Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Aharanov-Bohm Topological Phase due to the Generation of Electricity through Permanent Bound and Free Charge Polarization

Michael E. Tobar, Raymond Y. Chiao, and Maxim Goryachev ARC Centre of Excellence for Engineered Quantum Systems and ARC Centre of Excellence for Dark Matter Particle Physics, Department of Physics, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. School of Natural Sciences, University of California Merced, 5200 N. Lake Rd, Merced, CA 95343, USA (Dated: January 5, 2021)
What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure

In recent times, BERT based transformer models have become an inseparable part of the 'tech stack' of text processing models. Similar progress is being observed in the speech domain with a multitude of models observing state-of-the-art results by using audio transformer models to encode speech. This begs the question of what are these audio transformer models learning. Moreover, although the standard methodology is to choose the last layer embedding for any downstream task, but is it the optimal choice? We try to answer these questions for the two recent audio transformer models, Mockingjay and wave2vec2.0. We compare them on a comprehensive set of language delivery and structure features including audio, fluency and pronunciation features. Additionally, we probe the audio models' understanding of textual surface, syntax, and semantic features and compare them to BERT. We do this over exhaustive settings for native, non-native, synthetic, read and spontaneous speech datasets
On-the-Fly Attention Modularization for Neural Generation

Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufﬁcient learning of inductive biases by the attention mechanism. Our ﬁndings motivate on-the-ﬂy attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining ﬂuency and coherence.
Understanding Few-Shot Commonsense Knowledge Models

Providing natural language processing systems with commonsense knowledge is a critical challenge for achieving language understanding. Recently, commonsense knowledge models (Bosselut et al., 2019) have emerged as a suitable approach for hypothe-sizing situation-relevant commonsense knowledge on-demand in natural language applications. However, these systems are limited by the ﬁxed set of relations captured by schemas of the knowledge bases on which they’re trained. To address this limitation, we investigate training commonsense knowledge models in a few-shot setting with limited tuples per common-sense relation in the graph. We perform ﬁve separate studies on different dimensions of few-shot commonsense knowledge learning, providing a roadmap on best practices for training these systems efﬁciently. Importantly, we ﬁnd that human quality ratings for knowledge produced from a few-shot trained system can achieve performance within 6% of knowledge produced from fully supervised systems. This few-shot performance enables coverage of a wide breadth of relations in future common-sense systems.
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue

A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations. Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and language inputs. Existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation. These benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning. To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Video-grounded Dialogue. The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video. Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships. We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations. In total, DVD is built from 11k CATER synthetic videos and contains 10 instances of 10-round dialogues for each video, resulting in more than 100k dialogues and 1M question-answer pairs. Our code and dataset are publicly available.
Correction terms for the height of weighted recursive trees

Weighted recursive trees are built by adding successively vertices with predetermined weights to a tree: each new vertex is attached to a parent chosen randomly proportionally to its weight. Under some assumptions on the sequence of weights, the first order for the height of such trees has been recently established by one of the authors. In this paper, we obtain the second and third orders in the asymptotic expansion of the height of weighted recursive trees, under similar assumptions. Our methods are inspired from those used to prove similar results for branching random walks. Our results also apply to a related model of growing trees, called the preferential attachment tree with additive fitnesses.
Thermal resistance at a twist boundary and a semicoherent heterointerface

Traditional models of interfacial phonon scattering, including the acoustic mismatch model (AMM) and diffuse mismatch model (DMM), take into account the bulk properties of the material surrounding the interface, but not the atomic structure and properties of the interface itself. Here, we derive a theoretical formalism for the phonon scattering at a dislocation grid, or two interpenetrating orthogonal arrays of dislocations, as this is the most stable structure of both the symmetric twist boundary and semicoherent heterointerface. With this approach, we are able to separately examine the contribution to thermal resistance due to the step function change in acoustic properties and due to interfacial dislocation strain fields, which induces diffractive scattering. Both low-angle Si-Si twist boundaries and the Si-Ge heterointerfaces are considered here and compared to previous experimental and simulation results. This work indicates that scattering from misfit dislocation strain fields doubles the thermal boundary resistance of Si-Ge heterointerfaces compared to scattering due to acoustic mismatch alone. Scattering from grain boundary dislocation strain fields is predicted to dominate the thermal boundary resistance of Si-Si twist boundaries. This physical treatment can guide the thermal design of devices by quantifying the relative importance of interfacial strain fields, which can be engineered via fabrication and processing methods, versus acoustic mismatch, which is fixed for a given interface. Additionally, this approach captures experimental and simulation trends such as the dependence of thermal boundary resistance on the grain boundary angle and interfacial strain energy.
Statistical inference on the Hilbert sphere with application to random densities

The infinite-dimensional Hilbert sphere $S^\infty$ has been widely employed to model density functions and shapes, extending the finite-dimensional counterpart. We consider the Fr\'echet mean as an intrinsic summary of the central tendency of data lying on $S^\infty$. To break a path for sound statistical inference, we derive properties of the Fr\'echet mean on $S^\infty$ by establishing its existence and uniqueness as well as a root-$n$ central limit theorem (CLT) for the sample version, overcoming obstructions from infinite-dimensionality and lack of compactness on $S^\infty$. Intrinsic CLTs for the estimated tangent vectors and covariance operator are also obtained. Asymptotic and bootstrap hypothesis tests for the Fr\'echet mean based on projection and norm are then proposed and are shown to be consistent. The proposed two-sample tests are applied to make inference for daily taxi demand patterns over Manhattan modeled as densities, of which the square roots are analyzed on the Hilbert sphere. Numerical properties of the proposed hypothesis tests which utilize the spherical geometry are studied in the real data application and simulations, where we demonstrate that the tests based on the intrinsic geometry compare favorably to those based on an extrinsic or flat geometry.
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, there has been a surge in the amount of research literature on OpenQA, particularly on techniques that integrate with neural Machine Reading Comprehension (MRC). While these research works have advanced performance to new heights on benchmark datasets, they have been rarely covered in existing surveys on QA systems. In this work, we review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. Specifically, we begin with revisiting the origin and development of OpenQA systems. We then introduce modern OpenQA architecture named"Retriever-Reader"and analyze the various systems that follow this architecture as well as the specific techniques adopted in each of the components. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used. We hope our work would enable researchers to be informed of the recent advancement and also the open challenges in OpenQA research, so as to stimulate further progress in this field.
Tweeting for the Cause: Network analysis of UK petition sharing

Online government petitions represent a new data-rich mode of political participation. This work examines the thus far understudied dynamics of sharing petitions on social media in order to garner signatures and, ultimately, a government response. Using 20 months of Twitter data comprising over 1 million tweets linking to a petition, we perform analyses of networks constructed of petitions and supporters on Twitter, revealing implicit social dynamics therein. We find that Twitter users do not exclusively share petitions on one issue nor do they share exclusively popular petitions. Among the over 240,000 Twitter users, we find latent support groups, with the most central users primarily being politically active ‘average’ individuals. Twitter as a platform for sharing government petitions, thus, appears to hold potential to foster the creation of and coordination among a new form of latent support interest groups online. I. Introduction: Internet-based platforms facilitate citizen participation in government, and government-sponsored petition websites—available in a number of democracies, including the United Kingdom and the United States—are an increasingly popular example. On such platforms, citizens undertake a ‘tiny act’ of political participation (Margetts et al., 2015), expressing their policy preferences to government and lobbying for change. Petitions have a rich history in the UK, and online petitions, in some ways, present a continuation of that tradition (Bochel 2012). Although petitions are not new, online petition platforms—together with social media—offer a means of circulating and tallying signatures on a scale never seen 1 Acknowledgment The data collection and preliminary analysis for this paper was carried out as part of the ESRC professorial fellowship, ‘The Internet, Political Science and Public Policy: Reexamining Collective Action, Governance and Citizen-Government Interactions in the Digital Era' RES-051-27-0331 (2011-2015), and we thank the ESRC for their support. We would also like to thank Alex Paseltiner, Reid Pryzant, Matt McNaughton, Matt Baya, and Williams College OIT for computational assistance during this project.
Multi–Grid Back–Projection Networks

Multi–grid back–projection (MGBP) is a fully–convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of multi–grid partial differential equation (PDE) solvers this multiscale architecture scales computational complexity efficiently with increasing output resolutions. The basic processing block is inspired in the iterative back–projection (IBP) algorithm and constitutes a type of cross–scale residual block with feedback from low resolution references. The architecture performs in par with state–of–the-arts alternatives for regression targets that aim to recover an exact copy of a high resolution image or video from which only a downscale image is known. A perceptual quality target aims to create more realistic outputs by introducing artificial changes that can be different from a high resolution original content as long as they are consistent with the low resolution input. For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output. The noise input controls the amount of innovation that the network uses to create artificial realistic details. The effectiveness of this strategy is shown in benchmarks and it is explained as a particular strategy to traverse the perception–distortion plane.
Sidewise control of 1-d waves

We analyze the sidewise controllability for the variable coefficients one-dimensional wave equation. The control is acting on one extreme of the string with the aim that the solution tracks a given path or profile at the other free end. This sidewise profile control problem is also often referred to as nodal profile or tracking control. The problem is reformulated as a dual observability property for the corresponding adjoint system, which is proved by means of sidewise energy propagation arguments in a sufficiently large time, in the class of BV -coefficients. We also present a number of open problems and perspectives for further research.
CIZSL++: Creativity Inspired Generative Zero-Shot Learning

Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of ZSL, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art. First, we propose CIZSL-v1 as a creativity inspired model for generative ZSL. We relate ZSL to human creativity by observing that ZSL is about recognizing the unseen, and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. Second, CIZSL-v2 is proposed as an improved version of CIZSL-v1 for generative zero-shot learning. CIZSL-v2 consists of an investigation of additional inductive losses for unseen classes along with a semantic guided discriminator. Empirically, we show consistently that CIZSL losses can improve generative ZSL models on the challenging task of generalized ZSL from a noisy text on CUB and NABirds datasets. We also show the advantage of our approach to Attribute-based ZSL on AwA2, aPY, and SUN datasets. We also show that CIZSL-v2 has improved performance compared to CIZSL-v1.
Text Searching Allowing for Non-Overlapping Adjacent Unbalanced Translocations

In this paper we investigate the \emph{approximate string matching problem} when the allowed edit operations are \emph{non-overlapping unbalanced translocations of adjacent factors}. Such kind of edit operations take place when two adjacent sub-strings of the text swap, resulting in a modified string. The two involved substrings are allowed to be of different lengths. Such large-scale modifications on strings have various applications. They are among the most frequent chromosomal alterations, accounted for 30\% of all losses of heterozygosity, a major genetic event causing inactivation of cancer suppressor genes. In addition, among other applications, they are frequent modifications accounted in musical or in natural language information retrieval. However, despite of their central role in so many fields of text processing, little attention has been devoted to the problem of matching strings allowing for this kind of edit operation. In this paper we present three algorithms for solving the problem, all of them with a $\bigO(nm^3)$ worst-case and a $\bigO(m^2)$-space complexity, where $m$ and $n$ are the length of the pattern and of the text, respectively. % In particular, our first algorithm is based on the dynamic-programming approach. Our second solution improves the previous one by making use of the Directed Acyclic Word Graph of the pattern. Finally our third algorithm is based on an alignment procedure. We also show that under the assumptions of equiprobability and independence of characters, our second algorithm has a $\bigO(n\log^2_{\sigma} m)$ average time complexity, for an alphabet of size $\sigma \geq 4$.
Hypersurface support and prime ideal spectra for stable categories

We use hypersurface support to classify thick (two-sided) ideals in the stable categories of representations for several families of finite-dimensional integrable Hopf algebras: bosonized quantum complete intersections, quantum Borels in type $A$, Drinfeld doubles of height 1 Borels in finite characteristic, and rings of functions on finite group schemes over a perfect field. We then identify the prime ideal (Balmer) spectra for these stable categories. In the curious case of functions on a finite group scheme $G$, the spectrum of the category is identified not with the spectrum of cohomology, but with the quotient of the spectrum of cohomology by the adjoint action of the subgroup of connected components $\pi_0(G)$ in $G$.
Multipoint Correlation Functions: Spectral Representation and Numerical Evaluation

The many-body problem is usually approached from one of two perspectives: the first originates from an action and is based on Feynman diagrams, the second is centered around a Hamiltonian and deals with quantum states and operators. The connection between results obtained in either way is made through spectral (or Lehmann) representations, well known for two-point correlation functions. Here, we complete this picture by deriving generalized spectral representations for multipoint correlation functions that apply in all of the commonly used many-body frameworks: the imaginary-frequency Matsubara and the real-frequency zero-temperature and Keldysh formalisms. Our approach separates spectral from time-ordering properties and thereby elucidates the relation between the three formalisms. The spectral representations of multipoint correlation functions consist of partial spectral functions and convolution kernels. The former are formalism independent but system specific; the latter are system independent but formalism specific. Using a numerical renormalization group (NRG) method described in the accompanying paper, we present numerical results for selected quantum impurity models. We focus on the four-point vertex (effective interaction) obtained for the single-impurity Anderson model and for the dynamical mean-field theory (DMFT) solution of the one-band Hubbard model. In the Matsubara formalism, we analyze the evolution of the vertex down to very low temperatures and describe the crossover from strongly interacting particles to weakly interacting quasiparticles. In the Keldysh formalism, we first benchmark our results at weak and infinitely strong interaction and then reveal the rich real-frequency structure of the DMFT vertex in the coexistence regime of a metallic and insulating solution.
Ab initio ORTEP drawings: a case study of N-based molecular crystals with different chemical nature

The thermal motion of atoms and functional groups is a key characteristic of any molecular crystal, and such motion derived from scattering experiments is conveniently visualised by means of thermal ellipsoids (the famous “ORTEP” drawings). Unfortunately, it is often impossible to obtain the underlying anisotropic displacement parameters (ADPs) for hydrogen atoms, due to their low X-ray scattering power, and sometimes no ADPs can be refined at all even for heavier atoms. In these cases, it would seem advantageous to estimate ADPs by first-principles techniques, and indeed such ab initio ORTEP plots have become available very recently. Here, we test this young method for a representative set of hydrogen-bonded molecular crystals: first, we study urea (CON2H4) as a well-known benchmark, then, its all-nitrogen analogue guanidine (CN3H5); finally, we move on to rubidium guanidinate (RbCN3H4) as a specimen with pronounced ionic interactions. For all three systems, ADPs have been obtained from density-functional theory (DFT) based phonon computations using the PHONOPY software. The results are compared with neutron-diffraction data as the experimental “benchmark” in this regard, and a critical discussion of experimental aspects is given. We observe excellent agreement between experiment and theory for the hydrogen-bonded systems urea and guanidine at low temperature, whereas high-temperature data for guanidine deviate visibly, and the more salt-like RbCN3H4 may suffer from a less-than-ideal description even at 12 K. Both are discussed in depth as there are possible solutions and directions for further research. Generally, the present results shine a favourable light on a future, more routine application of combined experimental/theoretical approaches in chemical crystallography.
Effect of thidiazuron, benzylaminopurine and naphthalene acetic acid on in vitro propagation of tuberose (Polianthes tuberosa L.) from shoot tip explants.

Tuberose (Polianthes tuberosa L.) is an important export crop among small-scale farmers in Kenya. One of the main challenges facing production and marketing of good quality Tuberose cut flowers is the lack of clean planting material as the resource-poor farmers multiply their own propagules. The main objective of the present study was to evaluate the potential of Thidiazuron (TDZ), a phenyl urea, benzylaminopurine (BAP), a cytokinin and naphthalene acetic acid (NAA), an auxin, on in vitro propagation of tuberose from shoot tip explants. No multiple shooting was observed in any of the treatments tested. Results from the study indicated that TDZ, at low concentrations, was more potent than BAP in increasing shoot length and quality as well as the number of leaves per shoot. However, TDZ at high concentration (5 µM), was toxic leading to death of the shoot explant. Inclusion of NAA in media either supplemented with TDZ or BAP led to formation of calli, which did not differentiate further. Keywords: Thidiazuron; Benzylaminopurine; Naphthalene Acetic acid; propagation of Tuberose; Polianthes tuberosa L. JAGST Vol 6(1) 2004: 48-59