Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact retail shelf segmentation for mobile deployment

The recent surge of automation in the retail industries has rapidly increased demand for applying deep learning models on mobile devices. To make the deep learning models real-time on-device, a compact efficient network becomes inevitable. In this paper, we work on one such common problem in the retail industries - Shelf segmentation. Shelf segmentation can be interpreted as a pixel-wise classification problem, i.e., each pixel is classified as to whether they belong to visible shelf edges or not. The aim is not just to segment shelf edges, but also to deploy the model on mobile devices. As there is no standard solution for such dense classification problem on mobile devices, we look at semantic segmentation architectures which can be deployed on edge. We modify low-footprint semantic segmentation architectures to perform shelf segmentation. In addressing this issue, we modified the famous U-net architecture in certain aspects to make it fit for on-devices without impacting significant drop in accuracy and also with 15X fewer parameters. In this paper, we proposed Light Weight Segmentation Network (LWSNet), a small compact model able to run fast on devices with limited memory and can train with less amount (~ 100 images) of labeled data.
How do online consumers review negatively?

Negative reviews on e-commerce platforms, mainly in the form of texts, are posted by online consumers to express complaints about unsatisfactory experiences, providing a proxy of big data for sellers to consider improvements. However, the exact knowledge that lies beyond the negative reviewing still remains unknown. Aimed at a systemic understanding of how online consumers post negative reviews, using 1, 450, 000 negative reviews from this http URL, the largest B2C platform in China, the behavioral patterns from temporal, perceptional and emotional perspectives are comprehensively explored in the present study. Massive consumers behind these reviews across four sectors in the most recent 10 years are further split into five levels to reveal group discriminations at a fine resolution. Circadian rhythms of negative reviewing after making purchases were found, and the periodic intervals suggest stable habits in online consumption and that consumers tend to negatively review at the same hour of the purchase. Consumers from lower levels express more intensive negative feelings, especially on product pricing and seller attitudes, while those from upper levels demonstrate a stronger momentum of negative emotion. The value of negative reviews from higher-level consumers is thus unexpectedly highlighted because of less emotionalization and less biased narration, while the longer-lasting characteristic of these consumers' negative responses also stresses the need for more attention from sellers. Our results shed light on implementing distinguished proactive strategies in different buyer groups to help mitigate the negative impact due to negative reviews.
Microscopic study of the coupled-wire construction and plausible realization in spin-dependent optical lattices

Coupled-wire constructions offer particularly simple and powerful models to capture the essence of strongly correlated topological phases of matter. They often rely on effective theories valid in the low-energy and strong coupling limits, which impose severe constraints on the physical systems where they could be realized. We investigate the microscopic relevance of a class of coupled-wire models and their possible experimental realization in cold-atom experiments. We connect with earlier results and prove the emergence of fractional quantum Hall states in the limit of strong inter-wire tunneling. Contrary to previous studies relying on renormalization group arguments, our microscopic approach exposes the connection between coupled-wire constructions and model wavefunctions in continuum Landau levels. Then, we use exact diagonalization methods to investigate the appearance of these fractional quantum Hall states in more realistic settings. We examine the parameter regimes where these strongly correlated phases arise, and provide a way to detect their appearance in cold-atom experiments through standard time-of-flight measurements. Motivated by this experimental probe, we finally propose a realization of our model with cold-atom in spin-dependent optical lattices. Our estimates show that the previous fractional quantum Hall phases lie within experimentally accessible parameter regimes, giving a viable route towards their experimental study.
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion

Knowledge graphs (KGs) contains an instance-level entity graph and an ontology-level concept graph. Recent studies reveal that jointly modeling of these two graphs could improve the understanding of each one. The completion processes on the concept graph and the entity graph can be further regarded as processes of knowledge abstraction and concretization. However, concept graphs in existing datasets are usually small and the links between concepts and entities are usually sparse, which cannot provide sufficient information for knowledge transfer between the two graphs. In this paper, we propose large-scale datasets extracted from Wikidata, which provide more size-balanced concept graphs and abundant cross-view links. Based on the datasets, we further propose a benchmark to test the ability of existing models on knowledge abstraction, concretization and completion (KACC). Our dataset is available at this https URL.
Parameter Estimation in a Noisy 1D Environment via Two Absorbing Receivers

This paper investigates the estimation of different parameters, e.g., propagation distance and flow velocity, by utilizing two fully-absorbing receivers (RXs) in a one-dimensional (1D) environment. The time-varying number of absorbed molecules at each RX and the number of absorbed molecules in a time interval as time approaches infinity are derived. Noisy molecules in this environment, that are released by sources in addition to the transmitter, are also considered. A novel estimation method, namely difference estimation (DE), is proposed to eliminate the effect of noise by using the difference of received signals at the two RXs. For DE, the Cramer-Rao lower bound (CRLB) on the variance of estimation is derived. Independent maximum likelihood estimation is also considered at each RX as a benchmark to show the performance advantage of DE. Aided by particle-based simulation, the derived analytical results are verified. Furthermore, numerical results show that DE attains the CRLB and is less sensitive to the change of noise than independent estimation at each RX.
Two-Stage Robust Edge Service Placement and Sizing Under Demand Uncertainty

Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable numerous Internet of Things applications. In this article, we study an optimal resource procurement problem for a service provider (SP), who can purchase resources from various edge nodes in the edge computing market to serve its users’ requests. How to jointly optimize the service placement, resource sizing, and workload allocation decisions is a challenging problem, which becomes even more complicated when considering demand uncertainty. To this end, we propose a novel two-stage adaptive robust optimization framework to help the SP optimally determine the locations for installing its service (i.e., placement) and the amount of computing resource to purchase from each location (i.e., sizing). The proposed placement and sizing solution can hedge against any possible realization within a predefined demand uncertainty set. Given the first-stage robust solution, the optimal resource and workload allocation decisions are computed in the second stage after the uncertainty is revealed. To solve the two-stage model, this article presents an iterative solution approach by employing the column-and-constraint generation method that decomposes the underlying problem into a master problem and a max–min subproblem associated with the second stage. Extensive numerical results are shown to illustrate the efficacy of the proposed model.
Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task

Human-computer interactive systems that rely on machine learning are becoming paramount to the lives of millions of people who use digital assistants on a daily basis. Yet, further advances are limited by the availability of data and the cost of acquiring new samples. One way to address this problem is by improving the sample efficiency of current approaches. As a solution path, we present a model-based reinforcement learning algorithm for an interactive dialogue task. We build on commonly used actor-critic methods, adding an environment model and planner that augments a learning agent to learn the model of the environment dynamics. Our results show that, on a simulation that mimics the interactive task, our algorithm requires 70 times fewer samples, compared to the baseline of commonly used model-free algorithm, and demonstrates 2~times better performance asymptotically. Moreover, we introduce a novel contribution of computing a soft planner policy and further updating a model-free policy yielding a less computationally expensive model-free agent as good as the model-based one. This model-based architecture serves as a foundation that can be extended to other human-computer interactive tasks allowing further advances in this direction.
BlockSim: An Extensible Simulation Tool for Blockchain Systems

Both in the design and deployment of blockchain solutions many performance-impacting configuration choices need to be made. We introduce BlockSim, a framework and software tool to build and simulate discrete-event dynamic systems models for blockchain systems. BlockSim is designed to support the analysis of a large variety of blockchains and blockchain deployments as well as a wide set of analysis questions. At the core of BlockSim is a Base Model, which contains the main model constructs common across various blockchain systems organized in three abstraction layers (network, consensus, and incentives layer). The Base Model is usable for a wide variety of blockchain systems and can be extended easily to include system or deployment particulars. The BlockSim software tool provides a simulator that implements the Base Model in Python. The paper describes the Base Model, the simulator implementation, and the application of BlockSim to Bitcoin, Ethereum and other consensus algorithms. We validate BlockSim simulation results by comparison with performance results from actual systems and from other studies in the literature. We close the paper by a BlockSim simulation study of the impact of uncle blocks rewards on mining decentralization, for a variety of blockchain configurations.
Correlated randomly growing graphs

We introduce a new model of correlated randomly growing graphs and study the fundamental questions of detecting correlation and estimating aspects of the correlated structure. The model is simple and starts with any model of randomly growing graphs, such as uniform attachment (UA) or preferential attachment (PA). Given such a model, a pair of graphs $(G_1, G_2)$ is grown in two stages: until time $t_{\star}$ they are grown together (i.e., $G_1 = G_2$), after which they grow independently according to the underlying growth model. We show that whenever the seed graph has an influence in the underlying graph growth model---this has been shown for PA and UA trees and is conjectured to hold broadly---then correlation can be detected in this model, even if the graphs are grown together for just a single time step. We also give a general sufficient condition (which holds for PA and UA trees) under which detection is possible with probability going to $1$ as $t_{\star} \to \infty$. Finally, we show for PA and UA trees that the amount of correlation, measured by $t_{\star}$, can be estimated with vanishing relative error as $t_{\star} \to \infty$.
Lagrangian geometry of matroids

We introduce the conormal fan of a matroid M, which is a Lagrangian analog of the Bergman fan of M. We use the conormal fan to give a Lagrangian interpretation of the Chern-Schwartz-MacPherson cycle of M. This allows us to express the h-vector of the broken circuit complex of M in terms of the intersection theory of the conormal fan of M. We also develop general tools for tropical Hodge theory to prove that the conormal fan satisfies Poincare duality, the hard Lefschetz theorem, and the Hodge-Riemann relations. The Lagrangian interpretation of the Chern-Schwartz-MacPherson cycle of M, when combined with the Hodge-Riemann relations for the conormal fan of M, implies Brylawski's and Dawson's conjectures that the h-vectors of the broken circuit complex and the independence complex of M are log-concave sequences.
A new radio catalogue for M83: supernova remnants and H ii regions

We present a new catalogue of radio sources in the face-on spiral galaxy M83. Radio observations taken in 2011, 2015, and 2017 with the Australia Telescope Compact Array at 5.5 and 9 GHz have detected 270 radio sources. Although a small number of these sources are background extragalactic sources, most are either H II regions or supernova remnants (SNRs) within M83 itself. Three of the six historical supernovae are detected, as is the very young remnant that had been identified in a recent study, which is likely the result of a supernova that exploded in the last ∼100 yr but was missed. All of these objects are generally fading with time. Confusion limits our ability to measure the radio emission from a number of the SNRs in M83, but 64 were detected in unconfused regions, and these have the approximate power-law luminosity function that has been observed in other galaxies. The SNRs in M83 are systematically smaller in diameter and brighter than those that have been detected at radio wavelengths in M33. A number of the radio sources are coincident with X-ray sources in M83; most of these coincident sources turn out to be SNRs. Our dual frequency observations are among the most sensitive to date for a spiral galaxy outside the Local Group; despite this we were not able to place realistic constraints on the spectral indices, and as a result, it was not possible to search for SNRs based on their radio properties alone.
Fast Algorithms for General Spin Systems on Bipartite Expanders

A spin system is a framework in which the vertices of a graph are assigned spins from a finite set. The interactions between neighbouring spins give rise to weights, so a spin assignment can also be viewed as a weighted graph homomorphism. The problem of approximating the partition function (the aggregate weight of spin assignments) or of sampling from the resulting probability distribution is typically intractable for general graphs. In this work, we consider arbitrary spin systems on bipartite expander Δ-regular graphs, including the canonical class of bipartite random Δ-regular graphs. We develop fast approximate sampling and counting algorithms for general spin systems whenever the degree and the spectral gap of the graph are sufficiently large. Roughly, this guarantees that the spin system is in the so-called low-temperature regime. Our approach generalises the techniques of Jenssen et al. and Chen et al. by showing that typical configurations on bipartite expanders correspond to “bicliques” of the spin system; then, using suitable polymer models, we show how to sample such configurations and approximate the partition function in Õ(n2) time, where n is the size of the graph.
Real and Reactive Power Based Optimal Privacy Protection in Smart Grid Demand Response

Frequent metering of consumption data is crucial for demand side management in smart grids. However, the metered data can easily be processed with nonintrusive appliance load monitoring techniques to infer appliance usage, which provides insight about the private lives of consumers. Existing load shaping techniques for privacy focus only on hiding or altering metered real power, whereas smart meters also collect reactive power data for various purposes. In this work, we present optimizing the consumer privacy in a demand response scheme considering both real and reactive power data. Also, we consider the user cost and comfort as objectives, and build the optimization problem in such a way that the effects of optimizing sub-objectives on the others can be observed. Results show that hiding only real or only reactive power is not sufficient for ensuring privacy and they need to be altered simultaneously. Shaping real and reactive demand at the same time results in more than twofold increase in privacy in terms of mutual information.
Adversarial Feature Learning and Unsupervised Clustering Based Speech Synthesis for Found Data With Acoustic and Textual Noise

Attention-based sequence-to-sequence (seq2seq) speech synthesis has achieved extraordinary performance. But a studio-quality corpus with manual transcription is necessary to train such seq2seq systems. In this letter, we propose an approach to build high-quality and stable seq2seq based speech synthesis system using challenging found data, where training speech contains noisy interferences (acoustic noise) and texts are imperfect speech recognition transcripts (textual noise). To deal with text-side noise, we propose a VQVAE based heuristic method to compensate erroneous linguistic feature with phonetic information learned directly from speech. As for the speech-side noise, we propose to learn a noise-independent feature in the auto-regressive decoder through adversarial training and data augmentation, which does not need an extra speech enhancement model. Experiments show the effectiveness of the proposed approach in dealing with text-side and speech-side noise. Surpassing the denoising approach based on a state-of-the-art speech enhancement model, our system built on noisy found data can synthesize clean and high-quality speech with MOS close to the system built on the clean counterpart.
Why Johnny can't rely on anti-phishing educational interventions to protect himself against contemporary phishing attacks?

Phishing is a way of stealing people's sensitive information such as username, password and banking details by disguising as a legitimate entity (i.e. email, website). Anti-phishing education considered to be vital in strengthening "human", the weakest link in information security. Previous research in anti-phishing education focuses on improving educational interventions to better interact the end user. However, one can argue that existing anti-phishing educational interventions are limited in success due to their outdated teaching content incorporated. Furthermore, teaching outdated anti-phishing techniques might not help combat contemporary phishing attacks. Therefore, this research focuses on investigating the obfuscation techniques of phishing URLs used in anti-phishing education against the contemporary phishing attacks reported in this http URL. Our results showed that URL obfuscation with IP address has become insignificant and it revealed two emerging URL obfuscation techniques, that attackers use lately, haven't been incorporated into existing anti-phishing educational interventions.
Suppression of pro-inflammatory signalling pathways by sulforaphane

Low-grade inflammation has been associated with the risk of chronic pathologies including cancer, atherosclerosis and type 2 diabetes. Epidemiological studies demonstrate an inverse correlation between diets rich in cruciferous vegetables and risk of cancer, cardiovascular disease mortality and circulating levels of pro-inflammatory cytokines. Sulforaphane (SF), an isothiocyanate obtained from broccoli, has many biological functions. The aim of this thesis was to test whether SF was able to suppress pro-inflammatory signalling using in vitro models of chronic inflammation. In human monocytes (PBMCs and THP-1 cells), physiologically relevant concentrations of SF significantly suppressed the production of LPS-induced cytokines IL-6, IL-1β and TNFα. The suppression was also observed with all genes induced by 1ng/ml LPS as measured by whole genome arrays. In addition, it was demonstrated that SF could directly interact with thiol groups of cysteine residues 609 and 246 within the LPS receptor, TLR4 under non-reducing conditions, to reduce the levels of inflammatory mediators that are produced in response to LPS. The anti-inflammatory effect of SF was not restricted to the TLR4 pathway, and significant reductions were observed in NF-κB activity induced in response to TLR2 and NOD2 ligands. These findings were translated into a more complex in vitro model, investigating the effect of SF on lipid accumulation within adipocytes and adipose tissue inflammation in response to macrophage-conditioned medium (MaCM) using human SGBS adipocytes. SF at 10μM significantly reduced levels of lipid accumulation within adipocytes and increased the expression of carnitine palmitoyltransferase-1A (CPT1A), suggesting a role in energy metabolism, a process often disrupted in obesity. In addition, a significant suppression in SGBS adipocyte IL-1β and IL-6 expression was observed when adipocytes were exposed to MaCM from SF-treated macrophages, when SF was used at a concentration as low as 2μM. This work demonstrates that concentrations of SF that could be achieved via reasonable broccoli consumption can suppress pro-inflammatory cytokine production, induced in response to a number of signalling pathways in addition to suppression of lipid accumulation and adipose tissue inflammation. The relevance of this data supports the concept that consumption of broccoli could lead to a reduction in the chronic inflammatory status in vivo as well as a suppression of lipid accumulation within adipocytes, to reduce the risk of developing chronic disease.
Pitfalls of learning a reward function online

In some agent designs like inverse reinforcement learning an agent needs to learn its own reward function. Learning the reward function and optimising for it are typically two different processes, usually performed at different stages. We consider a continual (``one life'') learning approach where the agent both learns the reward function and optimises for it at the same time. We show that this comes with a number of pitfalls, such as deliberately manipulating the learning process in one direction, refusing to learn, ``learning'' facts already known to the agent, and making decisions that are strictly dominated (for all relevant reward functions). We formally introduce two desirable properties: the first is `unriggability', which prevents the agent from steering the learning process in the direction of a reward function that is easier to optimise. The second is `uninfluenceability', whereby the reward-function learning process operates by learning facts about the environment. We show that an uninfluenceable process is automatically unriggable, and if the set of possible environments is sufficiently large, the converse is true too.