Miracle-dz/UniFAR-FTUs huggingface dataset:
First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach

Most first-order methods rely on the global Lipschitz continuity of the objective gradient, which fails to hold in many problems. This paper develops a sequential local optimization (SLO) framework for first-order algorithms to optimize problems without Lipschitz gradient. Operating on the assumption that the gradient is locally Lipschitz continuous over any compact set, SLO develops a careful scheme to control the distance between successive iterates. The proposed framework can easily adapt to the existing first-order methods, such as projected gradient descent (PGD), truncated gradient descent (TGD), and a parameter-free variant of Armijo linesearch. We show that SLO requires [Formula: see text] gradient evaluations to find an ϵ-stationary point, where Y is certain compact set with [Formula: see text] radius, and [Formula: see text] denotes the Lipschitz constant of the i-th order derivatives in Y. It is worth noting that our analysis provides the first nonasymptotic convergence rate for the (slight variant of) Armijo linesearch algorithm without globally Lipschitz continuous gradient or convexity. As a generic framework, we also show that SLO can incorporate more complicated subroutines, such as a variant of the accelerated gradient descent (AGD) method that can harness the problem’s second-order smoothness without Hessian computation, which achieves an improved [Formula: see text] complexity. Funding: Dr. J. Zhang is supported by the MOE AcRF [Grant A-0009530-04-00], from Singapore Ministry of Education. Dr. M. Hong is supported by NSF [Grants CIF-1910385 and EPCN-2311007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoo.2021. 0029 .
Anubhuti - An annotated dataset for emotional analysis of Bengali short stories

Thousands of short stories and articles are being written in many different languages all around the world today. Bengali, or Bangla, is the second highest spoken language in India after Hindi and is the national language of the country of Bangladesh. This work reports in detail the creation of Anubhuti -- the first and largest text corpus for analyzing emotions expressed by writers of Bengali short stories. We explain the data collection methods, the manual annotation process and the resulting high inter-annotator agreement of the dataset due to the linguistic expertise of the annotators and the clear methodology of labelling followed. We also address some of the challenges faced in the collection of raw data and annotation process of a low resource language like Bengali. We have verified the performance of our dataset with baseline Machine Learning as well as a Deep Learning model for emotion classification and have found that these standard models have a high accuracy and relevant feature selection on Anubhuti. In addition, we also explain how this dataset can be of interest to linguists and data analysts to study the flow of emotions as expressed by writers of Bengali literature.
Direct observation of symmetry-breaking in a ferroelectric polar metal

Ferroelectric materials contain a switchable spontaneous polarization that persists even in the absence of an external electric field. The coexistence of ferroelectricity and metallicity in a material appears to be illusive, since polarization is ill-defined in metals, where the itinerant electrons are expected to screen the long-range dipole interactions necessary for dipole ordering. The surprising discovery of the polar metal, LiOsO3 has generated interest in searching for new polar metals motivated by the prospects of exotic quantum phenomena such as unconventional pairing mechanisms giving rise to superconductivity, topological spin currents, anisotropic upper critical fields, and Mott multiferroics. Previous studies have suggested that the coordination preferences of the Li atom play a key role in stabilizing the polar metal phase of LiOsO3, but a thorough understanding of how polar order and metallicity can coexist remains elusive. Here, we use XUV-SHG as novel technique to directly probe the broken inversion-symmetry around the Li atom. Our results agree with previous theories that the primary structural distortion that gives rise to the polar metal phase in LiOsO3 is a consequence of a sub-Angstrom Li atom displacement along the polar axis. A remarkable agreement between our experimental results and ab initio calculations provide physical insights for connecting the nonlinear response to unit-cell spatial asymmetries. It is shown that XUV-SHG can selectively probe inversion-breaking symmetry in a bulk material with elemental specificity. Compared to optical SHG methods, XUV-SHG fills a key gap for studying structural asymmetries when the structural distortion is energetically separated from the Fermi surface. Further, these results pave the way for time-resolved probing of symmetry-breaking structural phase transitions on femtosecond timescales with element specificity.
VoiceGrad: Non-Parallel Any-to-Many Voice Conversion with Annealed Langevin Dynamics

In this paper, we propose a non-parallel any-to-many voice conversion (VC) method termed VoiceGrad. Inspired by WaveGrad, a recently introduced novel waveform generation method, VoiceGrad is based upon the concepts of score matching and Langevin dynamics. It uses weighted denoising score matching to train a score approximator, a fully convolutional network with a U-Net structure designed to predict the gradient of the log density of the speech feature sequences of multiple speakers, and performs VC by using annealed Langevin dynamics to iteratively update an input feature sequence towards the nearest stationary point of the target distribution based on the trained score approximator network. Thanks to the nature of this concept, VoiceGrad enables any-to-many VC, a VC scenario in which the speaker of input speech can be arbitrary, and allows for non-parallel training, which requires no parallel utterances or transcriptions.
Improving Context Modeling in Neural Topic Segmentation

Topic segmentation is critical in key NLP tasks and recent works favor highly effective neural supervised approaches. However, current neural solutions are arguably limited in how they model context. In this paper, we enhance a segmenter based on a hierarchical attention BiLSTM network to better model context, by adding a coherence-related auxiliary task and restricted self-attention. Our optimized segmenter outperforms SOTA approaches when trained and tested on three datasets. We also the robustness of our proposed model in domain transfer setting by training a model on a large-scale dataset and testing it on four challenging real-world benchmarks. Furthermore, we apply our proposed strategy to two other languages (German and Chinese), and show its effectiveness in multilingual scenarios.
Linear magnetoresistance with a universal energy scale in a strong-coupling superconductor

The recent discovery of a nonsaturating linear magnetoresistance in several correlated electron systems near a quantum critical point has revealed an interesting interplay between the linear magnetoresistance and the zero-field linear-in-temperature resistivity. These studies suggest a possible role of quantum criticality on the observed linear magnetoresistance. Here, we report our discovery of a nonsaturating, linear magnetoresistance in Mo$_8$Ga$_{41}$, a nearly isotropic strong electron-phonon coupling superconductor with a linear-in-temperature resistivity from the transition temperature to $\sim$55 K. The growth of the resistivity in field is comparable to that in temperature, provided that both quantities are measured in the energy unit. Our datasets are remarkably similar to magnetoresistance data of the optimally doped La$_{2-x}$Sr$_x$CuO$_4$, despite the clearly different crystal and electronic structures, and the apparent absence of quantum critical physics in Mo$_8$Ga$_{41}$. A new empirical scaling formula is developed, which is able to capture the key features of the low-temperature magnetoresistance data of Mo$_8$Ga$_{41}$, as well as the data of La$_{2-x}$Sr$_x$CuO$_4$.
Associated of rs895819 with risk of cervical cancer in Chinese women

 Corresponding authors: Dr. Bubu Wang, Senior TechnicianLaboratory Medicine, Department of Clinical Laboratory, The Third Affiliated Hospital, Wenzhou Medical University, Hangzhou, China. Email: wangbubu.835071889@qq.com (B. Wang). Dr. Lingyan Zheng, Attending doctorClinical Onlology, Department of Oncology, The Third Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. E-mail: Lingyan_zheng@zju.edu. cn (L. Zheng).
The Lyman Alpha Spectral Database (LASD)

Lymanα (Lyα) emission from star-forming galaxies is an important tool to study a large range of astrophysical questions: it has the potential to carry information about the source galaxy, its nearby circumgalactic medium, and also the surrounding intergalactic medium. Substantial observational and theoretical work has therefore focused on understanding the details of this emission line. These efforts have been hampered, however, by an absence of spectroscopic reference samples that can be used both as comparisons for observational studies and as critical tests for theoretical work. For this reason, we have compiled a large sample of Lyα spectra, at both low and high redshift, and created a publicly available online database, at lasd.lyman-alpha.com. The Lyman Alpha Spectral Database (LASD) hosts these spectra, as well as a large set of spectral and kinematic quantities that have been homogeneously measured for the entire sample. As part of this we have developed an automated redshift determination algorithm which we show is accurate to within less than ±180 kms−1 on average, across many different Lyα profiles. The measurements can conveniently be viewed online and downloaded in tabular form. The LASD has the capacity for users to easily upload their own Lyα spectra, and all the same spectral measurements will be made, reported, and ingested into the database. We actively invite the community to do so, and the LASD is intended to be a long-term community resource. In this paper we present the design of the database as well as descriptions of the underlying algorithms and the initial Lyα emitter samples that are in the database.
Improving App Quality Despite Flawed Mobile Analytics

Analytics can help improve the quality of software; the improvements are affected by the fidelity of the analytics. The impact of poor fidelity may vary depending on the type of data being collected, for example, for crashes low fidelity may be sufficient.The mobile ecosystem includes a platform where apps run and an app store that intermediates between developers and users. Google’s Android ecosystem provides all the developers with analytics about various qualities of their app through a service called Android Vitals that automatically collects data on how their app is performing.My research found ways to improve app quality through using mobile analytics, including Android Vitals. It also found fidelity flaws in several analytics tools provided by Google. They confirmed and validated some flaws and chose not to discuss others.
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks

Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.
Bias and Debias in Recommender System: A Survey and Future Directions

While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias. Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, and so on. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and perform debiasing when necessary. When reviewing the papers that consider biases in RS, we find that, to our surprise, the studies are rather fragmented and lack a systematic organization. The terminology “bias” is widely used in the literature, but its definition is usually vague and even inconsistent across papers. This motivates us to provide a systematic survey of existing work on RS biases. In this paper, we first summarize seven types of biases in recommendation, along with their definitions and characteristics. We then provide a taxonomy to position and organize the existing work on recommendation debiasing. Finally, we identify some open challenges and envision some future directions, with the hope of inspiring more research work on this important yet less investigated topic. The summary of debiasing methods reviewed in this survey can be found at https://github.com/jiawei-chen/RecDebiasing.
Understanding the determinants of happiness through Gallup World Poll

Background: The idea of happiness is as old as civilization, but breakthrough is achieved only in 20th century. Happiness can be broadly segmented into biological and behavioural component. The sufferings from illnesses hamper happiness. Happiness correlates negatively with morbidity, mortality, stress and anxiety in contrast to a positive correlation with motivation, healthy behaviours and longevity. In this article, an attempt has been made to understand the relationship between happiness and its important contributory factors. Material and Methods: The current study used data from the Gallup World Poll available under license CC0. Data analysis was performed using R studio version 1.0. 136. Initially, descriptive analysis in the form of mean (standard deviation), violin plot, correlation matrix, and scatter plots were reported. Subsequently, robust regression estimates along with bootstrap standard errors and confidence intervals were used to report inferential statistics. Results: Norway, with a happiness score of 7.537 ranked first followed by Denmark with a score of 7.522. Burundi with a score of 2.905 is at the bottom of ranking for happiness. Freedom (CI; 0.95-2.22) and Family (CI; 0.92 - 1.57) are the strongest predictors of happiness. The trust variable does not have a significant (CI; -0.27 – 1.94) relationship with happiness. Conclusions: The values and norms in society are changing at a fast pace. Therefore, the measures of happiness require consistent and innovative approaches to measure it.
Autonomous detection of malicious events using machine learning models in drone networks

Drone systems, the so-called Unmanned Autonomous Vehicles (UAVs), have been widely employed in military and civilian sectors. Drone systems have been used for cyber warfare, warfighting and surveillance purposes of modern military and civilian applications. However, they have increasingly suffered from sophisticated malicious activities that exploit their vulnerabilities through network communications. As drones comprise a complex infrastructure as piloted aircraft but without operators, they still need a reliable security control to assert their safe operations. This paper proposes an autonomous intrusion detection scheme for discovering advanced and sophisticated cyberattacks that exploit drone networks. A testbed was configured to launch malicious events against a drone network for collecting legitimate and malicious observations and evaluate the performances of machine learning in real-time. Machine learning algorithms, including decision tree, k-nearest neighbors, naive Bayes, support vector machine and deep learning multi-layer perceptron, were trained and evaluated using the data collections, with promising results in terms of detection accuracy, false alarm rates, and processing times.
Identification and characterization of an alternative canalicular bile acid transporter

In vertebrates, bile flow is essential for movement of water and solutes across the liver canalicular membrane. The formation of bile is contributed by the active transport of bile acids and other organic solutes (Coleman 1987; Nathanson and Boyer 1991; Erlinger 1996). In recent years, the molecular motor of canalicular bile acid secretion has been identified to be a member of the ATP Binding Cassette transporter superfamily, known as Sister of p-glycoprotein (Spgp) or Bile Salt Efflux Pump (Bsep) (Childs et al. 1995; Gerloff et al. 1998). In humans, mutations in spgp cause a severe liver disease known as type 2 intrahepatic cholestasis (PFIC2), which is characterized by low bile acid output (Strautnieks et al. 1998; Jansen et al. 1999). Spgp' mice produce a cholestatic phenotype that is less severe than human PFIC2 (Wang et al. 2001). The work in this thesis investigated the molecular mechanism(s) of bile acid secretion in spgp''' mice. Hepatic responses of bile flow and bile acid secretion were significantly stimulated by cholic acid (CA) feeding in mutant mice. In addition, the capacity of total bile acid secretion was comparable to wildtype mice, compatible with the hypothesis that additional molecular mechanisms exist for bile acid secretion. To investigate whether another ATP-dependent bile acid transport may account for the bile acid clearance in spgp''' mice, mRNA and protein expression data were studied. The expression of Mdrla associated with resistance to multiple anticancer drugs, was dramatically increased in spgp''' mice. Kinetic studies showed ATP-dependent bile acid transport in canalicular membrane vesicles isolated from control mice but not in those from spgp'' mice. To reconcile this difference between in vivo data and in vitro canalicular membrane transport measurement, membrane vesicles containing highly overexpressed hamster Pgp (a homolog of mouse mdrla) was used to demonstrate ATP-dependent bile acid transport. The kinetic parameters of Pgp-mediated ATP-dependent taurocholate transport were compatible with a low-affinity transporter. Taken together, these studies have provided evidence that Mdrla is capable of transporting bile acids, which has not been recognized previously.
VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling

In this paper, we tackle the task of definition modeling, where the goal is to learn to generate definitions of words and phrases. Existing approaches for this task are discriminative, combining distributional and lexical semantics in an implicit rather than direct way. To tackle this issue we propose a generative model for the task, introducing a continuous latent variable to explicitly model the underlying relationship between a phrase used within a context and its definition. We rely on variational inference for estimation and leverage contextualized word embeddings for improved performance. Our approach is evaluated on four existing challenging benchmarks with the addition of two new datasets, "Cambridge" and the first non-English corpus "Robert", which we release to complement our empirical study. Our Variational Contextual Definition Modeler (VCDM) achieves state-of-the-art performance in terms of automatic and human evaluation metrics, demonstrating the effectiveness of our approach.
How unbiased statistical methods lead to biased scientific discoveries: A case study of the Efron–Petrosian statistic applied to the luminosity-redshift evolution of gamma-ray bursts

Statistical methods are frequently built upon assumptions that limit their applicability to certain problems and conditions. Failure to recognize these limitations can lead to conclusions that may be inaccurate or biased. An example of such methods is the non-parametric Efron-Petrosian test statistic used in the studies of truncated data. We argue and show how the inappropriate use of this statistical method can lead to biased conclusions when the assumptions under which the method is valid do not hold. We do so by reinvestigating the evidence recently provided by multiple independent reports on the evolution of the luminosity/energetics distribution of cosmological Long-duration Gamma-Ray Bursts (LGRBs) with redshift. We show that the effects of detection threshold has been likely significantly underestimated in the majority of previous studies. This underestimation of detection threshold leads to severely-incomplete LGRB samples that exhibit strong apparent luminosity-redshift or energetics-redshift correlations. We further confirm our findings by performing extensive Monte Carlo simulations of the cosmic rates and the luminosity/energy distributions of LGRBs and their detection process.
Iterative learning of graph connectivity from partially-observed cascade samples

Graph learning is an inference problem of estimating connectivity of a graph from a collection of epidemic cascades, with many useful applications in the areas of online/offline social networks, p2p networks, computer security, and epidemiology. We consider a practical scenario when the information of cascade samples are partially observed in the independent cascade (IC) model. For the graph learning problem, we propose an efficient algorithm that solves a localized version of computationally-intractable maximum likelihood estimation through approximations in both temporal and spatial aspects. Our algorithm iterates the operations of recovering missing time logs and inferring graph connectivity, and thereby progressively improves the inference quality. We study the sample complexity, which is the number of required cascade samples to meet a given inference quality, and show that it is asymptotically close to a lower bound, thus near-order-optimal in terms of the number of nodes. We evaluate the performance of our algorithm using five real-world social networks, whose size ranges from 20 to 900, and demonstrate that our algorithm performs better than other competing algorithms in terms of accuracy while maintaining fast running time.