Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Learning with Neuron Groups

Traditional deep network training methods optimize a monolithic objective function jointly for all the components. This can lead to various inefficiencies in terms of potential parallelization. Local learning is an approach to model-parallelism that removes the standard end-to-end learning setup and utilizes local objective functions to permit parallel learning amongst model components in a deep network. Recent works have demonstrated that variants of local learning can lead to efficient training of modern deep networks. However, in terms of how much computation can be distributed, these approaches are typically limited by the number of layers in a network. In this work we propose to study how local learning can be applied at the level of splitting layers or modules into sub-components, adding a notion of width-wise modularity to the existing depth-wise modularity associated with local learning. We investigate local-learning penalties that permit such models to be trained efficiently. Our experiments on the CIFAR-10, CIFAR-100, and Imagenet32 datasets demonstrate that introducing width-level modularity can lead to computational advantages over existing methods based on local learning and opens new opportunities for improved model-parallel distributed training. Code is available at: https://github.com/adeetyapatel12/GN-DGL.
Chirality-induced emergent spin-orbit coupling with photons

Chirality, or handedness, is a geometrical property denoting a lack of mirror symmetry. Chiral systems are ubiquitous in nature and are associated with the non-reciprocal interactions observed in topological materials and complex biomolecules, among other phenomena. In this paper, we demonstrate that chiral arrangements of dipole-coupled quantum emitters can facilitate the unidirectional transport of photonic spin excitations without breaking time-reversal symmetry. We show that this spin-selective transport stems from a universal emergent spin-orbit coupling induced by the chiral geometry, which in turn, results in a nontrivial topology. We also examine the eﬀects of collective dissipation on the dynamics and ﬁnd that many-body coherences lead to helicity-dependent photon emission: an eﬀect we call helical superradiance. Applications to emerging quantum technologies as well as the potential role of spin-selective photon transport in nature are discussed.
Curriculum Script Distillation for Multilingual Visual Question Answering

Pre-trained models with dual and cross encoders have shown remarkable success in pro-pelling the landscape of several tasks in vision and language in Visual Question Answering (VQA). However, since they are limited by the requirements of gold annotated data, most of these advancements do not see the light of day in other languages beyond English. We aim to address this problem by introducing a curriculum based on the source and target language translations to ﬁnetune the pre-trained models for the downstream task. Experimental results demonstrate that script plays a vital role in the performance of these models. Speciﬁcally, we show that target languages that share the same script perform better ( ∼ 6%) than other languages and mixed-script code-switched languages perform better than their counterparts ( ∼ 5-12%).
News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand

The relationship between electricity demand and weather is well established in power systems, along with the importance of behavioral and social aspects such as holidays and significant events. This study explores the link between electricity demand and more nuanced information about social events. This is done using mature Natural Language Processing (NLP) and demand forecasting techniques. The results indicate that day-ahead forecasts are improved by textual features such as word frequencies, public sentiments, topic distributions, and word embeddings. The social events contained in these features include global pandemics, politics, international conflicts, transportation, etc. Causality effects and correlations are discussed to propose explanations for the mechanisms behind the links highlighted. This study is believed to bring a new perspective to traditional electricity demand analysis. It confirms the feasibility of improving forecasts from unstructured text, with potential consequences for sociology and economics.
Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

Monitoring online customer reviews is important for business organizations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.1
A Framework for Modeling Polycyclic Aromatic Hydrocarbon Emission in Galaxy Evolution Simulations

We present a new methodology for simulating mid-infrared emission from polycyclic aromatic hydrocarbons (PAHs) in galaxy evolution simulations. To do this, we combine theoretical models of PAH emission features as they respond to varying interstellar radiation fields, grain-size distributions, and ionization states with a new model for dust evolution in galaxy simulations. We apply these models to three idealized arepo galaxy evolution simulations within the smuggle physics framework. We use these simulations to develop numerical experiments investigating the buildup of PAH masses and luminosities in galaxies in idealized analogs of the Milky Way, a dwarf galaxy, and a starburst disk. Our main results are as follows. Galaxies with high specific star formation rates have increased feedback energy per unit mass, and are able to shatter grains efficiently, driving up the fraction of ultrasmall grains. At the same time, in our model large radiation fields per unit gas density convert aliphatic grains into aromatics. The fraction of dust grains in the form of PAHs (q PAH) can be understood as a consequence of these processes, and in our model PAHs form primarily from interstellar processing (shattering) of larger grains rather than from the growth of smaller grains. We find that the hardness of the radiation field plays a larger role than variations in the grain-size distribution in setting the total integrated PAH luminosities, though cosmological simulations are necessary to investigate fully the complex interplay of processes that drive PAH band luminosities in galaxies.
Continuous Trajectory Generation Based on Two-Stage GAN

Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect. Although many previous works have studied the problem of trajectory generation, the continuity of the generated trajectories has been neglected, which makes these methods useless for practical urban simulation scenarios. To solve this problem, we propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network, namely TS-TrajGen, which efficiently integrates prior domain knowledge of human mobility with model-free learning paradigm. Specifically, we build the generator under the human mobility hypothesis of the A* algorithm to learn the human mobility behavior. For the discriminator, we combine the sequential reward with the mobility yaw reward to enhance the effectiveness of the generator. Finally, we propose a novel two-stage generation process to overcome the weak point of the existing stochastic generation process. Extensive experiments on two real-world datasets and two case studies demonstrate that our framework yields significant improvements over the state-of-the-art methods.
Compression of GPS Trajectories using Autoencoders

The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed in order to reduce the amount of storage needed while keeping the important information. In this paper, we present an lstm-autoencoder based approach in order to compress and reconstruct GPS trajectories, which is evaluated on both a gaming and real-world dataset. We consider various compression ratios and trajectory lengths. The performance is compared to other trajectory compression algorithms, i.e., Douglas-Peucker. Overall, the results indicate that our approach outperforms Douglas-Peucker significantly in terms of the discrete Fr\'echet distance and dynamic time warping. Furthermore, by reconstructing every point lossy, the proposed methodology offers multiple advantages over traditional methods.
Characteristic Length and Time Scales Associated with the Impact of an Elasto-plastic Particle with a Large Hard Plane Surface

In this paper we analyze plastic collisions between spherical nano-scaled particles and a large hard plane. We assume for most of the paper that surface tension between the particle and plane accelerates the particle to a speed which is large compared with its initial speed. The subsequent maximum width and depth of plastic indentation of the particle by the plane is independent of the initial speed of the particle, being a function of particle size, and material properties. This depth of indentation is independent of particle size, and if it is assumed equal to one interatomic spacing in the colliding particle, then a relationship is derived linking nano-scale yield strength to the elastic modulus of the particle. Similarly, should surface tension overcome the elastic recoil process so that the particle is captured on the surface of the plane, then the period of the surface oscillations of the particle depends on the particle size and on material properties. This special regime for nano-scale collisions allows a dimensional argument to approximate the relative importance of fluid viscosity between the particle and plane, relative to plastic processes, even though the corresponding continuum mathematical problem is ill-defined. Finally, we identify a condition which determines when a collision process can be assumed to be a localized phenomenon, and argue that when this condition is not satisfied, then complex dynamical motions are likely.
A robust fitted operator finite difference method for a two-parameter singular perturbation problem1

We consider singularly perturbed two-point BVPs with two small parameters ϵ and μ multiplying the derivatives. It is pointed out in P.A. Farrel (Sufficient conditions for uniform convergence of a class of difference schemes for singular perturbation problems, IMA J. Numer. Anal. 7 (1987), pp. 459–472), that ‘in general, the exponentially fitted finite difference methods (EFFDMs) are more effective inside the layers. However, though these methods are uniformly convergent, they do not give fairly good approximations in the whole interval of interest’. In this paper, we study that the non-standard finite difference method (NSFDM) that we develop overcomes this weakness. Like EFFDMs, the NSFDM is also a method of fitted operator type. Secondly, unlike several earlier works (see, for example Gracia et al., Appl. Numer. Math. 56 (2006), pp. 962–980) where the authors use a combination of approaches in various regions, the method presented in this paper consists of just one scheme throughout the domain of interest. This is very important because it increases the possibilities of extending the approach both for higher dimensional and higher order problems. Combination of schemes usually suffers from the drawback that their selection is mostly based on the relative values of ϵ and μ, otherwise they fail to provide monotonic solutions. We also investigate a number of issues associated with a variety of NSFDMs and finally provide some comparative numerical results.
Uniform Asymptotic Theory of Diffraction by a Finite Cylinder

Uniform asymptotic expressions for the far field scattered by a finite solid cylinder are found by the method of edge waves. New results have been obtained for the field in the vicinity of the specular reflected ray. The role of the second term of the Luneburg–Kline’s expansion in forming the specular reflection is emphasized. Some computational errors in the Ahluwalia’s work (1970) are noted. After these errors are corrected, Ahluwalia’s theory gives exactly the same results as the method of edge waves.
The Well-Calibrated Bayesian

Abstract Suppose that a forecaster sequentially assigns probabilities to events. He is well calibrated if, for example, of those events to which he assigns a probability 30 percent, the long-run proportion that actually occurs turns out to be 30 percent. We prove a theorem to the effect that a coherent Bayesian expects to be well calibrated, and consider its destructive implications for the theory of coherence.
Antenatal Care Dropout and Associated Factors in Ethiopia: A Systematic Review and Meta-Analysis

Background: Antenatal care during pregnancy is one of the most important strategies for improving maternal and newborn health and preventing maternal and newborn mortality and morbidity. The prevalence and predictors of antenatal care dropout in Ethiopia were studied, and the results were inconsistent and showed considerable variation, and this makes more difficult to provide clear evidence at the national level. Hence, this meta-analysis aimed at estimating the overall prevalence of antenatal care dropout and its associated factors in Ethiopia. Methods: A thorough search of pertinent studies released before December 30, 2022, was explored by using distinct databases such as (PubMed, DOJA, Embase, Cochrane library, African journals online, Google scholar, and web of science and the institutional repository of Ethiopian universities were used to search for relevant studies. Data were extracted using Microsoft Excel, and analysis was performed using STATA version 16. A random-effects model were used to estimate the overall national prevalence of antenatal care drop-out and the odds ratio. test statistics for to assessing heterogeneity and Egger's test for assessing publication bias were used. Results: A total of seven studies were included for this systematic review and meta-analysis with of 11839 study participants. The overall pooled prevalence of antenatal dropout in Ethiopia was found to be 41.37% (95% CI =35.04, 47.70). Distance from the health care facility (AOR = 2.55, 95% CI = 1.79, 3.31), pregnancy complication signs (AOR = 2.88, 95% CI= 2.41, 3.66), place of residence (AOR= 1.59, 95% CI = 1.31, 1.87), educational level (AOR=1.79, 95%CI = 1.37, 2.21), age group(30-49) (AOR=(AOR = 0.57, 95% CI = 0.26, 0.88) were significantly associated with antenatal care dropout. Conclusion: Based on this systematic review and meta-analysis, 41% of Ethiopian women dropped out of antenatal care visits before the minimum recommended visit (four times Hence, to reduce the number of ANC dropouts it is important to counsel and educate women at their first prenatal care. Issues of urban-rural disparity and locations identified as hotspots for incomplete ANC visits require that further attention.
Asymptotic equivalence of nonparametric regression and white noise

The principal result is that, under conditions, to any nonparametric regression problem there corresponds an asymptotically equivalent sequence of white noise with drift problems, and conversely. This asymptotic equivalence is in a global and uniform sense. Any normalized risk function attainable in one problem is asymptotically attainable in the other, with the difference in normalized risks converging to zero uniformly over the entire parameter space. The results are constructive. A recipe is provided for producing these asymptotically equivalent procedures. Some implications and generalizations of the principal result are also discussed.
Dual-band frequency selective surface with quasi-elliptic bandpass response

Based on the substrate integrated waveguide technology, we present a dual-band frequency selective surface (FSS) with a quasi-elliptic bandpass response. The characteristics of the quasi-elliptic bandpass response are realized by shunting two substrate integrated waveguide cavities of different sizes, with the same slots on both sides of the metal surfaces. Four cavities of different sizes and two slots of different sizes are used to design the novel FSS. Every bandpass response with sharp sidebands is induced by two transmission nulls that are generated by the coupling between the slot aperture resonance and the cavity resonance. The simulation results show that such dual-band FSS has the advantages of high selectivity and stable performance at different oblique incident angles. Moreover, it is easy to fabricate.
An Extraction Method of Network Security Situation Elements Based on Gradient Lifting Decision Tree

The primary purpose of acquiring network security situation elements is to detect and discover potential security threats from discrete and isolated data. In the complex network environment, the existing network security situation element acquisition technology has the problems of low extraction accuracy and low extraction efficiency. To solve these problems, a method for extracting network security situation elements based on Gradient Boosting Decision Tree (GBDT) is proposed. This method uses the attribute reduction function of rough set to preprocess the original data, which can effectively reduce redundancy. Furthermore, the initial parameters of GBDT are optimized by quantum particle swarm optimization (QPSO) algorithm to improve stability. Finally, the optimized GBDT classifier is used to classify and train with the reduced data set, and the accuracy of the final classifier is continuously improved by reducing the deviation via iterative optimization. Experiments demonstrate that the proposed algorithm achieves significant results and outperforms several state-of-the-art algorithms to the extraction of network security situation elements on the UNSW-NB15 dataset.
Flexible image sensor array with bulk heterojunction organic photodiode

Thick organic bulk heterojunction photodiodes with low dark current 1V∕μm is sufficient to achieve >75% charge collection in films of poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylene-vinylene] and [6,6]-phenyl-C61-butyric acid methyl ester blends up to 4μm thick, and the rate of photocurrent decay is reduced at saturation fields. The integration of a 4μm thick sensor layer onto a flexible amorphous silicon thin-film transistor backplane gave an image sensor array with 35% external quantum efficiency and noise equivalent power of 30pW∕cm2 at reverse bias voltage of −4V.
Binary Stars in the Orion Trapezium Cluster Core

We have obtained high angular resolution (0.″13 FWHM) near-infrared images of the central ~40'' × 40'' of the Trapezium cluster, using a speckle holography technique that we describe in detail. A search for close binary systems was made in Ks (2.16 μm) and H (1.65 μm) mosaic images, in which 45 and 35 stars were detected, respectively. The sensitivity limits for stellar detections are Ks ≃ 14.8 and H ≃ 14.9 over the whole mosaics and Ks ≃ 16.0 and H ≃ 15.9 for those regions of the mosaics where most data were accumulated, thus potentially including objects with substellar masses down to ~0.04 M☉. In total, four binary systems were identified with projected linear separations in the range of 0. ″14-0. ″5 (63-225 AU). The resulting binary fraction for low-mass pre-main-sequence stars is 5.9% ± 4.0%. This fraction agrees well with the binary frequency observed for main-sequence field stars, but is lower by a factor of ~3 than the fraction found from observations of young stars in Taurus-Auriga over the same range of separations. The difference in binary frequency between the core of the Trapezium cluster and the low-mass, low stellar density dark cloud Taurus-Auriga is established at a statistical significance level of 96% and suggests that binary frequencies are affected by the local star-forming environment. We show that the massive Trapezium star θ1 Ori A has a nearby companion separated by ~0. ″2 (~90 AU). The location of this companion is coincident, within the positional uncertainties, with a nonthermal and variable VLA radio source, which was previously associated with θ1 Ori A itself. We give H photometry for 32 stars, Ks photometry for 43 stars, and present a color-magnitude diagram for the Trapezium core.
Optical sensing elements based on ordered semiconductor and metal nanoparticle arrays and surface plasmons

The optical properties of regular nanoparticle arrays consisting of spherical semiconductor and noble metal nanoparticles are providing interesting aspects for the development of novel and powerful sensor concepts. In this contribution, we demonstrate femtosecond laser-induced transfer of metallic and semiconductor thin films as a unique tool for realizing controllable structures of any desired configuration of exactly spherical nanoparticles, having diameters between 40 nm and 1500 nm. The optical properties of nanoparticles and nanoparticle arrays fabricated by this new approach are investigated spectroscopically and by scattering of surface plasmon-polaritons (SPPs). SPP-scattering constitutes a novel method to obtain insight into the contribution of different multipole moments to the scattering properties of the particles. Furthermore, the particles can be combined with 3D photonic structures fabricated using two-photon polymerization, providing new approaches to the development of nanophotonic devices and 3D metamaterials. Here, we demonstrate an optical sensor with a sensitivity of 365 nm/RIU and a figure of merit of 21.5 in the visible spectral range.
Learning to Rank by Causal Effects Without Data to Accurately Estimate Causal Effects

Decision makers often want to identify the individuals for whom some intervention or treatment will be most eﬀective in order to decide who to treat. In such cases, decision makers would ideally like to rank potential recipients of the treatment according to their individual causal eﬀects. However, the available data may be completely inadequate to estimate causal eﬀects accurately. We formalize a new assumption—the rank preservation assumption (RPA)—that deﬁnes when data are suitable to learn how to rank individuals according to their causal eﬀects, even if the eﬀects themselves cannot be accurately estimated. The RPA holds when there is data to estimate a scoring variable that induces the same ranking of individuals as the causal eﬀect of interest. Some of the scoring variables we consider are confounded estimates, proxy causal eﬀects, and non-causal quantities. We show that such scoring variables can work well for treatment assignment if the RPA is met, and potentially even better than using causal eﬀects as scores. We also show that the RPA holds under conditions that are more general and weaker than the typical assumptions made in observational studies. Finally, we showcase how practitioners can apply and evaluate alternative scoring models (including non-causal models) to maximize the causal impact of their targeting decisions.