Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Recovery in the General Hypergraph Stochastic Block Model

This paper investigates fundamental limits of exact recovery in the general <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-uniform hypergraph stochastic block model (<inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-HSBM), wherein <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> nodes are partitioned into <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> disjoint communities with relative sizes <inline-formula> <tex-math notation="LaTeX">$(p_{1},\ldots , p_{k})$ </tex-math></inline-formula>. Each subset of nodes with cardinality <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is generated independently as an order-<inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> hyperedge with a certain probability that depends on the ground-truth communities that the <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> nodes belong to. The goal is to exactly recover the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> hidden communities based on the observed hypergraph. We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below the threshold (apart from a small regime of parameters that will be specified precisely). This threshold is represented in terms of a quantity which we term as the generalized Chernoff-Hellinger divergence between communities. Our result for this general model recovers prior results for the standard SBM and <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-HSBM with two symmetric communities as special cases. En route to proving our achievability results, we develop a polynomial-time two-stage algorithm that meets the threshold. The first stage adopts a certain hypergraph spectral clustering method to obtain a coarse estimate of communities, and the second stage refines each node individually via local refinement steps to ensure exact recovery.
Incoherent approximation for neutron up-scattering cross sections and its corrections for slow neutrons and low crystal temperatures

Stefan Döge,1, 2, ∗ Chen-Yu Liu,3 Albert Young,4 and Christoph Morkel5 1Technische Universität München, Department of Physics E18, James-Franck-Strasse 1, D-85748 Garching, Germany 2Institut Laue–Langevin, 71 avenue des Martyrs, F-38042 Grenoble Cedex 9, France 3Department of Physics, Indiana University, 727 East Third Street, Bloomington, Indiana 47405, USA 4Department of Physics, North Carolina State University, Raleigh, North Carolina 27695, USA 5Technische Universität München, Department of Physics E21, James-Franck-Strasse 1, D-85748 Garching, Germany (Dated: May 12, 2021)
Language Acquisition is Embodied, Interactive, Emotive: a Research Proposal

Humans' experience of the world is profoundly multimodal from the beginning, so why do existing state-of-the-art language models only use text as a modality to learn and represent semantic meaning? In this paper we review the literature on the role of embodiment and emotion in the interactive setting of spoken dialogue as necessary prerequisites for language learning for human children, including how words in child vocabularies are largely concrete, then shift to become more abstract as the children get older. We sketch a model of semantics that leverages current transformer-based models and a word-level grounded model, then explain the robot-dialogue system that will make use of our semantic model, the setting for the system to learn language, and existing benchmarks for evaluation.
Accountable Error Characterization

Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks. Accountability requires understanding of system limit and condition of erroneous predictions, as customers are often interested in understanding the incorrect predictions, and model developers are absorbed in finding methods that can be used to get incremental improvements to an existing system. Therefore, we propose an accountable error characterization method, AEC, to understand when and where errors occur within the existing black-box models. AEC, as constructed with human-understandable linguistic features, allows the model developers to automatically identify the main sources of errors for a given classification system. It can also be used to sample for the set of most informative input points for a next round of training. We perform error detection for a sentiment analysis task using AEC as a case study. Our results on the sample sentiment task show that AEC is able to characterize erroneous predictions into human understandable categories and also achieves promising results on selecting erroneous samples when compared with the uncertainty-based sampling.
Lagged teleconnections of climate variables identified via complex rotated Maximum Covariance Analysis

A proper description of ocean-atmosphere interactions is key for a correct understanding of climate evolution. The interplay among the different variables acting over the climate is complex, often leading to correlations across long spatial distances (teleconnections). In some occasions, those teleconnections occur with quite significant temporal shifts that are fundamental for the understanding of the underlying phenomena but which are poorly captured by standard methods. Applying orthogonal decomposition such as Maximum Covariance Analysis (MCA) to geophysical data sets allows to extract common dominant patterns between two different variables, but generally suffers from (i) the non-physical orthogonal constraint as well as (ii) the consideration of simple correlations, whereby temporally offset signals are not detected. Here we propose an extension, complex rotated MCA, to address both limitations. We transform our signals using the Hilbert transform and perform the orthogonal decomposition in complex space, allowing us to correctly correlate out-of-phase signals. Subsequent Varimax rotation removes the orthogonal constraints, leading to more physically meaningful modes of geophysical variability. As an example of application, we have employed this method on sea surface temperature and continental precipitation; our method successfully captures the temporal and spatial interactions between these two variables, namely for (i) the seasonal cycle, (ii) canonical ENSO, (iii) the global warming trend, (iv) the Pacific Decadal Oscillation, (v) ENSO Modoki and finally (vi) the Atlantic Meridional Mode. The complex rotated modes of MCA provide information on the regional amplitude, and under certain conditions, the regional time lag between changes on ocean temperature and land precipitation.
Connection between magnetic interactions and the spin-wave gap of the insulating phase of 
NaOsO3

Nikolaos Ntallis, Vladislav Borisov, Yaroslav O. Kvashnin, Danny Thonig, Erik Sjöqvist, Anders Bergman, Anna Delin, 4 Olle Eriksson, 2 and Manuel Pereiro ∗ Department of Physics and Astronomy, Uppsala University, Uppsala 751 20, Sweden School of Science and Technology, Örebro University, SE-701 82, Örebro, Sweden Department of Applied Physics, School of Engineering Sciences, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden Swedish e-Science Research Center (SeRC), KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden (Dated: May 12, 2021)
Resource-aware Distributed Gaussian Process Regression for Real-time Machine Learning

—We study the problem where a group of agents aim to collaboratively learn a common latent function through streaming data. We propose a Resource-aware Gaussian process regression algorithm that is cognizant of agents’ limited capabilities in communication, computation and memory. We quantify the improvement that limited inter-agent communication brings to the transient and steady-state performance in predictive variance and predictive mean. A set of simulations is conducted to evaluate the developed algorithm.
A systematic study of silicate absorption features in heavily obscured AGNs observed by Spitzer/IRS

Context. Heavily obscured active galactic nuclei (AGNs) are known to show deep silicate absorption features in the mid-infrared (mid-IR) wavelength range of 10–20 μm. The detailed profiles of the features reflect the properties of silicate dust, which are likely to include information on AGN activities obscured by large amounts of dust. Aims. We reveal AGN activities obscured by large amounts of dust through the silicate dust properties obtained from the mid-IR spectral bands. Methods. We selected 115 mid-IR spectra of heavily obscured AGNs observed by Spitzer/IRS and systematically analyzed the composition of silicate dust by spectral fitting using the 10 μm amorphous and 23 μm crystalline bands. Results. We find that the main component of the silicate dust obscuring AGNs is amorphous olivine, the median mass column density of which is one order of magnitude higher than those of the minor components of amorphous pyroxene and crystalline forsterite. The median mass fraction of the amorphous pyroxene, ∼2%, is significantly lower than that of the diffuse interstellar medium (ISM) dust in our Galaxy, while the median mass fraction of the crystalline forsterite, ∼6%, is higher than that of the diffuse ISM dust. We also find that the mass fractions of the amorphous pyroxene and the crystalline forsterite positively correlate with each other. Conclusions. The low mass fraction of the amorphous pyroxene suggests that the obscuring silicate dust is newly formed, originating from starburst activities. The relatively high mass fraction of crystalline forsterite implies that the silicate dust is processed in the high temperature environment close to the nucleus and transported to outer cooler regions by molecular outflows. The positive correlation between the mass fractions can be naturally explained considering that amorphous pyroxene is transformed from crystalline forsterite by ion bombardments. We also find that spectra with high ratios of the H2O ice absorption to silicate mass column density tend to indicate low mass fractions of amorphous pyroxene and crystalline forsterite, which is consistent with the scenario of the thermal dust processing close to the nucleus.
qRobot: A Quantum Computing Approach in Mobile Robot Order Picking and Batching Problem Solver Optimization

This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance traveled in warehouses and distribution centers where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. We developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave), and Pennylane locally or remotely. The proof of concept, when run in the above stated quantum environments, showed the execution time of our algorithm with different public access simulators on the market, computational results of our picking and batching algorithm, and analyze the quantum real-time execution. Our findings are that the behavior of the Amazon Braket D-Wave is better than Gate-based Quantum Computing over 20 qubits, and that AWS-Braket has better time performance than Qiskit or Pennylane.
Gravity with a generalized conformal scalar field: Theory and solutions

We naturally extend the theory of gravity with a conformally coupled scalar field by only requiring conformal invariance of the scalar field equation of motion and not of the action. The classically extended theory incorporates a scalar-Gauss-Bonnet sector and has second-order equations of motion, belonging to the Horndeski class. Remarkably, the theory features a purely geometrical field equation that allows for closed-form black hole solutions and cosmologies to be easily found. These solutions permit investigations of in-vogue scalar-Gauss-Bonnet corrections to the gravitational action without the need of resorting to approximations or numerical methods. We discuss on the connection to the recently formulated 4D Einstein-Gauss-Bonnet theory of gravity.
Temporal graph-based approach for behavioural entity classification

Graph-based analyses have gained a lot of relevance in the past years due to their high potential in describing complex systems by detailing the actors involved, their relations and their behaviours. Nevertheless, in scenarios where these aspects are evolving over time, it is not easy to extract valuable information or to characterize correctly all the actors. In this study, a two phased approach for exploiting the potential of graph structures in the cybersecurity domain is presented. The main idea is to convert a network classification problem into a graph-based behavioural one. We extract these graph structures that can represent the evolution of both normal and attack entities and apply a temporal dissection approach in order to highlight their micro-dynamics. Further, three clustering techniques are applied to the normal entities in order to aggregate similar behaviours, mitigate the imbalance problem and reduce noisy data. Our approach suggests the implementation of two promising deep learning paradigms for entity classification based on Graph Convolutional Networks.
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies

Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semantic similarity is typically restricted to hierarchical relations within the ontology, knowledge graph embeddings consider their full breadth. However, embeddings are produced over a single graph and complex tasks such as gene-disease association may require additional ontologies. We investigate the impact of employing richer semantic representations that are based on more than one ontology, able to represent both genes and diseases and consider multiple kinds of relations within the ontologies. Our experiments demonstrate the value of employing knowledge graph embeddings based on random-walks and highlight the need for a closer integration of different ontologies.
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds

This paper presents Poisoning MorphNet, the first backdoor attack method on point clouds. Conventional adversarial attack takes place in the inference stage, often fooling a model by perturbing samples. In contrast, backdoor attack aims to implant triggers into a model during the training stage, such that the victim model acts normally on the clean data unless a trigger is present in a sample. This work follows a typical setting of clean-label backdoor attack, where a few poisoned samples (with their content tampered yet labels unchanged) are injected into the training set. The unique contributions of MorphNet are two-fold. First, it is key to ensure the implanted triggers both visually imperceptible to humans and lead to high attack success rate on the point clouds. To this end, MorphNet jointly optimizes two objectives for sample-adaptive poisoning: a reconstruction loss that preserves the visual similarity between benign / poisoned point clouds, and a classification loss that enforces a modern recognition model of point clouds tends to mis-classify the poisoned sample to a pre-specified target category. This implicitly conducts spectral separation over point clouds, hiding sample-adaptive triggers in fine-grained high-frequency details. Secondly, existing backdoor attack methods are mainly designed for image data, easily defended by some point cloud specific operations (such as denoising). We propose a third loss in MorphNet for suppressing isolated points, leading to improved resistance to denoising-based defense. Comprehensive evaluations are conducted on ModelNet40 and ShapeNetcorev2. Our proposed Poisoning MorphNet outstrips all previous methods with clear margins.
Electron–electron double resonance detected NMR spectroscopy using ensemble NV centers at 230 GHz and 8.3 T

X iv :2 10 5. 05 16 8v 1 [ co nd -m at . m es -h al l] 1 1 M ay 2 02 1 Electron-electron double resonance detected NMR spectroscopy using ensemble NV centers at 230 GHz and 8.3 Tesla Benjamin Fortman, a) Laura Mugica-Sanchez, Noah Tischler, Cooper Selco, Yuxiao Hang, Karoly Holczer, and Susumu Takahashi 2 Department of Chemistry, University of Southern California, Los Angeles CA 90089, USA Department of Physics & Astronomy, University of Southern California, Los Angeles CA 90089, USA Department of Physics & Astronomy, University of California, Los Angeles CA 90095, USA
Near Neighbor Search via Efficient Average Distortion Embeddings

A recent series of papers by Andoni, Naor, Nikolov, Razenshteyn, and Waingarten (STOC 2018, FOCS 2018) has given approximate near neighbour search (NNS) data structures for a wide class of distance metrics, including all norms. In particular, these data structures achieve approximation on the order of $p$ for $\ell_p^d$ norms with space complexity nearly linear in the dataset size $n$ and polynomial in the dimension $d$, and query time sub-linear in $n$ and polynomial in $d$. The main shortcoming is the exponential in $d$ pre-processing time required for their construction. In this paper, we describe a more direct framework for constructing NNS data structures for general norms. More specifically, we show via an algorithmic reduction that an efficient NNS data structure for a given metric is implied by an efficient average distortion embedding of it into $\ell_1$ or into Euclidean space. In particular, the resulting data structures require only polynomial pre-processing time, as long as the embedding can be computed in polynomial time. As a concrete instantiation of this framework, we give an NNS data structure for $\ell_p$ with efficient pre-processing that matches the approximation factor, space and query complexity of the aforementioned data structure of Andoni et al. On the way, we resolve a question of Naor (Analysis and Geometry in Metric Spaces, 2014) and provide an explicit, efficiently computable embedding of $\ell_p$, for $p \ge 2$, into $\ell_2$ with (quadratic) average distortion on the order of $p$. We expect our approach to pave the way for constructing efficient NNS data structures for all norms.
Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols

Human language has been described as a system that makes \textit{use of finite means to express an unlimited array of thoughts}. Of particular interest is the aspect of compositionality, whereby, the meaning of a compound language expression can be deduced from the meaning of its constituent parts. If artificial agents can develop compositional communication protocols akin to human language, they can be made to seamlessly generalize to unseen combinations. Studies have recognized the role of curiosity in enabling linguistic development in children. In this paper, we seek to use this intrinsic feedback in inducing a systematic and unambiguous protolanguage. We demonstrate how compositionality can enable agents to not only interact with unseen objects but also transfer skills from one task to another in a zero-shot setting: \textit{Can an agent, trained to `pull' and `push twice', `pull twice'? }.
Excess Mortality in England during the 2019 Summer Heatwaves

There is increasing evidence that rising temperatures and heatwaves in the United Kingdom are associated with an increase in heat-related mortality. However, the Public Health England (PHE) Heatwave mortality monitoring reports, which use provisional death registrations to estimate heat-related mortality in England during heatwaves, have not yet been evaluated. This study aims to retrospectively quantify the impact of heatwaves on mortality during the 2019 summer period using daily death occurrences. Second, using the same method, it quantifies the heat-related mortality for the 2018 and 2017 heatwave periods. Last, it compares the results to the estimated excess deaths for the same period in the PHE Heatwave mortality monitoring reports. The number of cumulative excess deaths during the summer 2019 heatwaves were minimal (161) and were substantially lower than during the summer 2018 heatwaves (1700 deaths) and summer 2017 heatwaves (1489 deaths). All findings were at variance with the PHE Heatwave mortality monitoring reports which estimated cumulative excess deaths to be 892, 863 and 778 during the heatwave periods of 2019, 2018 and 2017, respectively. Issues are identified in the use of provisional death registrations for mortality monitoring and the reduced reliability of the Office for National Statistics (ONS) daily death occurrences database before 2019. These findings may identify more reliable ways to monitor heat mortality during heatwaves in the future.
Preparation of a P(FcA-co-ANI)/graphene composite for application in supercapacitors

A conducting copolymer of 1,1′-ferrocenediacyl anilide and aniline (P(FcA-co-ANI)) was synthesized, which had a conjugated structure and ferrocene moieties in the main chain. The monomer and copolymer were characterized using proton nuclear magnetic resonance and Fourier-transform infrared (FTIR) spectroscopies. A P(FcA-co-ANI)/reduced graphene oxide (rGO) composite was synthesized by oxidation polymerization, using rGO as a substrate. The characteristic peaks of P(FcA-co-ANI) and rGO were observed in the FTIR spectrum of P(FcA-co-ANI)/rGO. The X-ray diffraction pattern of P(FcA-co-ANI)/rGO exhibited similar peaks to the pattern of P(FcA-co-ANI), except for the absence of the weak broad peak at 9.0° owing to rGO. The surface morphologies of the materials were characterized by atomic force microscopy, transmission electron microscopy and scanning electron microscopy. The interlayer distances of rGO and P(FcA-co-ANI)/rGO were 0.96 and 1.38 nm, respectively. The morphology of the copolymer was spherical, and it contained island structures covering the surface of the graphene layers. The electrochemical properties of the composite were measured by cyclic voltammetry, galvanostatic charge–discharge measurements and electrochemical impedance spectroscopy. The maximum specific capacitance of the composite was 722.5 F/g at 0.5 A/g. The diffusion resistance was very small, and the composites durability was sufficient for subjecting to prolonged oxidation and reduction.