Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs

Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.
Quantum experiments with microscale particles

ABSTRACT Quantum theory is incredibly successful, explaining the microscopic world with great accuracy, from the behaviour of subatomic particles to chemical reactions to solid-state electronics. There is not a single experimental finding challenging its predictions, and ever more quantum phenomena are exploited in technology, including interferometric sensing and quantum cryptography. In order to explore novel applications and test the validity of quantum physics at the macroscale, researchers strive to prepare ever heavier and bigger objects in quantum superpositions. Experiments with levitated microscale particles are about to push this quest into uncharted waters.
Certifying Neural Network Robustness to Random Input Noise from Samples

Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in the literature are designed for adversarial input uncertainty, but researchers have recently shown a need for methods that consider random uncertainty. In this paper, we propose a novel robustness certification method that upper bounds the probability of misclassification when the input noise follows an arbitrary probability distribution. This bound is cast as a chance-constrained optimization problem, which is then reformulated using input-output samples to replace the optimization constraints. The resulting optimization reduces to a linear program with an analytical solution. Furthermore, we develop a sufficient condition on the number of samples needed to make the misclassification bound hold with overwhelming probability. Our case studies on MNIST classifiers show that this method is able to certify a uniform infinity-norm uncertainty region with a radius of nearly 50 times larger than what the current state-of-the-art method can certify.
5G-Flow: Flexible and Efficient 5G RAN Architecture Using OpenFlow

Convergence of multiple access technologies is one of the key enablers in providing a diverse set of services to the Fifth Generation (5G) users. Though the 3rd Generation Partnership Project (3GPP) 5G standard defines a common core supporting multiple Radio Access Technologies (RATs), Radio Access Network (RAN) level decisions are taken separately across individual RATs as the existing 5G architecture lacks a unified control and management framework for a multi-RAT network. A unified access network is likely to utilize RAN resources more efficiently and provide an improved performance. To bridge these gaps, we present an OpenFlow based RAN architecture comprising multiple RATs. We refer to it as \textit{5G-Flow}. With minimal changes in the 3GPP 5G RAN and none in the core network, we are able to realize a unified and integrated multi-access 5G-Flow RAN. We simplify the existing 3GPP 5G RAN by replacing RAN nodes with OpenFlow switches and a Software-Defined Networking (SDN) controller. Moreover, a UE in the 5G-Flow network can use 5G RAN to connect to any core network (4G or 5G) or directly connect to Internet without going via the core network. We also present a simple method to realize 5G non-standalone architecture using 5G-Flow RAN. We have developed an evaluation platform to compare the performance of our architecture with the standard 3GPP 5G network. Results demonstrate significant gains in the network performance of 5G-Flow RAN architecture over the existing 3GPP 5G network.
Potentials and challenges of polymer informatics: exploiting machine learning for polymer design

There has been rapidly growing demand of polymeric materials coming from different aspects of modern life because of the highly diverse physical and chemical properties of polymers. Polymer informatics is an interdisciplinary research field of polymer science, computer science, information science and machine learning that serves as a platform to exploit existing polymer data for efficient design of functional polymers. Despite many potential benefits of employing a data-driven approach to polymer design, there has been notable challenges of the development of polymer informatics attributed to the complex hierarchical structures of polymers, such as the lack of open databases and unified structural representation. In this study, we review and discuss the applications of machine learning on different aspects of the polymer design process through four perspectives: polymer databases, representation (descriptor) of polymers, predictive models for polymer properties, and polymer design strategy. We hope that this paper can serve as an entry point for researchers interested in the field of polymer informatics.
Towards Self-Improving Hybrid Elasticity Control of Cloud-based Software Systems

Elasticity is a form of self-adaptivity in cloud-based software systems that is typically restricted to the infrastructure layer and realized through auto-scaling. However, both reactive and proactive forms of infrastructure auto-scaling have limitations, when used separately as well as together. To address these limitations, we propose an approach for self-improving hybrid elasticity control that combines (a) infrastructure and software elasticity, and (b) proactive, reactive and responsive decision-making. At the infrastructure layer, resources are provisioned proactively based on one-step-ahead workload forecasts, and reactively, based on observed workload variations. At the software layer, features are activated or deactivated in response to transient, minor deviations from the predicted workload. The proposed approach can lead to better performance-aware and cost-effective resource management in cloud-based software systems. We validate our approach via a partial realization and simulation with real-world datasets.
Pick-Place With Uncertain Object Instance Segmentation and Shape Completion

In this paper we consider joint perception and control of a pick-place system. It is important to consider perception and control jointly as some actions are more likely to succeed than others given non-uniform, perceptual uncertainty. Our approach is to combine 3D object instance segmentation and shape completion with classical regrasp planning. We use the perceptual modules to estimate their own uncertainty and then incorporate this uncertainty as a regrasp planning cost. We compare 7 different regrasp planning cost functions, 4 of which explicitly model probability of plan execution success. Results show uncertainty-aware costs improve performance for complex tasks, e.g., for a bin packing task, object placement success is 6.2% higher in simulation and 4.1% higher in the real world with an uncertainty-aware cost versus the commonly used minimum-number-of-grasps cost.
Robust asteroseismic properties of the bright planet host HD 38529

The Transiting Exoplanet Survey Satellite (TESS) is recording short-cadence, high duty-cycle timeseries across most of the sky, which presents the opportunity to detect and study oscillations in interesting stars, in particular planet hosts. We have detected and analysed solar-like oscillations in the bright G4 subgiant HD 38529, which hosts an inner, roughly Jupiter-mass planet on a $14.3\, \mathrm{d}$ orbit and an outer, low-mass brown dwarf on a $2136\, \mathrm{d}$ orbit. We combine results from multiple stellar modelling teams to produce robust asteroseismic estimates of the star’s properties, including its mass $M=1.48\pm 0.04\, \mathrm{M}_\odot {}$, radius $R=2.68\pm 0.03\, \mathrm{R}_\odot {}$, and age $t=3.07\pm 0.39\, \mathrm{Gyr}{}$. Our results confirm that HD 38529 has a mass near the higher end of the range that can be found in the literature and also demonstrate that precise stellar properties can be measured given shorter timeseries than produced by CoRoT, Kepler, or K2.
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning

Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource management. Crucial common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society's greatest challenges such as food security, inequality and climate change. Here we take inspiration from a recent research program investigating the game-theoretic incentives of humans in social dilemma situations such as the well-known tragedy of the commons. However, instead of focusing on biologically evolved human-like agents, our concern is rather to better understand the learning and operating behaviour of engineered networked systems comprising general-purpose reinforcement learning agents, subject only to nonbiological constraints such as memory, computation and communication bandwidth. Harnessing tools from empirical game-theoretic analysis, we analyse the differences in resulting solution concepts that stem from employing different information structures in the design of networked multi-agent systems. These information structures pertain to the type of information shared between agents as well as the employed communication protocol and network topology. Our analysis contributes new insights into the consequences associated with certain design choices and provides an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.
Geometry of Information Structures, Strategic Measures and associated Control Topologies.

In many areas of applied mathematics, engineering, and social and natural sciences, decentralization of information is a key aspect determining how to approach a problem. In this review article, we study information structures in a probability theoretic and geometric context. We define information structures, place various topologies on them, and study closedness, compactness and convexity properties on the strategic measures induced by information structures and decentralized control/decision policies under varying degree of relaxations with regard to access to private or common randomness. Ultimately, we present existence and tight approximation results for optimal decision/control policies. We discuss various lower bounding techniques, through relaxations and convex programs ranging from classically realizable and classically non-realizable (such as quantum and non-signaling) relaxations. For each of these, we establish closedness and convexity properties and also a hierarchy of correlation structures. As a second main theme, we review and introduce various topologies on decision/control strategies defined independent of information structures, but for which information structures determine whether the topologies entail utility in arriving at existence, compactness, convexification or approximation results. These approaches, which we term as the strategic measures approach and the control topology approach, lead to complementary results on existence, approximations and upper and lower bounds in optimal decentralized decision and control.
R-GAP: Recursive Gradient Attack on Privacy

Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data. Many such frameworks only ask collaborators to share their local update of a common model, i.e. gradients with respect to locally stored data, instead of exposing their raw data to other collaborators. However, recent optimization-based gradient attacks show that raw data can often be accurately recovered from gradients. It has been shown that minimizing the Euclidean distance between true gradients and those calculated from estimated data is often effective in fully recovering private data. However, there is a fundamental lack of theoretical understanding of how and when gradients can lead to unique recovery of original data. Our research fills this gap by providing a closed-form recursive procedure to recover data from gradients in deep neural networks. We demonstrate that gradient attacks consist of recursively solving a sequence of systems of linear equations. Furthermore, our closed-form approach works as well as or even better than optimization-based approaches at a fraction of the computation, we name it Recursive Gradient Attack on Privacy (R-GAP). Additionally, we propose a rank analysis method, which can be used to estimate a network architecture's risk of a gradient attack. Experimental results demonstrate the validity of the closed-form attack and rank analysis, while demonstrating its superior computational properties and lack of susceptibility to local optima vis a vis optimization-based attacks. Source code is available for download from https://github.com/JunyiZhu-AI/R-GAP.
Deep Learning in Ultrasound Elastography Imaging

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.
Effect of a dietary probiotic, Lactobacillus johnsonii BS15, on growth performance, quality traits, antioxidant ability, and nutritional and flavour substances of chicken meat

To determine whether Lactobacillus johnsonii BS15 supplementation improves the growth performance and meat quality of broilers, 450 1-day-old male chicks (Cobb 500) were randomly divided into three groups: control group (basal corn-soybean diet), L-BS15 group (basal diet + 1 × 105 colony-forming unit BS15/g as feed), and H-BS15 group (basal diet + 1 × 106 colony-forming unit BS15/g as feed). These diets were fed for 42 days. Abdominal fat decreased (P < 0.05) as the concentration of BS15 increased. BS15 supplementation significantly increased the pH at 24 h after sacrifice and decreased drip loss and shearing force (P < 0.05), although meat colour and pH at 40 min after sacrifice remained unchanged. Antioxidant capacity and total protein and intramuscular fat levels were unchanged by the treatments, but BS15 supplementation significantly increased inosine monophosphate level and decreased total cholesterol and triglyceride levels (P < 0.05). The levels of proline, total amino acids, and flavour-related amino acids were increased (P < 0.05) by BS15 supplementation, but the levels of other amino acids did not change significantly. BS15 supplementation significantly decreased (P < 0.05) C16:0, C22:4n-6, and n-6 : n-3 ratio and increased (P < 0.05) C18:3n-3, C20:5n-3,C22:6n-3, total polyunsaturated fatty acid (PUFA), n-3 PUFA and PUFA : saturated fatty acid ratio. These findings suggest that L. johnsonii BS15 supplementation improves the meat quality of broilers by increasing the shelf life and flavour and nutritional substances. In addition, BS15 supplementation can induce changes in fat deposits and fatty acid composition.
A Study in Frequency-dependent Effects on Precision Pulsar Timing Parameters with the Pulsar Signal Simulator

In this paper we introduce a new python package, the Pulsar Signal Simulator, or psrsigsim, which is designed to simulate a pulsar signal from emission at the pulsar, through the interstellar medium, to observation by a radio telescope, and digitization in a standard data format. We use psrsigsim to simulate observations of three millisecond pulsars, PSRs J1744–1134, B1855+09, and B1953+29, to explore the covariances between frequency-dependent parameters, such as variations in the dispersion measure (DM), pulse profile evolution with frequency, and pulse scatter broadening. We show that psrsigsim can produce realistic simulated data and can accurately recover the parameters injected into the data. We also find that while there are covariances when fitting DM variations and frequency-dependent parameters, they have little effect on timing precision. Our simulations also show that time-variable scattering delays decrease the accuracy and increase the variability of the recovered DM and frequency-dependent parameters. Despite this, our simulations also show that the time-variable scattering delays have little impact on the rms of the timing residuals. This suggests that the variability seen in recovered DMs, when time-variable scattering delays are present, is due to a covariance between the two parameters, with the DM modeling out the additional scattering delays.
CLASSIFICATION OF THE REPRODUCTIVE PATTERNS OF AMNIOTES

The classification of reproductive patterns inherited from the 19th century reflects mammalian biases and fails to capture our understanding of amniote reproductive diversity. Much more useful is a bipartite classification that fully distinguishes between two separate reproductive parameters: sources of nutrients for development, and state of the reproductive product at emer- gence from the mother. "Oviparity" and "viviparity" are used in their literal senses to refer to "egg-laying" and "live-bearing" reproduction. "Lecithotrophy" refers to provision of nutrients to the embryo via the yolk, and "matrotrophy" to provision by alternative means (e.g., a placenta, a pattern termed "placentotrophy"). Use of these terms has several advantages, including elimina- tion of confusion associated with the archaic term "ovoviviparity". This system also recognizes commonality between patterns involving maternal provision of nutrients before birth or hatching (e.g., to eggs and embryos) and afterwards (to neonates). Growing consensus around the bipartite classification of reproductive patterns reflects recognition of the importance of a common biolog- ical vocabulary that applies widely across traditional methodological, disciplinary, and taxonomic boundaries.
Local Differentially Private Regret Minimization in Reinforcement Learning

Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitive information that needs to be protected from third parties. Motivated by this, we study privacy in the context of finite-horizon Markov Decision Processes (MDPs) by requiring information to be obfuscated on the user side. We formulate this notion of privacy for RL by leveraging the local differential privacy (LDP) framework. We present an optimistic algorithm that simultaneously satisfies LDP requirements, and achieves sublinear regret. We also establish a lower bound for regret minimization in finite-horizon MDPs with LDP guarantees. These results show that while LDP is appealing in practical applications, the setting is inherently more complex. In particular, our results demonstrate that the cost of privacy is multiplicative when compared to non-private settings.
Scattering for radial bi-harmonic Hartree equations.

This note studies the asymptotic behavior of global solutions to the fourth-order generalized Hartree equation $$i\dot u+\Delta^2 u\pm(I_\alpha*|u|^p)|u|^{p-2}u=0. $$ Indeed, for both attractive and repulsive sign, the scattering is obtained in the mass super-critical and energy sub-critical regimes, with radial setting.
A Graph Neural Network based approach for detecting Suspicious Users on Online Social Media

Online Social Media platforms (such as Twitter and Facebook) are extensively used for spreading the news to a wider public effortlessly at a rapid pace. However, now a days these platforms are also used with an aim of spreading rumors and fake news to a large audience in a short time span that can cause panic, fear, and financial loss to society. Thus, it is important to detect and control these rumors before it spreads to the masses. One way to control the spread of these rumors is by identifying possible suspicious users who are often involved in spreading the rumors. Our basic assumption is that the users who are often involved in spreading rumors are more likely to be suspicious in contrast to the users whose involvement in spreading rumors are less. This is due to the fact that sometimes, users may posts the rumor tweets by accident. In this paper, we use PHEME rumor tweet dataset which contains rumor and non-rumor tweets information on five incidents, that is, i) Charlie hebdo, ii)German wings crash, iii)Ottawa shooting, iv)Sydney siege, and v)Ferguson. We transform this rumor tweets dataset into suspicious users dataset before leveraging Graph Neural Network (GNN) based approach for identifying suspicious users. Specifically, we explore Graph Convolutional Network (GCN),which is a type of GNN, for identifying suspicious users and then we compare GCN results with the other three approaches which act as baseline approaches: SVM, RF and LSTM based deep learning architecture. Extensive experiments performed on real-world dataset, where we achieve up to 0.864 value for F1-Score and 0.720 value for AUC ROC, shows the effectiveness of GNN based approach for identifying suspicious users.