Miracle-dz/UniFAR-FTUs huggingface dataset:
When Do Local Discriminators Work? On Subadditivity of Probability Divergences

Local discriminators have been employed in deep generative models, in image-to-image translation methods, in analyzing time-series data, etc. The approach is to apply local discriminators to different patches of an image or subsequences of time-series data, resulting in improved generation quality, reduced discriminator size, and faster and more stable training dynamics. These empirical successes, however, are based on heuristics; it is not clear what subset of features each local discriminator should be applied to, and there are no theoretical guarantees about the effect of the discriminator localization on estimating the distance between the generated and target distributions. In this paper, we provide theoretical foundations to answer these questions for high-dimensional distributions with conditional independence structure captured by either a Bayesian network or a Markov Random Field (MRF). Our results are based on subadditivity properties of probability divergences, which establish upper bounds on the distance between two high-dimensional distributions by the sum of distances between their marginals over (local) neighborhoods of the graphical structure of the Bayes-net or the MRF. We prove that several popular probability divergences, including Jensen-Shannon, Total Variation, Wasserstein, Integral Probability Metrics (IPMs), and nearly all f-divergences, satisfy some notion of subadditivity under mild conditions. Thus, given an underlying feature dependency graph and using our theoretical results, one can use, in a principled way, a set of simple local discriminators, rather than a giant discriminator on the entire graph, providing significant statistical and computational benefits. Our experiments on synthetic as well as real-world datasets demonstrate the benefits of using our principled design of local discriminators in generative models.
Improved Inference for Heterogeneous Treatment Effects Using Real-World Data Subject to Hidden Confounding

The heterogeneity of treatment effect (HTE) lies at the heart of precision medicine. Randomized clinical trials (RCTs) are gold-standard to estimate the HTE but are typically underpowered. While real-world data (RWD) have large predictive power but are often confounded due to lack of randomization of treatment. In this article, we show that the RWD, even subject to hidden confounding, may be used to empower RCTs in estimating the HTE using the confounding function. The confounding function summarizes the impact of unmeasured confounders on the difference in the potential outcome between the treated and untreated groups, accounting for the observed covariates, which is unidentifiable based only on the RWD. Coupling the RCT and RWD, we show that the HTE and confounding function are identifiable. We then derive the semiparametric efficient scores and integrative estimators of the HTE and confounding function. We clarify the conditions under which the integrative estimator of the HTE is strictly more efficient than the RCT estimator. As a by-product, our framework can be used to generalize the average treatment effects from the RCT to a target population without requiring an overlap covariate distribution assumption between the RCT and RWD. We illustrate the integrative estimators with a simulation study and an application.
Organic Solar Cells; Fabrication Technique, Operating Principle, Characterization and Improvement

Organic solar cells (OSCs) have received a special attention over the past years due to their solution processability, low cost, flexibility and capability of role-to-role production. The power conversion efficiency of these devices has been significantly increased over the past decades from 1% in 1986 to 5% in 2005 and to up to 13% in 2017 thanks to the molecular optimization and the use of non-fullerene acceptors in their active materials. Despite such interesting efficiency, their applications remain limited so far because of instability and short life time of their active layers. It is expected that these obstacles will be surmounted in a foreseeable future upon rigorous research studies performed in the field. This paper is devoted to reviewing the operating principle, characterization parameters and the most important approaches that are considered aiming at improving the overall performance of these devices.
Excitation of symmetry protected modes in a lithium niobate membrane photonic crystal for sensing applications

Our theoretical study reveals the opportunity to develop an electric field sensor basedon the exploitation of Symmetry Protected Mode (SPM) that we excite within an electro-opticalmaterial, namely lithium niobate (LN). The SPM consists of a dark Fano-like resonance thatresults from the combination of a discrete Bloch mode of a Photonic Crystal (PhC) with acontinuum mode of a membrane, both of them made in LN. The dark character is linked tothe structure geometry having a high degree of symmetry. The SPM excitation is then madepossible thanks to an illumination under small oblique incidence which breaks the symmetry ofthe configuration. This results in several ultra-sensitive and tunable Fano-like resonances withhigh quality factors up to 10^5 in the telecoms spectral range. Some of these resonances providemodes with a highly confined electric field inside LN. This confinement allows the enhancementof the electro-optic Pockels effect by a factor up to 4x10^5, thus exacerbating the detectionsensitivity of the designed sensor.
A Unified Survey of Treatment Effect Heterogeneity Modelling and Uplift Modelling

A central question in many fields of scientific research is to determine how an outcome is affected by an action, i.e., to estimate the causal effect or treatment effect of an action. In recent years, in areas such as personalised healthcare, sociology, and online marketing, a need has emerged to estimate heterogeneous treatment effects with respect to individuals of different characteristics. To meet this need, two major approaches have been taken: treatment effect heterogeneity modelling and uplifting modelling. Researchers and practitioners in different communities have developed algorithms based on these approaches to estimate the heterogeneous treatment effects. In this article, we present a unified view of these two seemingly disconnected yet closely related approaches under the potential outcome framework. We provide a structured survey of existing methods following either of the two approaches, emphasising their inherent connections and using unified notation to facilitate comparisons. We also review the main applications of the surveyed methods in personalised marketing, personalised medicine, and sociology. Finally, we summarise and discuss the available software packages and source codes in terms of their coverage of different methods and applicability to different datasets, and we provide general guidelines for method selection.
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement

We present MRGAN, a multi-rooted adversarial network which generates part-disentangled 3D point-cloud shapes without part-based shape supervision. The network fuses multiple branches of tree-structured graph convolution layers which produce point clouds, with learnable constant inputs at the tree roots. Each branch learns to grow a different shape part, offering control over the shape generation at the part level. Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind. Of these, a novel convexity loss incentivizes the generation of parts that are more convex, as semantic parts tend to be. In addition, a root-dropping loss further ensures that each root seeds a single part, preventing the degeneration or over-growth of the point-producing branches. We evaluate the performance of our network on a number of 3D shape classes, and offer qualitative and quantitative comparisons to previous works and baseline approaches. We demonstrate the controllability offered by our part-disentangled generation through two applications for shape modeling: part mixing and individual part variation, without receiving segmented shapes as input.
Detection and annotation of plant organs from digitised herbarium scans using deep learning

Abstract As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN. For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but were not equally well recognised.
Holographic Theories at Finite θ‐Angle, CP‐Violation, Glueball Spectra and Strong‐Coupling Instabilities

A general class of holographic theories with a nontrivial θ‐angle are analyzed. The instanton density operator is dual to a bulk axion field. We calculate the ground‐state solutions with nontrivial source, aUV , for the axion, for both steep and soft dilaton potentials in the IR, and both in d=3 and d=4 . We find all cases to be qualitatively similar. We also calculate the spin=2, 0 glueball spectra and show that the glueball masses monotonically decrease as functions of aUV (or θ‐angle). The slopes of glueball masses are different, generically, in different potentials. In the case of steep dilaton potentials, the glueball (masses)2 turn negative before the maximum of aUV is attained. We interpret this as a signal for a favored instanton condensation in the bulk. We also investigate strong CP‐violation in the effective glueball action.
Artificial neural network for estimating monthly reference evapotransiration under arid and semi arid environments

The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six network solutions on five inputs for each monitoring station were done. Comparison of the results showed that the accuracy of ANNs is decreased when relative humidity, wind speed and solar or extraterrestrial radiation are excluded as input variables. The results also showed that monthly evapotranspiration could be computed with relatively good accuracy compared with local calibrated Hargreaves equation based on air temperature using trained ANNs at another location. We conclude, based on our overall results, that temperature-based method ANNs can be used with relatively good accuracy for water resource management, irrigation scheduling and management, and environmental assessment when data are not enough using trained ANNs from another location.
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

Counterfactual evaluation can estimate Click-Through-Rate (CTR) differences between ranking systems based on historical interaction data, while mitigating the effect of position bias and item-selection bias. We introduce the novel Logging-Policy Optimization Algorithm (LogOpt), which optimizes the policy for logging data so that the counterfactual estimate has minimal variance. As minimizing variance leads to faster convergence, LogOpt increases the data-efficiency of counterfactual estimation. LogOpt turns the counterfactual approach - which is indifferent to the logging policy - into an online approach, where the algorithm decides what rankings to display. We prove that, as an online evaluation method, LogOpt is unbiased w.r. t. position and item-selection bias, unlike existing interleaving methods. Furthermore, we perform large-scale experiments by simulating comparisons between thousands of rankers. Our results show that while interleaving methods make systematic errors, LogOpt is as efficient as interleaving without being biased.
Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation

In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal that mimics backpropagation but is far less computationally intensive. The weight update calculation at each layer requires only local data apart from this signal. We also employ a rule capable of producing specific output spike trains; by setting the target spike time equal to the actual spike time with a slight negative offset for key high-value neurons the actual spike time becomes as early as possible. In simulated MNIST handwritten digit classification, two-layer networks trained with this rule matched the performance of a comparable backpropagation based non-spiking network.
IEEE 802.11be – Extremely High Throughput WLAN: New Challenges and Opportunities

—With the emergence of 4k/8k video, the throughput requirement of video delivery will keep grow to tens of Gbps. Other new high-throughput and low-latency video applications including augmented reality (AR), virtual reality (VR), and online gaming, are also proliferating. Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard – IEEE 802.11ax. To meet these emerging demands, the IEEE 802.11 will release a new amendment standard IEEE 802.11be – Extremely High Throughput (EHT), also known as Wireless-Fidelity (Wi-Fi) 7. This article provides the comprehensive survey on the key medium access control (MAC) layer techniques and physical layer (PHY) techniques being discussed in the EHT task group, including the channelization and tone plan, multiple resource units (multi-RU) support, 4096 quadrature amplitude modulation (4096-QAM), preamble designs, multiple link operations (e.g., multi-link aggregation and channel access), multiple input multiple output (MIMO) enhancement, multiple access point (multi-AP) coordination (e.g., multi-AP joint transmission), enhanced link adaptation and retransmission protocols (e.g., hybrid automatic repeat request (HARQ)). This survey covers both the critical technologies being discussed in EHT standard and the related latest progresses from worldwide research. Besides, the potential developments beyond EHT are discussed to provide some possible future research directions for WLAN.
Chunghyul-dan acts as an anti-inflammatory agent in endothelial cells by regulating gene expression

Abstract Chunghyul-dan (CHD) is a combinatorial drug known to exert anti-inflammatory effects in endothelial cells. In this study, we employed global transcriptional profiling using cDNA microarrays to identify molecular mechanisms responsible for the anti-inflammatory activity of CHD in endothelial cells. An analysis of the microarray data revealed that transcript levels of monocyte chemotactic protein-1 (MCP-1), vascular cell-adhesion molecule-1 (VCAM-1) and activated leukocyte cell-adhesion molecule were dramatically altered in CHD-treated endothelial cells. These changes in gene expression were confirmed by RT-PCR, Western blotting and ELISA. Chronic CHD treatment also appeared to decrease MCP-1 secretion, probably as a result of decreased MCP-1 expression. In addition, we determined that chronic CHD treatment inhibited lipopolysaccharide-stimulated adhesion of THP-1 leukocytes to endothelial cells. The inhibitory effect of CHD on LPS-stimulated adhesion resulted from down-regulation of VCAM-1 expression. Transmigration of THP-1 leukocytes through endothelial cells was also inhibited by chronic CHD treatment. In conclusion, CHD controls a variety of inflammatory activities by regulating MCP-1 and VCAM-1 gene expression.
Ionization-density-dependent Scintillation Pulse Shape and Mechanism of Luminescence Quenching in LaBr
3
:Ce

Pulse shape discrimination (PSD) is usually achieved using the different fast and slow decay components of inorganic scintillators, such as BaF2, CsI:Tl, etc. However, LaBr3:Ce is considered to not possess different components at room temperature, but has been proved to have the capability of discriminating gamma and alpha events using fast digitizers. The physical mechanism of such PSD capability of single-decay component LaBr3:Ce was still unclear. Ionization density-dependent transport and rate equations are used to quantitatively model the competing processes in a particle track. With one parameter set, the model reproduces the non-proportionality response of electrons or alpha particles, and predicts the measured {\alpha}/{\gamma} pulse shape difference. In particular, the nonlinear quenching of excited dopant ions, Ce3+, is confirmed herein for the first time to mainly contribute observable ionization {\alpha}/{\gamma} pulse shape differences. Further study of the luminescence quenching can also help to better understand the fundamental physics of nonlinear quenching and thus improve the crystal engineering. Moreover, based on the mechanism of dopant quenching, the ionization density-dependent pulse shape differences in other fast single-decay-component inorganic scintillators, such as LYSO and CeBr3, are also predicted and verified with experiments.
PTF11rka: an interacting supernova at the crossroads of stripped-envelope and H-poor superluminous stellar core collapses

The hydrogen-poor supernova (SN) PTF11rka (z = 0.0744), reported by the Palomar Transient Factory, was observed with various telescopes starting a few days after the estimated explosion time of 2011 December 5 UT and up to 432 rest-frame days thereafter. The rising part of the light curve was monitored only in the RPTF filter band, and maximum in this band was reached ∼30 rest-frame days after the estimated explosion time. The light curve and spectra of PTF11rka are consistent with the core-collapse explosion of a ∼10 M⊙ carbon–oxygen core evolved from a progenitor of main-sequence mass 25–40 M⊙, that liberated a kinetic energy Ek≈4 × 1051 erg, expelled ∼8 M⊙ of ejecta, and synthesized ∼0.5 M⊙ of 56Ni. The photospheric spectra of PTF11rka are characterized by narrow absorption lines that point to suppression of the highest ejecta velocities (≳ 15 000 km s−1). This would be expected if the ejecta impacted a dense, clumpy circumstellar medium. This in turn caused them to lose a fraction of their energy (∼5 × 1050 erg), less than 2 per cent of which was converted into radiation that sustained the light curve before maximum brightness. This is reminiscent of the superluminous SN 2007bi, the light-curve shape and spectra of which are very similar to those of PTF11rka, although the latter is a factor of 10 less luminous and evolves faster in time. PTF11rka is in fact more similar to gamma-ray burst SNe in luminosity, although it has a lower energy and a lower Ek/Mej ratio.
An adaptive finite element DtN method for the elastic wave scattering by biperiodic structures

Consider the scattering of a time-harmonic elastic plane wave by a bi-periodic rigid surface. The displacement of elastic wave motion is modeled by the three-dimensional Navier equation in an unbounded domain above the surface. Based on the Dirichlet-to-Neumann (DtN) operator, which is given as an infinite series, an exact transparent boundary condition is introduced and the scattering problem is formulated equivalently into a boundary value problem in a bounded domain. An a posteriori error estimate based adaptive finite element DtN method is proposed to solve the discrete variational problem where the DtN operator is truncated into a finite number of terms. The a posteriori error estimate takes account of the finite element approximation error and the truncation error of the DtN operator which is shown to decay exponentially with respect to the truncation parameter. Numerical experiments are presented to illustrate the effectiveness of the proposed method.
Spatial integrals in non-standard dimensions via Gaussian measure and analytic continuation

Non-integer dimensions are commonplace in quantum field theories (QFTs) through dimensional regularization. In particular this affects angular calculations involving dot products. The structure of these rises from the generally accepted axiom that Gaussian integrals can be written as a d-dimensional product of a single dimensional Gaussian integral. This result can be extended in a straightforward manner to involve any above zero-dimensional "surfaces", but there is somewhat clear ambiguity with convergence when considering negative values of dimensions. This obstacle can be answered with proper regularization strategy, which leads to an acceptable analytic continuation. Furthermore, we suggest a method of symmetrizing the angular calculations back to positive dimensional variants by applying the symmetries of Euler gamma functions. Through this method, the region d ∈ [0,1] is recognized to be fundamentally different from either remaining half-axis of dimensionality. By setting this region as the proper limit for the angle generating dimension, we fully establish the rules of iterative use of the generating method and the maximal number integration angles.
Effect of subacute ruminal acidosis on the preference of cows for pellets containing sodium bicarbonate

Lactating cows with low rumen pH were given a choice between two test pellets during a 3-wk experiment. Intake of test pellets containing 4% sodium bicarbonate increased over time, but intake of control test pellets with an equal sodium content through inclusion of sodium chloride remained unaltered. Cows could have preferred bicarbonate pellets in order to attenuate ruminal acidosis, but increased aversion to sodium chloride cannot be excluded. Key words: Acidosis, diet choice, dairy cattle, sodium bicarbonate