Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsically gapless topological phases

Topology in quantum matter is typically associated with gapped phases. For example, in symmetry protected topological (SPT) phases, the bulk energy gap localizes edge modes near the boundary. In this work we identify a new mechanism that leads to topological phases which are not only gapless but where the absence of a gap is essential. These `intrinsically gapless SPT phases' have no gapped counterpart and are hence also distinct from recently discovered examples of gapless SPT phases. The essential ingredient of these phases is that on-site symmetries act in an anomalous fashion at low energies. Intrinsically gapless SPT phases are found to display several unique properties including (i) protected edge modes that are impossible to realize in a gapped system with the same symmetries, (ii) string order parameters that are likewise forbidden in gapped phases, and (iii) constraints on the phase diagram obtained upon perturbing the phase. We verify predictions of the general theory in a specific realization protected by $\mathbb Z_4$ symmetry, the one dimensional Ising-Hubbard chain, using both numerical simulations and effective field theory. We also discuss extensions to higher dimensions and possible experimental realizations.
Closing the seed dispersal loop for Guettarda viburnoides (Rub.): Connecting patterns of avian seed dispersal with population growth in a Neotropical savanna

i ACKNOWLEDGMENTS.iii CHAPTER 1 — QUANTITY AND QUALITY COMPONENTS OF GUETTARDA VIBURNOIDES (RUBIACEAE) SEED DISPERSAL BY FRUIT-EATING BIRDS IN BOLIVIAN SAVANNAS.1 Abstract.
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation

We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards accurate and high-resolution 2D human pose estimation. Recently, high-performance hand-crafted convolutional networks for pose estimation show growing demands on multi-scale fusion and high-resolution representations. However, current NAS works exhibit limited flexibility on scale searching, they dominantly adopt simplified search spaces of single-branch architectures. Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations. The presentedAutoPose framework is able to search for multi-branch scales and network depth, in addition to the cell-level microstructure. Motivated by the search space, a novel bi-level optimization method is presented, where the network-level architecture is searched via reinforcement learning, and the cell-level search is conducted by the gradient-based method. Within 2.5 GPU days, AutoPose is able to find very competitive architectures on the MS COCO dataset, that are also transferable to the MPII dataset. Our code is available at this https URL.
Recovering variable stars in large surveys: EAup Algol-type class in the Catalina Survey

The discovery and characterization of Algol eclipsing binaries (EAs) provide an opportunity to contribute for a better picture of the structure and evolution of low-mass stars. However, the cadence of most current photometric surveys hinders the detection of EAs since the separation between observations is usually larger than the eclipse(s) duration and hence few measurements are found at the eclipses. Even when those objects are detected as variable, their periods can be missed if an appropriate oversampling factor is not used in the search tools. In this paper, we apply this approach to find the periods of stars catalogued in the Catalina Real-Time Transient Survey (CRTS) as EAs having unknown period (EA_(up)). As a result, the periods of ∼56percent of them were determined. Eight objects were identified as low-mass binary systems and modelled with the Wilson & Devinney synthesis code combined with a Markov chain Monte Carlo optimization procedure. The computed masses and radii are in agreement with theoretical models and show no evidence of inflated radii. This paper is the first of a series aiming to identify suspected binary systems in large surveys.
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

Generative modeling has recently shown great promise in computer vision, but its success is often limited to separate tasks. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model across various tasks. We instantiate it on the illustrative dual-task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of the object from new viewpoints. To this end, we propose bowtie networks that jointly learn 3D geometric and semantic representations with feedback in the loop. Experimental evaluation on challenging fine-grained recognition datasets demonstrates that our synthesized images are realistic from multiple viewpoints and significantly improve recognition performance as ways of data augmentation, especially in the low-data regime. We further show that our approach is flexible and can be easily extended to incorporate other tasks, such as style guided synthesis.
Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem

In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in computer science, with applications in economics, business, finance, etc. We attempt to solve a number of small knapsack problems whose optimal solutions are known; we find that adiabatic quantum optimization fails to produce solutions corresponding to optimal filling of the knapsack in all problem instances. We compare results obtained on the quantum hardware to the classical simulated annealing algorithm and two solvers employing a hybrid branch-and-bound algorithm. The simulated annealing algorithm also fails to produce the optimal filling of the knapsack, though solutions obtained by simulated and quantum annealing are no more similar to each other than to the correct solution. We discuss potential causes for this observed failure of adiabatic quantum optimization.
The Branch Set of Minimal Disks in Metric Spaces

We study the structure of the branch set of solutions to Plateau’s problem in metric spaces satisfying a quadratic isoperimetric inequality. In our 1st result, we give examples of spaces with isoperimetric constant arbitrarily close to the Euclidean isoperimetric constant $(4\pi )^{-1}$ for which solutions have large branch set. This complements recent results of Lytchak–Wenger and Stadler stating, respectively, that any space with Euclidean isoperimetric constant is a CAT($0$) space and solutions to Plateau’s problem in a CAT($0$) space have only isolated branch points. We also show that any planar cell-like set can appear as the branch set of a solution to Plateau’s problem. These results answer two questions posed by Lytchak and Wenger. Moreover, we investigate several related questions about energy-minimizing parametrizations of metric disks: when such a map is quasisymmetric, when its branch set is empty, and when it is unique up to a conformal diffeomorphism.
Multi-Party Private Set Intersection: An Information-Theoretic Approach

We investigate the problem of multi-party private set intersection (MP-PSI). In MP-PSI, there are <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> parties, each storing a data set <inline-formula> <tex-math notation="LaTeX">${\mathcal {P}}_{i}$ </tex-math></inline-formula> over <inline-formula> <tex-math notation="LaTeX">$N_{i}$ </tex-math></inline-formula> replicated and non-colluding databases, and we want to calculate the intersection of the data sets <inline-formula> <tex-math notation="LaTeX">$\cap _{i=1}^{M} {\mathcal {P}}_{i}$ </tex-math></inline-formula> without leaking any information beyond the set intersection to any of the parties. We consider a specific communication protocol where one of the parties, called the leader party, initiates the MP-PSI protocol by sending queries to the remaining parties which are called client parties. The client parties are not allowed to communicate with each other. We propose an information-theoretic scheme that privately calculates the intersection <inline-formula> <tex-math notation="LaTeX">$\cap _{i=1}^{M} {\mathcal {P}}_{i}$ </tex-math></inline-formula> with a download cost of <inline-formula> <tex-math notation="LaTeX">$D = \min _{t \in \{1,\ldots, M\}} \sum _{i \in \{1,\ldots, M\}\setminus {t}} \lceil \frac {| {\mathcal {P}}_{t}|N_{i}}{N_{i}-1}\rceil $ </tex-math></inline-formula>. Similar to the 2-party PSI problem, our scheme builds on the connection between the PSI problem and the multi-message symmetric private information retrieval (MM-SPIR) problem. Our scheme is a non-trivial generalization of the 2-party PSI scheme as it needs an intricate design of the shared common randomness. Interestingly, in terms of the download cost, our scheme does not incur any penalty due to the more stringent privacy constraints in the MP-PSI problem compared to the 2-party PSI problem.
Estimating action plans for smart poultry houses

In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCR[t]), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCR[t] for each poultry house. In this article, we claim that the specialist's perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR[t].
On codes decoding a constant fraction of errors on the BSC

We strengthen the results from a recent work by the second author, achieving bounds on the weight distribution of binary linear codes that are successful under block-MAP (as well as bit-MAP) decoding on the BEC. We conclude that a linear code that is successful on the BEC can also decode over a range of binary memoryless symmetric (BMS) channels. In particular, applying the result of Kudekar, Kumar, Mondelli, Pfister, Şaşoğlu and Urbanke from STOC 2016, we prove that a Reed–Muller code of positive rate R decodes errors on the p with high probability if p < 1/2 − √2−R(1−2−R).
Exploring Longitudinal Effects of Session-based Recommendations

Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an ongoing session. The lack of long-term preference information about individual users in such settings usually results in a limited level of personalization, where a small set of popular items may be recommended to many users. This repeated exposure of such a subset of the items through the recommendations may in turn lead to a reinforcement effect over time, and to a system which is not able to help users discover new content anymore to the desirable extent. In this work, we investigate such potential longitudinal effects of session-based recommendations in a simulation-based approach. Specifically, we analyze to what extent algorithms of different types may lead to concentration effects over time. Our experiments in the music domain reveal that all investigated algorithms—both neural and heuristic ones—may lead to lower item coverage and to a higher concentration on a subset of the items. Additional simulation experiments however also indicate that relatively simple re-ranking strategies, e.g., by avoiding too many repeated recommendations in the music domain, may help to deal with this problem.
How to Put Users in Control of their Data via Federated Pair-Wise Recommendation

Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices. In such scenarios, privacy is a crucial concern since users may not be willing to share their sensitive preferences (e.g., visited locations, read books, bought items) with a central server. Unfortunately, data harvesting and collection is at the basis of modern, state-of-the-art approaches to recommendation. Decreased users' willingness to share personal information along with data minimization/protection policies (such as the European GDPR), can result in the "data scarcity" dilemma affecting data-intensive applications such as recommender systems (RS). We argue that scarcity of adequate data due to privacy concerns can severely impair the quality of learned models and, in the long term, result in a turnover and disloyal customers with direct consequences for lives, society, and businesses. To address these issues, we present FPL, an architecture in which users collaborate in training a central factorization model while controlling the amount of sensitive data leaving their devices. The proposed approach implements pair-wise learning to rank optimization by following the Federated Learning principles conceived originally to mitigate the privacy risks of traditional machine learning. We have conducted an extensive experimental evaluation on three Foursquare datasets and have verified the effectiveness of the proposed architecture concerning accuracy and beyond-accuracy objectives. We have analyzed the impact of communication cost with the central server on the system's performance, by varying the amount of local computation and training parallelism. Finally, we have carefully examined the impact of disclosed users' information on the quality of the final model and .
HunFlair: an easy-to-use tool for state-of-the-art biomedical named entity recognition

Abstract Summary Named entity recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, be highly accurate and be robust toward variations in text genre and style. We present HunFlair, a NER tagger fulfilling these requirements. HunFlair is integrated into the widely used NLP framework Flair, recognizes five biomedical entity types, reaches or overcomes state-of-the-art performance on a wide set of evaluation corpora, and is trained in a cross-corpus setting to avoid corpus-specific bias. Technically, it uses a character-level language model pretrained on roughly 24 million biomedical abstracts and three million full texts. It outperforms other off-the-shelf biomedical NER tools with an average gain of 7.26 pp over the next best tool in a cross-corpus setting and achieves on-par results with state-of-the-art research prototypes in in-corpus experiments. HunFlair can be installed with a single command and is applied with only four lines of code. Furthermore, it is accompanied by harmonized versions of 23 biomedical NER corpora. Availability and implementation HunFlair ist freely available through the Flair NLP framework (https://github.com/flairNLP/flair) under an MIT license and is compatible with all major operating systems. Supplementary information Supplementary data are available at Bioinformatics online.
On locally analytic vectors of the completed cohomology of modular curves

Abstract We study the locally analytic vectors in the completed cohomology of modular curves and determine the eigenvectors of a rational Borel subalgebra of $\mathfrak {gl}_2(\mathbb {Q}_p)$ . As applications, we prove a classicality result for overconvergent eigenforms of weight 1 and give a new proof of the Fontaine–Mazur conjecture in the irregular case under some mild hypotheses. For an overconvergent eigenform of weight k, we show its corresponding Galois representation has Hodge–Tate–Sen weights $0,k-1$ and prove a converse result.
Magnetorotational core collapse of possible GRB progenitors – III. Three-dimensional models

We explore the influence of non-axisymmetric modes on the dynamics of the collapsed core of rotating, magnetised high-mass stars in three-dimensional simulations of a rapidly rotating star with an initial mass of $M_{\rm ZAMS} = 35 M_\odot$ endowed with four different pre-collapse configurations of the magnetic field, ranging from moderate to very strong field strength and including the field predicted by the stellar evolution model. The model with the weakest magnetic field achieves shock revival due to neutrino heating in a gain layer characterised by a large-scale, hydrodynamic $m = 1$ spiral mode. Later on, the growing magnetic field of the proto neutron star launches weak outflows into the early ejecta. Their orientation follows the evolution of the rotational axis of the proto neutron star, which starts to tilt from the original orientation due to the asymmetric accretion flows impinging on its surface. The models with stronger magnetisation generate mildly relativistic, magnetically driven polar outflows propagating over a distance of $10^4$ km within a few 100 ms. These jets are stabilised against disruptive non-axisymmetric instabilities by their fast propagation and by the shear of their toroidal magnetic field. Within the simulation times of around 1 s, the explosions reach moderate energies and the growth of the proto neutron star masses ceases at values substantially below the threshold for black hole formation, which, in combination with the high rotational energies, might suggest a possible later proto-magnetar activity.
Category-Level 3D Non-Rigid Registration from Single-View RGB Images

In this paper, we propose a novel approach to solve the 3D non-rigid registration problem from RGB images using Convolutional Neural Networks (CNNs). Our objective is to find a deformation field (typically used for transferring knowledge between instances, e.g., grasping skills) that warps a given 3D canonical model into a novel instance observed by a single-view RGB image. This is done by training a CNN that infers a deformation field for the visible parts of the canonical model and by employing a learned shape (latent) space for inferring the deformations of the occluded parts. As result of the registration, the observed model is reconstructed. Because our method does not need depth information, it can register objects that are typically hard to perceive with RGB-D sensors, e.g. with transparent or shiny surfaces. Even without depth data, our approach outperforms the Coherent Point Drift (CPD) registration method for the evaluated object categories.
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition

This paper presents a transfer learning method in speech emotion recognition based on a Time-Delay Neural Network (TDNN) architecture. A major challenge in the current speech-based emotion detection research is data scarcity. The proposed method resolves this problem by applying transfer learning techniques in order to leverage data from the automatic speech recognition (ASR) task for which ample data is available. Our experiments also show the advantage of speaker-class adaptation modeling techniques by adopting identity-vector (i-vector) based features in addition to standard Mel-Frequency Cepstral Coefficient (MFCC) features. [1] We show the transfer learning models significantly outperform the other methods without pretraining on ASR. The experiments performed on the publicly available IEMOCAP dataset which provides 12 hours of motional speech data. The transfer learning was initialized by using the Ted-Lium v.2 speech dataset providing 207 hours of audio with the corresponding transcripts. We achieve the highest significantly higher accuracy when compared to state-of-the-art, using five-fold cross validation. Using only speech, we obtain an accuracy 71.7% for anger, excitement, sadness, and neutrality emotion content.
Rigidity results for complete manifolds with nonnegative scalar curvature

In this paper, we are going to show some rigidity results for complete open Riemannian manifolds with nonnegative scalar curvature. Without using the famous Cheeger-Gromoll splitting theorem we give a new proof to a rigidity result for complete manifolds with nonnegative scalar curvature admitting a proper smooth map to $T^{n-1}\times \mathbf R$ with nonzero degree. Here we introduce a trick to obtain the compactness of limit hypersurface from locally graphical convergence. Based on the same idea we also establish an optimal $2$-systole inequality for several classes of complete Riemannian manifolds with positive scalar curvature and further prove a rigidity result for the equality case.
A Survey of Active Learning for Text Classification using Deep Neural Networks

Natural language processing (NLP) and neural networks (NNs) have both undergone significant changes in recent years. For active learning (AL) purposes, NNs are, however, less commonly used -- despite their current popularity. By using the superior text classification performance of NNs for AL, we can either increase a model's performance using the same amount of data or reduce the data and therefore the required annotation efforts while keeping the same performance. We review AL for text classification using deep neural networks (DNNs) and elaborate on two main causes which used to hinder the adoption: (a) the inability of NNs to provide reliable uncertainty estimates, on which the most commonly used query strategies rely, and (b) the challenge of training DNNs on small data. To investigate the former, we construct a taxonomy of query strategies, which distinguishes between data-based, model-based, and prediction-based instance selection, and investigate the prevalence of these classes in recent research. Moreover, we review recent NN-based advances in NLP like word embeddings or language models in the context of (D)NNs, survey the current state-of-the-art at the intersection of AL, text classification, and DNNs and relate recent advances in NLP to AL. Finally, we analyze recent work in AL for text classification, connect the respective query strategies to the taxonomy, and outline commonalities and shortcomings. As a result, we highlight gaps in current research and present open research questions.