Miracle-dz/UniFAR-FTUs huggingface dataset:
Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares

We provide a new full-commitment intertemporal collective household model to estimate resource shares, defined as the fraction of household expenditure enjoyed by household members. Our model implies nonlinear time-varying household quantity demand functions that depend on fixed effects. We provide new econometric results showing identification of a large class of models that includes our household model. We cover fixed-$T$ panel models where the response variable is an unknown monotonic function of a linear latent variable with fixed effects, regressors, and a nonparametric error term. The function may be weakly monotonic and time-varying, and the fixed effects are unrestricted. We identify the structural parameters and features of the distribution of fixed effects. In our household model, these correspond to features of the distribution of resource shares. Using Bangladeshi data, we show: women's resource shares decline with household budgets; and, half the variation in women's resource shares is due to unobserved heterogeneity.
Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free cameras

Reliable detection of human occupancy in indoor environments is critical for various energy efficiency, security, and safety applications. We consider this challenge of occupancy detection using extremely low-quality, privacy-preserving images from low power image sensors. We propose a combined few shot learning and clustering algorithm to address this challenge that has very low commissioning and maintenance cost. While the few shot learning concept enables us to commission our system with a few labeled examples, the clustering step serves the purpose of online adaptation to changing imaging environment over time. Apart from validating and comparing our algorithm on benchmark datasets, we also demonstrate performance of our algorithm on streaming images collected from real homes using our novel battery free camera hardware.
Highly nonclassical phonon emission statistics through two-phonon loss of van der Pol oscillator

The ability to produce nonclassical wave in a system is essential for advances in quantum communication and computation. Here, we propose a scheme to generate highly nonclassical phonon emission statistics -- antibunched wave in a quantum van der Pol (vdP) oscillator subject to an external driving, both single- and two-phonon losses. It is found that phonon antibunching depends significantly on the nonlinear two-phonon loss of the vdP oscillator, where the degree of the antibunching increases monotonically with the two-phonon loss and the distinguished parameter regimes with optimal antibunching and single-phonon emission are identified clearly. In addition, we give an in-depth insight into strong antibunching in the emitted phonon statistics by analytical calculations using a three-oscillator-level model, which agree well with the full numerical simulations employing a master-equation approach and a Schrodinger-equation approach at weak driving. In turn, the fluorescence phonon emission spectra of the vdP oscillator, given by the power spectral density, are also evaluated. We further show that high phonon emission amplitudes, simultaneously accompanied by strong phonon antibunching, are attainable in the vdP system, which are beneficial to the correlation measurement in practical experiments. Our approach only requires a single vdP oscillator, without the need for reconfiguring the two coupled nonlinear resonators or the complex nanophotonic structures as compared to the previous unconventional blockade schemes. The present scheme could inspire methods to achieve antibunching in other systems.
Cognitive Representation Learning of Self-Media Online Article Quality

The automatic quality assessment of self-media online articles is an urgent and new issue, which is of great value to the online recommendation and search. Different from traditional and well-formed articles, self-media online articles are mainly created by users, which have the appearance characteristics of different text levels and multi-modal hybrid editing, along with the potential characteristics of diverse content, different styles, large semantic spans and good interactive experience requirements. To solve these challenges, we establish a joint model CoQAN in combination with the layout organization, writing characteristics and text semantics, designing different representation learning subnetworks, especially for the feature learning process and interactive reading habits on mobile terminals. It is more consistent with the cognitive style of expressing an expert's evaluation of articles. We have also constructed a large scale real-world assessment dataset. Extensive experimental results show that the proposed framework significantly outperforms state-of-the-art methods, and effectively learns and integrates different factors of the online article quality assessment.
Ranking Enhanced Dialogue Generation

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from.
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay

Recent work proposed the computation of so-called PI-explanations of Naive Bayes Classifiers (NBCs). PI-explanations are subset-minimal sets of feature-value pairs that are sufficient for the prediction, and have been computed with state-of-the-art exact algorithms that are worst-case exponential in time and space. In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers. Furthermore, we show that the enumeration of PI-explanations can be obtained with polynomial delay. Experimental results demonstrate the performance gains of the new algorithms when compared with earlier work. The experimental results also investigate ways to measure the quality of heuristic explanations
The CHIME Pulsar Project: System Overview

We present the design, implementation, and performance of the digital pulsar observing system constructed for the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Using accelerated computing, this system processes independent, digitally steered beams formed by the CHIME correlator to simultaneously observe up to 10 radio pulsars and transient sources. Each of these independent streams is processed by the CHIME/Pulsar back-end system, which can coherently dedisperse, in real time, up to dispersion measure values of 2500 pc cm−3. The tracking beams and real-time analysis system are autonomously controlled by a priority-based algorithm that schedules both known sources and positions of interest for observation with observing cadences as rapid as 1 day. Given the distribution of known pulsars and radio-transient sources and the dynamic scheduling, the CHIME/Pulsar system can monitor 400–500 positions once per sidereal day and observe most sources with declinations greater than −20° once every ∼4 weeks. We also discuss the extensive science program enabled through the current modes of data acquisition for CHIME/Pulsar that centers on timing and searching experiments.
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation

This paper proposes a novel model for video generation and especially makes the attempt to deal with the problem of video generation from text descriptions, i.e., synthesizing realistic videos conditioned on given texts. Existing video generation methods cannot be easily adapted to handle this task well, due to the frame discontinuity issue and their text-free generation schemes. To address these problems, we propose a recurrent deconvolutional generative adversarial network (RD-GAN), which includes a recurrent deconvolutional network (RDN) as the generator and a 3D convolutional neural network (3D-CNN) as the discriminator. The RDN is a deconvolutional version of conventional recurrent neural network, which can well model the long-range temporal dependency of generated video frames and make good use of conditional information. The proposed model can be jointly trained by pushing the RDN to generate realistic videos so that the 3D-CNN cannot distinguish them from real ones. We apply the proposed RD-GAN to a series of tasks including conventional video generation, conditional video generation, video prediction and video classification, and demonstrate its effectiveness by achieving well performance.
Analyzing Who and What Appears in a Decade of US Cable TV News

Cable TV news reaches millions of U.S. households each day, and decisions about who appears on the news, and what stories get talked about, can profoundly influence public opinion and discourse. In this paper, we use computational techniques to analyze a data set of nearly 24/7 video, audio, and text captions from three major U.S. cable TV networks (CNN, FOX News, and MSNBC) from the last decade. Using automated machine learning tools, we detect faces in 244,038 hours of video, label their presented gender, identify prominent public figures, and align text captions to audio. We use these labels to perform face screen time and caption word frequency analyses of the contents of cable TV news. For example, we find that the ratio of female-presenting to male-presenting individuals has increased from 0.41 to 0.54 over the last decade. Donald Trump and Barack Obama received the most screen time over the last decade, with Trump receiving twice the screen time of Obama. Hillary Clinton's face was on screen 11% of the time when "email" was said in 2015 and 2016. In addition to reporting the results of our own analyses, we describe the design of an interactive web-based tool that allows the general public to perform their own screen time analyses on the entire cable TV news data set.
Lift and drag forces acting on a particle moving in the presence of slip and shear near a wall

Abstract The lift and drag forces acting on a small spherical particle moving with a finite slip in single-wall-bounded flows are investigated via direct numerical simulations. This study is an extension of our previous work that considered the lift and drag forces acting on a sphere moving near a wall in the presence shear, but in the absence of slip (Ekanayake et al., J. Fluid Mech., vol. 904, 2020, A6). The effect of slip velocity on the particle force is analysed as a function of separation distance for low slip and shear Reynolds numbers ($10^{-3} \leq Re_{{slip}} \leq 10^{-1}$ and $10^{-3} \leq Re_{\gamma } \leq 10^{-1}$) in both quiescent and linear shear flows. A generalised lift model valid for arbitrary particle–wall separation distances and $Re_{\gamma }, Re_{{slip}} \leq 10^{-1}$ is developed based on the results of the simulations. The proposed model can now predict the lift forces in linear shear flows in the presence or absence of slip, and in quiescent flows when slip is present. Existing drag models are also compared with numerical results for both quiescent and linear shear flows to determine which models capture near-wall slip velocities most accurately for low particle Reynolds numbers. Finally, we compare the results of the proposed lift model to previous experimental results of negatively buoyant particles and to numerical results of neutrally buoyant (force-free) particles moving near a wall in quiescent and linear shear flows. The generalised lift model presented can be used to predict the behaviour of particle suspensions in biological and industrial flows where the particle Reynolds numbers based on slip and shear are ${O}(10^{-1})$ and below.
Global patterns of tree species richness in moist forests : another look

After examining some perceived inconsistencies in the relationship between species richness and energy, Latham and Ricklefs rejected the Energy Diversity Theory in favour of historical explanations of the geographical patterns of tree species richness. We have re-examined Latham and Ricklefs's data, both by itself and pooled with Currie and Paquin's data. We find that Latham and Ricklefs's data are, in fact, consistent with the richness-energy hypothesis. Because of strong collinearity between annual evapotranspiration and region in their particular data sets, the data cannot be used to distinguish between the hypotheses that contemporary climate, versus some other interregional difference, is responsible for observed species richness patterns. Finally, there is no particular reason to attribute differences among regions to historical factors rather than to contemporary ones.
Emergence of dynamic vortex glasses in disordered polar active fluids

Significance Active systems, from bacteria to human crowds, often move through disordered environments, but the impact of quenched disorder on self-organized collective flow remains largely unexplored. We combine microfluidic experiments involving a colloidal active fluid and hydrodynamic theory to study a flocking fluid navigating a disordered substrate. While weak disorder hardly disrupts the robust collective motion, above a critical disorder strength the system organizes in a remarkable state of correlated flows through a frozen network of defects—a dynamical vortex glass, akin to vortex glasses in dirty superconductors. Strong disorder and activity conspire to generate a gauge field that proliferates pinned vortices, a general mechanism that may apply to a broad class of synthetic and biological active fluids. In equilibrium, disorder conspires with topological defects to redefine the ordered states of matter in systems as diverse as crystals, superconductors, and liquid crystals. Far from equilibrium, however, the consequences of quenched disorder on active condensed matter remain virtually uncharted. Here, we reveal a state of strongly disordered active matter with no counterparts in equilibrium: a dynamical vortex glass. Combining microfluidic experiments and theory, we show how colloidal flocks collectively cruise through disordered environments without relaxing the topological singularities of their flows. The resulting state is highly dynamical but the flow patterns, shaped by a finite density of frozen vortices, are stationary and exponentially degenerated. Quenched isotropic disorder acts as a random gauge field turning active liquids into dynamical vortex glasses. We argue that this robust mechanism should shape the collective dynamics of a broad class of disordered active matter, from synthetic active nematics to collections of living cells exploring heterogeneous media.
Core-collapse Supernovae: From Neutrino-driven 1D Explosions to Light Curves and Spectra

We present bolometric and broadband light curves and spectra for a suite of core-collapse supernova models exploded self-consistently in spherical symmetry within the PUSH framework. We analyze broad trends in these light curves and categorize them based on morphology. We find that these morphological categories relate simply to the progenitor radius and mass of the hydrogen envelope. We present a proof-of-concept sensitive-variable analysis, indicating that an important determining factor in the properties of a light curve within a given category is 56Ni mass. We follow spectra from the photospheric to the nebular phase. These spectra show characteristic iron-line blanketing at short wavelengths and Doppler-shifted Fe ii and Ti ii absorption lines. To enable this analysis, we develop a first-of-its-kind pipeline from a massive progenitor model, through a self-consistent explosion in spherical symmetry, to electromagnetic counterparts. This opens the door to more detailed analyses of the collective properties of these observables. We provide a machine-readable database of our light curves and spectra online at go.ncsu. edu/astrodata.
Construction and Investigation of MicroRNA-mRNA Regulatory Network of Gastric Cancer with Helicobacter pylori Infection

Background Helicobacter pylori (H. pylori) is a common human pathogen, which is closely correlated with gastric cancer (GC). However, the mechanism of H. pylori-related GC has not been elucidated. This study aimed to explore the role of H. pylori infection in GC and find biomarkers for early diagnosis of H. pylori-related GC. Methods We identified differentially expressed microRNAs (DEMs) and genes (DEGs) from the Gene Expression Omnibus (GEO) dataset, constructed microRNA-(miRNA-)mRNA expression networks, analyzed the function and signal pathway of cross-genes, analyzed the relations between cross-genes and GC prognosis with the Cancer Genome Atlas (TCGA) data, and verified the expression of cross-genes in patients with H. pylori infection. Results 22 DEMs and 68 DEGs were identified in GSE197694 and GSE27411 dataset. 16 miRNAs and 509 genes were involved in the expression network, while the cross-genes of the network were mainly enriched in MAP kinase (MAPK) signaling pathway and TGF-beta signaling pathway. Patients with higher expression of hsa-miR-196b-3p, CALML4, or SMAD6 or lower expression of PITX2 or TGFB2 had better outcomes than those with lower expression of hsa-miR-196b-3p, CALML4, or SMAD6 or higher expression of PITX2 or TGFB2 (P < 0.05). Patients with H. pylori infection had a higher expression of hsa-miR-196b-3p and CALML4 than those without H. pylori infection (P < 0.05). Conclusion The study of miRNA-mRNA expression network would provide molecular support for early diagnosis and treatment of H. pylori-related GC.
Temporal trends in bird abundance in response to thinning of lodgepole pine (Pinus contorta)

Forest managers are continually seeking methods of extracting more timber from the land base while maintaining appropriate wildlife habitat. A 7-year before–after control–impact (BACI) experiment was conducted in 60-year-old lodgepole pine (Pinus contorta Douglas ex Loudon) to determine the effects of thinning on forest birds. One year of pretreatment data was compared with 6 years of posttreatment data. Using linear trend analysis corrected for detection error, we found that Chipping Sparrow (Spizella passerina (Bechstein, 1798)), Dark-eyed Junco (Junco hyemalis (Linnaeus, 1758)), and Red-eyed Vireo (Vireo olivaceus (Linnaeus, 1766)) increased over time in thinned stands and declined in control stands. Alder Flycatcher (Empidonax alnorum Brewster, 1895), Hermit Thrush (Catharus guttatus (Pallas, 1811)), Rose-breasted Grosbeak (Pheucticus ludovicianus (Linnaeus, 1766)), and Blue-headed Vireo (Vireo solitarius (Wilson, 1810)) decreased in control and thinned stands but declined more in controls. Varied Thr.
Real-World Clinical Outcomes Associated with Canagliflozin in Patients with Type 2 Diabetes Mellitus in Spain: The Real-Wecan Study

The aims of this multicentric retrospective study were to assess in a real-world setting the effectiveness and safety of canagliflozin 100 mg/d (CANA100) as an add-on to the background antihyperglycemic therapy, and to evaluate the intensification of prior sodium–glucose co-transporter type 2 inhibitor (SGLT-2i) therapy by switching to canagliflozin 300 mg/d (CANA300) in patients with T2DM. One cohort of SGLT2i-naïve patients with T2DM who were initiated on CANA100 and a second cohort of patients with prior background SGLT-2i therapy who switched to CANA300 were included in the study. The primary outcome of the study was the mean change in HbA1c over the follow-up time. In total, 583 patients were included—279 in the cohort of CANA100 (HbA1c 8.05%, weight 94.9 kg) and 304 in the cohort of CANA300 (HbA1c 7.51%, weight 92.0 kg). Median follow-up periods in both cohorts were 9.1 and 15.4 months respectively. CANA100 was associated to significant reductions in HbA1c (−0.90%) and weight (−4.1 kg) at the end of the follow-up. In those patients with baseline HbA1c > 8% (mean 9.25%), CANA100 lowered HbA1c levels by 1.51%. In the second cohort, patients switching to CANA300 experienced a significant decrease in HbA1c (−0.35%) and weight (−2.1 kg). In those patients with baseline HbA1c > 8% (mean 8.94%), CANA300 lowered HbA1c levels by 1.12%. There were significant improvements in blood pressure in both cohorts. No unexpected adverse events were reported. In summary, CANA100 (as an add-on therapy) and CANA300 (switching from prior SGLT-2i therapy) significantly improved several cardiometabolic parameters in patients with T2DM.
Metric learning vs classification for disentangled music representation learning

Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two central methods for representation learning include deep metric learning and classification, both having the same goal of learning a representation that can generalize well across tasks. Along with generalization, the emerging concept of disentangled representations is also of great interest, where multiple semantic concepts (e.g., genre, mood, instrumentation) are learned jointly but remain separable in the learned representation space. In this paper we present a single representation learning framework that elucidates the relationship between metric learning, classification, and disentanglement in a holistic manner. For this, we (1) outline past work on the relationship between metric learning and classification, (2) extend this relationship to multi-label data by exploring three different learning approaches and their disentangled versions, and (3) evaluate all models on four tasks (training time, similarity retrieval, auto-tagging, and triplet prediction). We find that classification-based models are generally advantageous for training time, similarity retrieval, and auto-tagging, while deep metric learning exhibits better performance for triplet-prediction. Finally, we show that our proposed approach yields state-of-the-art results for music auto-tagging.
Numerical estimates of lattice star vertex exponents

Numerical values of lattice star vertex exponents are estimated using parallel implementations of the GARM and Wang-Landau algorithms in the square and cubic lattices. In the square lattice the results are consistent with exact values of the exponents, but in the cubic lattice there are deviations from the predictions of the $\epsilon$-expansion. In addition, the entropic exponents of acyclic branched lattice networks are calculated, and found to be consistent with the predicted values in terms of star vertex exponents.