Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation

Parallel randomized trial (RT) and real-world (RW) data are becoming increasingly available for treatment evaluation. Given the complementary features of the RT and RW data, we propose a test-based elastic integrative analysis of the RT and RW data for accurate and robust estimation of the heterogeneity of treatment effect (HTE), which lies at the heart of precision medicine. When the RW data are not subject to bias, e.g., due to unmeasured confounding, our approach combines the RT and RW data for optimal estimation by exploiting semiparametric efficiency theory. Utilizing the design advantage of RTs, we construct a built-in test procedure to gauge the reliability of the RW data and decide whether or not to use RW data in an integrative analysis. We characterize the asymptotic distribution of the test-based elastic integrative estimator under local alternatives, which provides a better approximation of the finite-sample behaviors of the test and estimator when the idealistic assumption required for the RW data is weakly violated. We provide a data-adaptive procedure to select the threshold of the test statistic that promises the smallest mean square error of the proposed estimator of the HTE. Lastly, we construct an elastic confidence interval that has a good finite-sample coverage property. We apply the proposed method to characterize who can benefit from adjuvant chemotherapy in patients with stage IB non-small cell lung cancer.
Cross-lingual Multispeaker Text-to-Speech under Limited-Data Scenario

Modeling voices for multiple speakers and multiple languages in one text-to-speech system has been a challenge for a long time. This paper presents an extension on Tacotron2 to achieve bilingual multispeaker speech synthesis when there are limited data for each language. We achieve cross-lingual synthesis, including code-switching cases, between English and Mandarin for monolingual speakers. The two languages share the same phonemic representations for input, while the language attribute and the speaker identity are independently controlled by language tokens and speaker embeddings, respectively. In addition, we investigate the model's performance on the cross-lingual synthesis, with and without a bilingual dataset during training. With the bilingual dataset, not only can the model generate high-fidelity speech for all speakers concerning the language they speak, but also can generate accented, yet fluent and intelligible speech for monolingual speakers regarding non-native language. For example, the Mandarin speaker can speak English fluently. Furthermore, the model trained with bilingual dataset is robust for code-switching text-to-speech, as shown in our results and provided samples. {this https URL}.
SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems

We develop a data-driven model discovery and system identification technique for spatially-dependent boundary value problems (BVPs). Specifically, we leverage the sparse identification of nonlinear dynamics (SINDy) algorithm and group sparse regression techniques with a set of forcing functions and corresponding state variable measurements to yield a parsimonious model of the system. The approach models forced systems governed by linear or nonlinear operators of the form $L[u(x)] = f(x)$ on a prescribed domain $x \in [a, b]$. We demonstrate the approach on a range of example systems, including Sturm-Liouville operators, beam theory (elasticity), and a class of nonlinear BVPs. The generated data-driven model is used to infer both the operator and/or spatially-dependent parameters that describe the heterogenous, physical quantities of the system. Our SINDy-BVP framework will enables the characterization of a broad range of systems, including for instance, the discovery of anisotropic materials with heterogeneous variability.
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.
Astrophysical Distance Scale. II. Application of the JAGB Method: A Nearby Galaxy Sample

We apply the near-infrared J-region asymptotic giant branch (JAGB) method, recently introduced by Madore & Freedman, to measure the distances to 14 nearby galaxies out to 4 Mpc. We use the geometric detached eclipsing binary (DEB) distances to the LMC and SMC as independent zero-point calibrators. We find excellent agreement with previously published distances based on the tip of the red giant branch (TRGB): the JAGB distance determinations (including the LMC and SMC) agree in the mean to within mag, just over 1%, where the TRGB I-band zero-point is MI = −4.05 mag. With further development and testing, the JAGB method has the potential to provide an independent calibration of Type Ia supernovae, especially with the James Webb Space Telescope. The JAGB stars (with MJ = −6.20 mag) can be detected farther than the fainter TRGB stars, allowing greater numbers of calibrating galaxies for the determination of H0. Along with the TRGB and Cepheids, JAGB stars are amenable to theoretical understanding and further refined empirical calibration. A preliminary test shows little dependence, if any, of the JAGB magnitude on metallicity of the parent galaxy. These early results suggest that the JAGB method has considerable promise for providing high-precision distances to galaxies in the local universe that are independent of distances derived from the Leavitt Law and/or the TRGB method, and it has numerous and demonstrable advantages over the possible use of Mira variables.
Edge removal in undirected networks

The edge-removal problem asks whether the removal of a $\lambda$-capacity edge from a given network can decrease the communication rate between source-terminal pairs by more than $\lambda$. We prove that for undirected networks, removing a $\lambda$ capacity edge decreases the rate by $O(\lambda$. Through previously known reductive arguments, here newly applied to undirected networks, our result implies that the zero-error capacity region of an undirected network equals its vanishing-error capacity region. Whether it is possible to prove similar results for directed networks remains an open question.
Detecting and explaining changes in various assets' relationships in financial markets

We study the method for detecting relationship changes in financial markets and providing human-interpretable network visualization to support the decision-making of fund managers dealing with multi-assets. First, we construct co-occurrence networks with each asset as a node and a pair with a strong relationship in price change as an edge at each time step. Second, we calculate Graph-Based Entropy to represent the variety of price changes based on the network. Third, we apply the Differential Network to finance, which is traditionally used in the field of bioinformatics. By the method described above, we can visualize when and what kind of changes are occurring in the financial market, and which assets play a central role in changes in financial markets. Experiments with multi-asset time-series data showed results that were well fit with actual events while maintaining high interpretability.
Latitudinal and seasonal patterns in clutch size of some single‐brooded British birds

Capsule Daylength, rather than latitude, was found to be an important determinant of variation in clutch size. Aims To describe the nature of spatial and temporal variation in clutch size, and explore the ecological correlates of these patterns. Methods We tested the prediction that seasonal declines in clutch size will be greater at higher latitudes. The environmental variables focused on were the influence of daylength, plant productivity, seasonality (i.e. Ashmole's hypothesis) and physiological mechanisms that relate clutch size to ambient temperature. We used data from 1980 to 2003 on spatial variation in clutch size across Britain for single‐brooded species, in which clutch size can be taken as a measure of annual reproductive investment. We included all seven species, from five families, with sufficient data in the British Trust for Ornithology's Nest Record Scheme. Results There are strong seasonal declines in clutch size but little evidence for latitudinal gradients in clutch size or in latitudinal gradients in the rate of seasonal clutch size decline. Of the environmental variables investigated, daylength had the most marked effect on clutch size; this was positive in diurnal species and negative in the one nocturnal species. Conclusions Although this study was confined to a relatively small latitudinal range of 8°, we found marked latitudinal gradients in a number of factors thought to drive spatial patterns in clutch size. Moreover, such variation is of sufficient magnitude to generate spatial patterns in other ecological variables in Britain. There is thus no simple explanation for the lack of a latitudinal gradient in clutch size. The results concerning daylength indicate that the time available for foraging is an important determinant of variation in clutch size.
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data

Abstract Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with replacement due to its high computational efficiency. If the data volume is so large that nonuniform subsampling probabilities cannot be calculated all at once, then subsampling with replacement is infeasible to implement. This article solves this problem using Poisson subsampling. We first derive optimal Poisson subsampling probabilities in the context of quasi-likelihood estimation under the A- and L-optimality criteria. For a practically implementable algorithm with approximated optimal subsampling probabilities, we establish the consistency and asymptotic normality of the resultant estimators. To deal with the situation that the full data are stored in different blocks or at multiple locations, we develop a distributed subsampling framework, in which statistics are computed simultaneously on smaller partitions of the full data. Asymptotic properties of the resultant aggregated estimator are investigated. We illustrate and evaluate the proposed strategies through numerical experiments on simulated and real datasets. Supplementary materials for this article are available online.
HIV‐1 Tat protein induces interleukin‐10 in human peripheral blood monocytes: involvement of protein kinase C‐βII and ‐δ

In HIV‐infected patients, production of interleukin‐10 (IL‐10), a highly immunosuppressive cytokine, is associated with the disease progression toward AIDS. We have previously shown that HIV‐1 Tat induces IL‐10 production by human monocytes via a protein kinase C (PKC) ‐dependent pathway. Here we show that PKC activation by Tat is essential for IL‐10 induction. Among the eight PKC isoforms present in human monocytes, we investigated which isoform(s) plays this crucial role in Tat‐mediated IL‐10 production and show that 1) Tat can activate PKC‐α, PKC‐βll, PKC‐δ, and PKC‐ε, 2) of these four potential candidates, only PKC‐βll, PKC‐δ, and PKC‐ε are activated by the active domain Tat 1–45, which is responsible for IL‐10 production and depleted by long‐term exposure to PMA, which abolishes Tat‐mediated IL‐10 production, 3) whereas selective inhibition of PKC‐α and PKC‐ε by specific antisense oligonucleotides has no effect on Tat‐mediated IL‐10 induction, inhibition of either PKC‐βll or PKC‐δ partially inhibits IL‐10 pro‐duction;and 4) the simultaneous inhibition of PKC‐βll and PKC‐δ totally inhibits Tat‐mediated IL‐10. Altogether, these results suggest that the induction of IL‐10 by Tat is strictly dependent on the PKC‐δ and ‐βII isoforms. —Bennasser, Y. , Bahraoui, E. HIV‐1 Tat protein induces interleukin 10 in human peripheral blood monocytes: involvement of protein kinase C‐βII and ‐δ. FASEB J. 16, 546–554 (2002)
rTop-k: A Statistical Estimation Approach to Distributed SGD

The large communication cost for exchanging gradients between different nodes significantly limits the scalability of distributed training for large-scale learning models. Motivated by this observation, there has been significant recent interest in techniques that reduce the communication cost of distributed Stochastic Gradient Descent (SGD), with gradient sparsification techniques such as top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and random-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> shown to be particularly effective. The same observation has also motivated a separate line of work in distributed statistical estimation theory focusing on the impact of communication constraints on the estimation efficiency of different statistical models. The primary goal of this paper is to connect these two research lines and demonstrate how statistical estimation models and their analysis can lead to new insights in the design of communication-efficient training techniques. We propose a simple statistical estimation model for the stochastic gradients which captures the sparsity and skewness of their distribution. The statistically optimal communication scheme arising from the analysis of this model leads to a new sparsification technique for SGD, which concatenates random-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, considered separately in the prior literature. We show through extensive experiments on both image and language domains with CIFAR-10, ImageNet, and Penn Treebank datasets that the concatenated application of these two sparsification methods consistently and significantly outperforms either method applied alone.
Optimal Growth in Repeated Matching Platforms: Options versus Adoption

We study the design of a decentralized platform in which workers and jobs repeatedly match, and their future engagement with the platform depends on whether they successfully find a match. The platform offers two types of matches to workers: an "adopted match" which entails repeatedly matching with the same job or a one-time match. Due to randomness in match compatibility, adoption seems favorable as it reduces uncertainty in matching. However, high adoption levels reduce the number of available jobs, which in turn can suppress future worker engagement if the remaining workers cannot find a match. To optimally resolve the trade-off between adoption and maintaining available options, we develop a random market model that captures the heterogeneity in workers' future engagement based on match type. Our analysis reveals that the optimal policy for maximizing the matching in a single period is either full or no adoption. For sufficiently thick markets, we show that the optimal single-period policy is also optimal for maximizing the total discounted number of matches. In thinner markets, even though a static policy of full or no adoption can be suboptimal, it achieves a constant-factor approximation where the factor improves with market thickness.
Unsupervised anomaly localization using VAE and beta-VAE

Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. An VAE trained on normal images is expected to only be able to reconstruct normal images, allowing the localization of anomalous pixels in an image via manipulating information within the VAE ELBO loss. The ELBO consists of KL divergence loss (image-wise) and reconstruction loss (pixel-wise). It is natural and straightforward to use the later as the predictor. However, usually local anomaly added to a normal image can deteriorate the whole reconstructed image, causing segmentation using only naive pixel errors not accurate. Energy based projection was proposed to increase the reconstruction accuracy of normal regions/pixels, which achieved the state-of-the-art localization accuracy on simple natural images. Another possible predictors are ELBO and its components gradients with respect to each pixels. Previous work claimed that KL gradient is a robust predictor. In this paper, we argue that the energy based projection in medical imaging is not as useful as on natural images. Moreover, we observe that the robustness of KL gradient predictor totally depends on the setting of the VAE and dataset. We also explored the effect of the weight of KL loss within beta-VAE and predictor ensemble in anomaly localization.
Ultrahigh-precision Rydberg atomic localization using optical vortices

We propose a robust localization of the highly-excited Rydberg atoms, interacting with doughnut-shaped optical vortices. Compared with the earlier standing-wave (SW)-based localization methods, a vortex beam can provide an ultrahigh-precision two-dimensional localization solely in the zero-intensity center, within a confined excitation region down to the nanometer scale. We show that the presence of the Rydberg-Rydberg interaction permits counter-intuitively much stronger confinement towards a high spatial resolution when it is partially compensated by a suitable detuning. In addition, applying an auxiliary SW modulation to the two-photon detuning allows a three-dimensional confinement of Rydberg atoms. In this case, the vortex field provides a transverse confinement while the SW modulation of the two-photon detuning localizes the Rydberg atoms longitudinally. To develop a new subwavelength localization technique, our results pave one-step closer to reduce excitation volumes to the level of a few nanometers, representing a feasible implementation for the future experimental applications.
Medical Image Generation using Generative Adversarial Networks

Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the computer vision community which has gained significant attention from the last few years in identifying the internal structure of multimodal medical imaging data. The adversarial network simultaneously generates realistic medical images and corresponding annotations, which proven to be useful in many cases such as image augmentation, image registration, medical image generation, image reconstruction, and image-to-image translation. These properties bring the attention of the researcher in the field of medical image analysis and we are witness of rapid adaption in many novel and traditional applications. This chapter provides state-of-the-art progress in GANs-based clinical application in medical image generation, and cross-modality synthesis. The various framework of GANs which gained popularity in the interpretation of medical images, such as Deep Convolutional GAN (DCGAN), Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image translation model (UNIT), continue to improve their performance by incorporating additional hybrid architecture, has been discussed. Further, some of the recent applications of these frameworks for image reconstruction, and synthesis, and future research directions in the area have been covered.
Application Management in Fog Computing Environments

The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled cyber-physical systems associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. Fog computing, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed and heterogeneous, and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing.
Strong Post-merger Gravitational Radiation of GW170817-like Events

The post-merger gravitational-wave (GW) radiation of the remnant formed in the binary neutron star (BNS) coalescence has not been directly measured yet. We show in this work that the properties of the BNS involved in GW170817, additionally constrained by PSR J0030+0451, the lower limit on the maximum gravitational mass of nonrotating neutron star (NS), and some nuclear data, are in favor of strong post-merger GW radiation. This conclusion applies to the mergers of Galactic BNS systems as well. Significant post-merger GW radiation is also preferred to improve the consistency between the maximum gravitational mass of the nonrotating NS inferred from GW170817/GRB170817A/AT2017gfo and the latest mass measurements of pulsars. The prominent post-merger gravitational radiation of GW170817-like events are expected to be detectable by advanced LIGO/Virgo detectors in the next decade and then shed valuable light on the properties of the matter in the extremely high density.
An approach to Beethoven's 10th Symphony

Ludwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this paper is to investigate the possibility of extracting patterns on his compositional model from symbolic data and generate what would have been his last symphony, the Tenth. A neural network model has been built based on the Long Short-Therm Memory (LSTM) neural networks. After training the model, the generated music has been analysed by comparing the input data with the results, and establishing differences between the generated outputs based on the training data used to obtain them. The structure of the outputs strongly depends on the symphonies used to train the network.