Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmology as a holographic wormhole

: In this paper, we suggest a framework for cosmology based on gravitational eﬀective ﬁeld theories with a negative fundamental cosmological constant, which may ex-hibit accelerated expansion due to the positive potential energy of rolling scalar ﬁelds. The framework postulates an exact time-reversal symmetry of the quantum state (with a time-symmetric big bang / big crunch background cosmology) and an analyticity property that relates cosmological observables to observables in a Euclidean gravitational theory deﬁned with a pair of asymptotically Anti-de Sitter (AdS) planar boundaries. We propose a microscopic deﬁnition for this Euclidean theory using holography, so the model is UV complete. While it is not yet clear whether the framework can give realistic predictions, it has the potential to resolve various naturalness puzzles without the need for inﬂation. This is a shorter version of [1] (arXiv:2203.11220), emphasizing the eﬀective ﬁeld theory point of view.
Variable selection in high-dimensional logistic regression models using a whitening approach

In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research. However, omics data usually has a limited sample size but high feature dimensions, and it is assumed that only a few features (biomarkers) are active, i.e. informative to discriminate between different categories (cancer subtypes, responder/non-responder to treatment, for example). Identifying active biomarkers for classification has therefore become fundamental for omics data analysis. Focusing on binary classification, we propose an innovative feature selection method aiming at dealing with the high correlations between the biomarkers. Various research has shown the notorious influence of correlated biomarkers and the difficulty of accurately identifying active ones. Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features. The performance of WLogit is assessed using synthetic data in several scenarios and compared with other approaches. The results suggest that WLogit can identify almost all active biomarkers even in the cases where the biomarkers are highly correlated, while the other methods fail, which consequently leads to higher classification accuracy. The performance is also evaluated on the classification of two Lymphoma subtypes, and the obtained classifier also outperformed other methods. Our method is implemented in the \texttt{WLogit} R package available from the Comprehensive R Archive Network (CRAN).
Exploiting Inherent Elasticity of Serverless in Irregular Algorithms

Serverless computing, in particular the Function-as-a-Service (FaaS) execution model, has recently shown to be effective for running large-scale computations. However, little attention has been paid to highly-parallel applications with unbalanced and irregular workloads. Typically, these workloads have been kept out of the cloud due to the impossibility of anticipating their computing resources ahead of time, frequently leading to severe resource over- and underprovisioning situations. Our main insight in this article is, however, that the elasticity and ease of management of serverless computing technology can be a key enabler for effectively running these problematic workloads for the first time in the cloud. More concretely, we demonstrate that with a simple serverless executor pool abstraction one can achieve a better cost-performance trade-off than a Spark cluster of static size built upon large EC2 virtual machines. To support this conclusion, we evaluate three irregular algorithms: Unbalanced Tree Search (UTS), Mandelbrot Set using the Mariani-Silver algorithm and Betweenness Centrality (BC) on a random graph. For instance, our serverless implementation of UTS is able to outperform Spark by up to 55% with the same cost. We also show that a serverless environment can outperform a large EC2 in the BC algorithm by a 10% using the same amount of virtual CPUs. This provides the first concrete evidence that highly-parallel, irregular workloads can be efficiently executed using purely stateless functions with almost zero burden on users i.e., no need for users to understand non-obvious system-level parameters and optimizations. Furthermore, we show that UTS can benefit from the FaaS pay-as-you-go billing model, which makes it worth for the first time to enable certain application-level optimizations that can lead to significant improvements (e.g. of 41%) with negligible increase in cost.
A perspective on Attitude Control Issues and Techniques

This paper reviews the attitude control problems for rigid-body systems, starting from the attitude representation for rigid body kinematics. Highly redundant rotation matrix defines the attitude orientation globally and uniquely by 9 parameters, which is the most fundamental one, without any singularities; minimum 3-parameter Euler angles or (modified) Rodrigues parameters define the attitude orientation neither globally nor uniquely, but the former exhibits kinematical singularity and Gimbal lock, while the latter two exhibit geometrical singularity; once-redundant axis-angle or unit quaternion globally define the attitude rotation but not uniquely using 4 parameters, but the former is not appropriate to define very small or very large rotations, while the latter shows unwinding phenomenon despite of the reduced computation burden. In addition, we explore the relationships among those attitude representations, including the connections among Gimbal lock, unwinding phenomenon and a nowhere dense set of zero Lebesgue measure. Based on attitude representations, we analyze different attitude control laws, almost global control and global attitude control, nominal and general robustness, as well as the technique tools.
Designs, Motion Mechanism, Motion Coordination, and Communication of Bionic Robot Fishes: A Survey

In the last few years, there have been many developments and significant accomplishments in the research of bionic robot fishes. However, in terms of swimming performance, existing bionic robot fishes lag far behind fish, prompting researchers to constantly innovative designs of various bionic robot fishes. In this paper, the latest designs of robot fishes are presented in detail, distinguished by the propulsion mode. New robot fishes are mainly includes soft robot fishes and rigid-soft coupled robot fishes. The latest progress in the study of the swimming mechanism is analyzed on the basis of summarizing the main swimming theories of fish. The current state-of-the-art research in the new field of motion coordination and communication of multiple robot fishes is summarized. The general research trend in robot fishes is to utilize a more efficient and robust methods to best mimic real fish while exhibiting superior swimming performance. The current challenges and potential future research directions are discussed. Various methods are needed to narrow the gap in swimming performance between robot fishes and fish. This paper is a first step to bring together roboticists and marine biologists interested in learning the state-of-the-art research on bionic robot fishes.
Randomized K-FACs: Speeding up K-FAC with Randomized Numerical Linear Algebra

K-FAC is a successful tractable implementation of Natural Gradient for Deep Learning, which nevertheless suffers from the requirement to compute the inverse of the Kronecker factors (through an eigen-decomposition). This can be very time-consuming (or even prohibitive) when these factors are large. In this paper, we theoretically show that, owing to the exponential-average construction paradigm of the Kronecker factors that is typically used, their eigen-spectrum must decay. We show numerically that in practice this decay is very rapid, leading to the idea that we could save substantial computation by only focusing on the first few eigen-modes when inverting the Kronecker-factors. Importantly, the spectrum decay happens over a constant number of modes irrespectively of the layer width. This allows us to reduce the time complexity of K-FAC from cubic to quadratic in layer width, partially closing the gap w.r. t. SENG (another practical Natural Gradient implementation for Deep learning which scales linearly in width). Randomized Numerical Linear Algebra provides us with the necessary tools to do so. Numerical results show we obtain $\approx2.5\times$ reduction in per-epoch time and $\approx3.3\times$ reduction in time to target accuracy. We compare our proposed K-FAC sped-up versions SENG, and observe that for CIFAR10 classification with VGG16_bn we perform on par with it.
On the Learning and Learnablity of Quasimetrics

Our world is full of asymmetries. Gravity and wind can make reaching a place easier than coming back. Social artifacts such as genealogy charts and citation graphs are inherently directed. In reinforcement learning and control, optimal goal-reaching strategies are rarely reversible (symmetrical). Distance functions supported on these asymmetrical structures are called quasimetrics. Despite their common appearance, little research has been done on the learning of quasimetrics. Our theoretical analysis reveals that a common class of learning algorithms, including unconstrained multilayer perceptrons (MLPs), provably fails to learn a quasimetric consistent with training data. In contrast, our proposed Poisson Quasimetric Embedding (PQE) is the first quasimetric learning formulation that both is learnable with gradient-based optimization and enjoys strong performance guarantees. Experiments on random graphs, social graphs, and offline Q-learning demonstrate its effectiveness over many common baselines. Project Page: ssnl.github. io/quasimetric. Code: github.com/SsnL/poisson_quasimetric_embedding.
Runtime analysis of competitive co-evolutionary algorithms for maximin optimisation of a bilinear function

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliably. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. An example application of the framework shows a scenario where a simple coevolutionary algorithm obtains a solution in polynomial expected time. Finally, we describe settings where the co-evolutionary algorithm needs exponential time with overwhelmingly high probability to obtain a solution.
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS

This paper introduces R-MelNet, a two-part autoregressive architecture with a frontend based on the first tier of MelNet and a backend WaveRNN-style audio decoder for neural text-to-speech synthesis. Taking as input a mixed sequence of characters and phonemes, with an optional audio priming sequence, this model produces low-resolution mel-spectral features which are interpolated and used by a WaveRNN decoder to produce an audio waveform. Coupled with half precision training, R-MelNet uses under 11 gigabytes of GPU memory on a single commodity GPU (NVIDIA 2080Ti). We detail a number of critical implementation details for stable half precision training, including an approximate, numerically stable mixture of logistics attention. Using a stochastic, multi-sample per step inference scheme, the resulting model generates highly varied audio, while enabling text and audio based controls to modify output waveforms. Qualitative and quantitative evaluations of an R-MelNet system trained on a single speaker TTS dataset demonstrate the effectiveness of our approach.
Interpretable Melody Generation from Lyrics with Discrete-Valued Adversarial Training

Generating melody from lyrics is an interesting yet challenging task in the area of artificial intelligence and music. However, the difficulty of keeping the consistency between input lyrics and generated melody limits the generation quality of previous works. In our proposal, we demonstrate our proposed interpretable lyrics-to-melody generation system which can interact with users to understand the generation process and recreate the desired songs. To improve the reliability of melody generation that matches lyrics, mutual information is exploited to strengthen the consistency between lyrics and generated melodies. Gumbel-Softmax is exploited to solve the non-differentiability problem of generating discrete music attributes by Generative Adversarial Networks (GANs). Moreover, the predicted probabilities output by the generator is utilized to recommend music attributes. Interacting with our lyrics-to-melody generation system, users can listen to the generated AI song as well as recreate a new song by selecting from recommended music attributes.
Efficient Entity Candidate Generation for Low-Resource Languages

Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Nevertheless, it has often been overlooked in the monolingual English entity linking literature, as naïve approaches obtain very good performance. Unfortunately, the existing approaches for English cannot be successfully transferred to poorly resourced languages. This paper constitutes an in-depth analysis of the candidate generation problem in the context of cross-lingual entity linking with a focus on low-resource languages. Among other contributions, we point out limitations in the evaluation conducted in previous works. We introduce a characterization of queries into types based on their difficulty, which improves the interpretability of the performance of different methods. We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches. A thorough empirical analysis on 9 real-world datasets under 2 evaluation settings shows that our simple solution outperforms the state-of-the-art approach in terms of both quality and efficiency for almost all datasets and query types.
Constraining white dwarf tides from gravitational waves with LISA

Double white dwarf (DWD) systems are unique among gravitational-wave sources because their evolution is governed not just by general relativity, but also by mass transfer and tides. While the black hole and neutron star binaries observed with ground-based gravitational-wave detectors are driven to inspiral due to the emission of gravitational radiation—manifesting as a “chirp”-like gravitational-wave signal—the astrophysical processes at work in DWDs can cause the inspiral to stall and even reverse into an outspiral. The dynamics of the DWD outspiral thus encode information about tides, which tell us about the behaviour of electron-degenerate matter. We carry out a population study to determine the eﬀect of the strength of tides on the distributions of the DWD binary parameters that the Laser Interferometer Space Antenna (LISA) will be able to constrain. We ﬁnd that the strength of tidal coupling leaves a unique signature in the distribution of gravitational-wave frequencies and frequency derivatives for detectably mass transferring DWD systems. By measuring the population properties of DWDs, LISA will be able to probe the behavior of electron-degenerate matter.
PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis

With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models, then sampled the images in the dataset according to normal distribution, and fed the sampling results into the modified PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method.
Insurance pricing with hierarchically structured data an illustration with a workers' compensation insurance portfolio

Actuaries use predictive modeling techniques to assess the loss cost on a contract as a function of observable risk characteristics. State-of-the-art statistical and machine learning methods are not well equipped to handle hierarchically structured risk factors with a large number of levels. In this paper, we demonstrate the data-driven construction of an insurance pricing model when hierarchically structured risk factors, contract-specific as well as externally collected risk factors are available. We examine the pricing of a workers' compensation insurance product with a hierarchical credibility model [Jewell, W. S. (1975). The use of collateral data in credibility theory: A hierarchical model. Laxenburg: IIASA], Ohlsson's combination of a generalized linear and a hierarchical credibility model [Ohlsson, E. (2008). Combining generalized linear models and credibility models in practice. Scandinavian Actuarial Journal 2008(4), 301–314] and mixed models. We compare the predictive performance of these models and evaluate the effect of the distributional assumption on the target variable by comparing linear mixed models with Tweedie generalized linear mixed models. For our case-study the Tweedie distribution is well suited to model and predict the loss cost on a contract. Moreover, incorporating contract-specific risk factors in the model improves the predictive performance and the risk differentiation in our workers' compensation insurance portfolio.
A GPU-based Imager with Polarised Primary-beam Correction

The next generation of radio telescopes will strive for unprecedented dynamic range across wide fields of view, and direction-dependent gains such as the gain from the primary-beam pattern, or leakage of one Stokes product into another, must be removed from the cleaned images if dynamic range is to reach its full potential. Unfortunately, such processing is extremely computationally intensive, and is made even more challenging by the very large volumes of data that these instruments will generate. Here we describe a new GPU-based imager, aimed primarily at use with the ASKAP telescope, that is capable of generating cleaned, full-polarisation images that include widefield, primary-beam, and polarisation leakage corrections.
Review of recent advances in flexible oxide semiconductor thin-film transistors

ABSTRACT This paper describes the recent advances in flexible oxide thin-film transistors (TFTs), one of the rapidly emerging technologies for the next-generation display applications. First, the paper focuses on the effect of the buffer layer over the plastic substrate, which significantly influences the electrical performance and stability of oxide TFTs. Then oxide semiconductor TFTs fabricated through atomic layer deposition among the various oxide semiconductor fabrication methods were reviewed due to their potential as high-performance flexible TFTs. Finally, the mechanical fatigue behaviors of the TFTs were investigated, including the various mechanical factors, such as the bending radius, cycles, and stress directions. Structural solutions for the TFT were also introduced, such as TFT design modification and the use of the neutral plane concept, to improve the mechanical durability.
Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.
A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback

We present a modified tuning of the algorithm of Zimmert and Seldin [2020] for adversarial multiarmed bandits with delayed feedback, which in addition to the minimax optimal adversarial regret guarantee shown by Zimmert and Seldin simultaneously achieves a near-optimal regret guarantee in the stochastic setting with fixed delays. Specifically, the adversarial regret guarantee is $\mathcal{O}(\sqrt{TK} + \sqrt{dT\log K})$, where $T$ is the time horizon, $K$ is the number of arms, and $d$ is the fixed delay, whereas the stochastic regret guarantee is $\mathcal{O}\left(\sum_{i \neq i^*}(\frac{1}{\Delta_i} \log(T) + \frac{d}{\Delta_{i}\log K}) + d K^{1/3}\log K\right)$, where $\Delta_i$ are the suboptimality gaps. We also present an extension of the algorithm to the case of arbitrary delays, which is based on an oracle knowledge of the maximal delay $d_{max}$ and achieves $\mathcal{O}(\sqrt{TK} + \sqrt{D\log K} + d_{max}K^{1/3} \log K)$ regret in the adversarial regime, where $D$ is the total delay, and $\mathcal{O}\left(\sum_{i \neq i^*}(\frac{1}{\Delta_i} \log(T) + \frac{\sigma_{max}}{\Delta_{i}\log K}) + d_{max}K^{1/3}\log K\right)$ regret in the stochastic regime, where $\sigma_{max}$ is the maximal number of outstanding observations. Finally, we present a lower bound that matches regret upper bound achieved by the skipping technique of Zimmert and Seldin [2020] in the adversarial setting.