe to achieve invariant scattering properties (including extinction, scattering and absorption) for arbitrary polarizations, requiring only rotation symmetry and absence of optical activities. It is discovered that sole rotation symmetries can effectively decouple the two scattering channels that originate from the incident circularly polarized waves of opposite handedness, leading to invariance of all scattering properties for any polarizations on the same latitude circle of the Poincare sphere. Further incorporations of extra inversion or mirror symmetries would eliminate the optical activities and thus ensure scattering property invariance for arbitrary polarizations. In sharp contrast to previous investigations that rely heavily on complicated algebraic formulas, our arguments are fully intuitive and geometric, bringing to surface the essential physical principles rather than obscuring them. The all-polarization invariance we reveal is induced by discrete spatial symmetries of the scattering configurations, underlying which there are functioning laws of reciprocity and conservation of parity and helicity. This symmetry-protected intrinsic invariance is robust against any symmetry-preserving perturbations, which may render extra flexibilities for designing optical devices with stable functionalities.
Learning the Effective Dynamics of Complex Multiscale Systems

Simulations of complex multiscale systems are essential for science and technology ranging from weather forecasting to aircraft design. The predictive capabilities of simulations hinges on their capacity to capture the governing system dynamics. Large scale simulations, resolving all spatiotemporal scales, provide invaluable insight at a high computational cost. In turn, simulations using reduced order models are affordable but their veracity hinges often on linearisation and/or heuristics. Here we present a novel systematic framework to extract and forecast accurately the effective dynamics (LED) of complex systems with multiple spatio-temporal scales. The framework fuses advanced machine learning algorithms with equation-free approaches. It deploys autoencoders to obtain a mapping between fine and coarse grained representations of the system and learns to forecast the latent space dynamics using recurrent neural networks. We compare the LED framework with existing approaches on a number of benchmark problems and demonstrate reduction in computational efforts by several orders of magnitude without sacrificing the accuracy of the system.
IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency

Multiple object tracking (MOT) is a crucial task in computer vision society. However, most tracking-by-detection MOT methods, with available detected bounding boxes, cannot effectively handle static, slow-moving and fast-moving camera scenarios simultaneously due to ego-motion and frequent occlusion. In this work, we propose a novel tracking framework, called "instance-aware MOT" (IA-MOT), that can track multiple objects in either static or moving cameras by jointly considering the instance-level features and object motions. First, robust appearance features are extracted from a variant of Mask R-CNN detector with an additional embedding head, by sending the given detections as the region proposals. Meanwhile, the spatial attention, which focuses on the foreground within the bounding boxes, is generated from the given instance masks and applied to the extracted embedding features. In the tracking stage, object instance masks are aligned by feature similarity and motion consistency using the Hungarian association algorithm. Moreover, object re-identification (ReID) is incorporated to recover ID switches caused by long-term occlusion or missing detection. Overall, when evaluated on the MOTS20 and KITTI-MOTS dataset, our proposed method won the first place in Track 3 of the BMTT Challenge in CVPR2020 workshops.
Toward Solving the Puzzle: Dissecting the Complex Merger A521 with Multiwavelength Data

A521 has been a subject of extensive panchromatic studies from X-ray to radio. The cluster possesses a number of remarkable features, including a bright radio relic with a steep spectrum, more than three distinct galaxy groups forming a filament, and two disturbed X-ray peaks at odds with the distant position and tilted orientation of the radio relic. These lines of evidence indicate a complex merger. In this paper, we present a multiwavelength study of A521 based on Subaru optical, Hubble Space Telescope infrared, Chandra X-ray, Giant Metrewave Radio Telescope radio, and Multiple Mirror Telescope optical spectroscopic observations. Our weak-lensing (WL) analysis with improved systematics control reveals that A521 is mainly composed of three substructures aligned in the northwest to southeast orientation. These WL mass substructures are remarkably well-aligned with the cluster optical luminosity distribution constructed from our new enhanced cluster member catalog. These individual substructure masses are determined by simultaneously fitting three Navarro–Frenk–White profiles. We find that the total mass of A521 modeled by the superposition of the three halos is , a factor of 2 higher than the previous WL measurement. With these WL mass constraints combined with X-ray and radio features, we consider two merging scenarios, carry out the corresponding numerical simulations, and discuss the strengths and weaknesses of each case.
Multi-wavelength, spatially resolved modelling of HD 48682’s debris disc

Asteroids and comets (planetesimals) are created in gas- and dust-rich protoplanetary discs. The presence of these planetesimals around main-sequence stars is usually inferred from the detection of excess continuum emission at infrared wavelengths from dust grains produced by destructive processes within these discs. Modelling of the disc structure and dust grain properties for those discs is often hindered by the absence of any meaningful constraint on the location and spatial extent of the disc. Multi-wavelength, spatially resolved imaging is thus invaluable in refining the interpretation of these systems. Observations of HD 48682 at far-infrared (Spitzer, Herschel) and sub-millimetre (JCMT, SMA) wavelengths indicated the presence of an extended, cold debris disc with a blackbody temperature of 57.9 ± 0.7 K. Here, we combined these data to perform a comprehensive study of the disc architecture and its implications for the dust grain properties. The deconvolved images revealed a cold debris belt, verified by combining a 3D radiative transfer dust continuum model with image analysis to replicate the structure using a single, axisymmetric annulus. A Markov chain Monte Carlo analysis calculated the maximum likelihood of HD48682's disc radius ( $R_{\rm disc} = 89^{+17}_{-20}~$ au), fractional width ( $\Delta R_{\rm disc} = 0.41^{+0.27}_{-0.20}$ ), position angle ( $\theta = 66{_{.}^{\circ}} 3^{+4.5}_{-4.9}$ ), and inclination ( $\phi = 112{_{.}^{\circ}} 5^{+4.2}_{-4.2}$ ). HD 48682 has been revealed to host a collisionally active, broad disc whose emission is dominated by small dust grains, smin ∼ 0.6 μm, and a size distribution exponent of 3.60 ± 0.02.
On Analyzing Annotation Consistency in Online Abusive Behavior Datasets

Online abusive behavior is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have proposed, collected, and annotated online abusive content datasets. These datasets play a critical role in facilitating the research on online hate speech and abusive behaviors. However, the annotation of such datasets is a difficult task; it is often contentious on what should be the true label of a given text as the semantic difference of the labels may be blurred (e.g., abusive and hate) and often subjective. In this study, we proposed an analytical framework to study the annotation consistency in online hate and abusive content datasets. We applied our proposed framework to evaluate the consistency of the annotation in three popular datasets that are widely used in online hate speech and abusive behavior studies. We found that there is still a substantial amount of annotation inconsistency in the existing datasets, particularly when the labels are semantically similar.
CR2MET: A high-resolution precipitation and temperature dataset for hydroclimatic research in Chile

The lack of direct observations allowing reliable regional-scale climate characterization is a stumbling block for national environmental studies and large-sample hydrological assessments. Given the influence of the Andes on the precipitation regime and the scarcity of data in elevated lands, reducing biases and uncertainties in climatic variables represents a particularly challenging task across the complex landscape Chile.
Biochemistry of Reactive Oxygen and Nitrogen Species

Reactive species or free radicals include reactive oxygen and nitrogen species that are called reactive oxygen nitrogen species. Reactive oxygen species are formed as a natural by-product of the normal metabolism of oxygen and have significant roles in cell signaling and homeostasis. The reactive oxygen species are generated as a byproduct of biochemical reactions, in mitochondria, peroxisomes, cytochrome P450, and other cellular components. When oxygen homeostasis is not maintained, oxidative stress is increased in the cellular environment. Superoxide, hydrogen peroxide and hydroxyl radicals are normal metabolic by-products which are generated continuously by the mitochondria in growing cells. Microsomal cyto‐ chrome P450 enzymes, flavoprotein oxidases and peroxisomal enzymes are other significant intracellular sources of reactive oxygen species.
Formulation and in vitro evaluation of orodispersible tablets of fexofenadine hydrochloride

Purpose: To develop orodispersible tablets (ODTs) of fexofenadine hydrochloride using three different superdisintegrants in various ratios and to compare their disintegration properties.Methods: Direct compression technique was used for the preparation of ODTs. Mannitol and Avicel CE-15 (microcrystalline cellulose and guar gum) were used as direct compression diluents. The disintegration time of tablets using each polymer (superdisintegrant) was evaluated as well as othertablet properties including weight fluctuation, hardness, friability, wetting time and water absorption ratio.Results: Satisfactory values were obtained for all the evaluated parameters. As the polymer concentration increased, there was a decrease in disintegration time. A comparison of the three different polymers used revealed that CCM3 formulated with 12 % croscarmellose sodium and 14.66 % lactose had the least disintegration time of 32.33 ± 3.23 s. In vitro release studies showed that the maximum drug release of 94.38 ± 0.12 % in 25 min was obtained for ODT tablets containing croscarmellose sodium (CCM3). Conclusion: The orodispersible tablets had quick disintegrating property which was achieved using superdisintegrants. Thus, superdisintegrants improve the disintegration efficiency of orodispersible fexofenadine tablets at low concentrations, when compared to traditional disintegrants. Keywords: Croscarmellose sodium, Direct compression, Fexofenadine, Orodispersible tablets
DETECTION OF HIGH IMPEDANCE FAULT LOCATION IN 20 KV UNDERGROUND DISTRIBUTION NETWORKS BY DISCRETE WAVELET TRANSFORMATION

Different designing approaches for detection of the high impedance fault occurrence location are investigated in this paper and the main goal of representing this research is to design a software system for 20 KV distribution networks in Iran. Machine learning for high impedance fault detection, finds the location of fault by feature extraction using discrete wavelet transform and applying partial and approximate coefficients and following that using the multi-class Support Vector Machine classification, provides the possibility of fault estimation. Results are indicative of appropriate performance of system and fault detection accuracy in training and testing states and some problems like uncertainties and relation with actual output, are removed.
PDE-Constrained Optimization Using Hyper-Reduced-Order Models

The high-cost associated with solving large-scale systems of equations prevents the use of high-performance computing codes in many-query settings such as routine analysis and design optimization. These settings can require thousands of high-dimensional runs for different values of design parameters. Because they reduce the dimension of the underlying equations, projection-based reduced-order models (ROMs) are naturally suitable to be used in many-query contexts. When the underlying equations are nonlinear, however, an additional level of approximation is required to achieve speedups with the ROM. These approximations, referred to as hyper-reduction here, encompass methods such as the Discrete Empirical Interpolation Method (DEIM) [1] and the Gauss-Newton with Approximated Tensors (GNAT) [2,3] to name just a few.
Social Choice for Social Good

In this thesis, we study ways in which computational social choice can contribute to social good, by providing better democratic representation and by allocating scarce goods. On the side of political representation, we study multiple emerging innovations to the democratic process. If legislative elections with proportional representation allow for approval-based ballots rather than the choice of a single party, we give voting rules that satisfy attractive axioms of proportionality. For liquid democracy, a kind of transitive proxy voting, we show how an extension to multiple delegation options can decrease the concentration of power in few hands. Finally, for sortition, a system in which representatives are randomly selected citizens, we develop sampling algorithms, both for the case where all citizens participate if sampled and for the case in which participants self select. Concerning the allocation of scarce goods, we investigate the applications of refugee resettlement and kidney exchange. For refugee resettlement, we show how submodular optimization can lead to more diverse matchings that might increase employment by reducing competition for jobs between similar refugees. In kidney exchange, we give approximately incentive-compatible mechanisms for transplant chains in a semi-random model of compatibilities. Finally, we present three directions for future research, revisiting the topics of sortition, refugee resettlement, and semi-random models.
Improved Utilization of a Plant By-Products Mixture by Supplementing Dietary Bamboo Charcoal for Juvenile Amberjack Seriola dumerili

A 3×2 factorial experiment was carried out to investigate the effects of a plant by-products mixture (PBM; shochu distillery by-products: okara, 4:1) and bamboo charcoal (BC) on growth performance, feed utilization, carcass composition and hematological parameters of juvenile amberjack (Seriola dumerili) by feeding low fishmeal based diets. Six isonitrogenous and isolipidic diets were prepared by adding three levels of PBM at 0, 5 and 10% and two levels of BC at 0 and 4%. Triplicate groups of juvenile amberjack (3.00 g, 20 fish per tank) were fed the respective test diets twice daily to apparent satiation for 45 days. Results indicated that PBM had no significant (P>0.05) effect on growth performance and feed utilization of fish. Inclusion of BC significantly (P<0.05) improved the performance of fish. However, no interaction was found between PBM and BC. Survival was not affected by PBM, BC or their interaction. Whole body proximate composition was also significantly influenced by BC supplementation. Blood chemistry showed some differences but the values were comparable among the dietary groups. The present study demonstrated that BC supplementation is effective for improving the utilization of a plant by-product mixture in low fishmeal and comparatively higher level of plant protein based diet for juvenile amberjack. It is assuming that BC could absorb the noxious substances present in PBM which might improve the utilization of diets. Based on the present experiment, it is concluded that 5-10% PBM along with 4% BC in 25% fishmeal based diet is effective supplementation for better performances of juvenile amberjack.