Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarization Guided Specular Reflection Separation

Since specular reflection often exists in the real captured images and causes deviation between the recorded color and intrinsic color, specular reflection separation can bring advantages to multiple applications that require consistent object surface appearance. However, due to the color of an object is significantly influenced by the color of the illumination, the existing researches still suffer from the near-duplicate challenge, that is, the separation becomes unstable when the illumination color is close to the surface color. In this paper, we derive a polarization guided model to incorporate the polarization information into a designed iteration optimization separation strategy to separate the specular reflection. Based on the analysis of polarization, we propose a polarization guided model to generate a polarization chromaticity image, which is able to reveal the geometrical profile of the input image in complex scenarios, e.g., diversity of illumination. The polarization chromaticity image can accurately cluster the pixels with similar diffuse color. We further use the specular separation of all these clusters as an implicit prior to ensure that the diffuse component will not be mistakenly separated as the specular component. With the polarization guided model, we reformulate the specular reflection separation into a unified optimization function which can be solved by the ADMM strategy. The specular reflection will be detected and separated jointly by RGB and polarimetric information. Both qualitative and quantitative experimental results have shown that our method can faithfully separate the specular reflection, especially in some challenging scenarios.
Fight Virus Like a Virus: A New Defense Method Against File-Encrypting Ransomware

Nowadays ransomware has become a new profitable form of attack. This type of malware acts as a form of extortion which encrypts the files in a victim's computer and forces the victim to pay the ransom to have the data recovered. Even companies and tech savvy people must use extensive resources to maintain backups for recovery or else they will lose valuable data, not mentioning average users. Unfortunately, not any recovery tool can effectively defend various types of ransomware. To address this challenge, we propose a novel ransomware defense mechanism that can be easily deployed in modern Windows system to recover the data and mitigate a ransomware attack. The uniqueness of our approach is to fight the virus like a virus. We leverage Alternative Data Streams which are sometimes used by malicious applications, to develop a data protection method that misleads the ransomware to attack only file 'shells' instead of the actual file content. We evaluated different file encrypting ransomware and demonstrate usability, efficiency and effectiveness of our approach.
Attention-Based Multimodal Image Matching

We propose an attention-based approach for multimodal image patch matching using a Transformer encoder attending to the feature maps of a multiscale Siamese CNN. Our encoder is shown to efficiently aggregate multiscale image embeddings while emphasizing task-specific appearance-invariant image cues. We also introduce an attention-residual architecture, using a residual connection bypassing the encoder. This additional learning signal facilitates end-to-end training from scratch. Our approach is experimentally shown to achieve new state-of-the-art accuracy on both multimodal and single modality benchmarks, illustrating its general applicability. To the best of our knowledge, this is the first successful implementation of the Transformer encoder architecture to the multimodal image patch matching task.
Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning

Online hate is a growing concern on many social media platforms and other sites. To combat it, technology companies are increasingly identifying and sanctioning `hateful users' rather than simply moderating hateful content. Yet, most research in online hate detection to date has focused on hateful content. This paper examines how fairer and more accurate hateful user detection systems can be developed by incorporating social network information through geometric deep learning. Geometric deep learning dynamically learns information-rich network representations and can generalise to unseen nodes. This is essential for moving beyond manually engineered network features, which lack scalability and produce information-sparse network representations. This paper compares the accuracy of geometric deep learning with other techniques which either exclude network information or incorporate it through manual feature engineering (e.g., node2vec). It also evaluates the fairness of these techniques using the `predictive equality' criteria, comparing the false positive rates on a subset of 136 African-American users with 4836 other users. Geometric deep learning produces the most accurate and fairest classifier, with an AUC score of 90.8\% on the entire dataset and a false positive rate of zero among the African-American subset for the best performing model. This highlights the benefits of more effectively incorporating social network features in automated hateful user detection. Such an approach is also easily operationalized for real-world content moderation as it has an efficient and scalable design.
Historic wandering domains near cycles

We explain how to obtain non-trivial historic contractive wandering domains for a dense set of diffeomorphisms in certain type of C r -Newhouse domains of homoclinic tangencies in dimension d ⩾ 3 and r ⩾ 1. In particular, this gives for the first time a contribution to Takens’ last problem in the C 1 topology and in dimension d > 2. We show how these Newhouse domains can be obtained arbitrarily close to diffeomorphisms exhibiting heterodimensional cycles (in dimension d = 3) or non-transverse equidimensional cycles (in any dimension d ⩾ 3) associated with periodic points with non-real complex leading multipliers.
A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning

With the deployment of smart sensors and advancements in communication technologies, big data analytics have become vastly popular in the smart grid domain, informing stakeholders of the best power utilization strategy. However, these power-related data are stored and owned by different parties. For example, power consumption data are stored in numerous transformer stations across cities; mobility data of the population, which are important indicators of power consumption, are held by mobile companies. Direct data sharing might compromise party benefits, individual privacy and even national security. Inspired by the federated learning scheme from Google AI, we propose a federated learning framework for smart grids, which enables collaborative learning of power consumption patterns without leaking individual power traces. Horizontal federated learning is employed when data are scattered in the sample space; vertical federated learning, on the other hand, is designed for the case with data scattered in the feature space. Case studies show that, with proper encryption schemes such as Paillier encryption, the machine learning models constructed from the proposed framework are lossless, privacy-preserving and effective. Finally, the promising future of federated learning in other facets of the smart grid is discussed, including electric vehicles, distributed generation/consumption and integrated energy systems.
Projectors in the Virtual Temperley-Lieb Algebra

We present a method of defining projectors in the virtual Temperley-Lieb algebra, that generalizes the Jones-Wenzl projectors in Temperley-Lieb algebra. We show that the projectors have similar properties with the Jones-Wenzl projectors, and contain an extra property which is associated with the virtual generator elements, that is, the product of a projector with a virtual generator is unchanged. We also show the uniqueness of the projector fn in terms of its axiomatic properties in the virtual Temperley-Lieba algebra V TLn(d). Finally, we find the coefficients of fn and give an explicit formula for the projector fn. keywords : virtual Temperley-Lieb algebra; projector; recurrence formula; coefficient. Corresponding author
Light-curve properties of SN 2017fgc and HV SNe Ia

Photometric and spectroscopic observations of type Ia supernova (SN) 2017fgc which cover the period from −12 to +137 days since the B-band maximum are presented. SN 2017fgc is a photometrically normal SN Ia with the luminosity decline rate, ∆m15(B)true= 1.10 ± 0.10 mag. Spectroscopically, it belongs to the High Velocity (HV) SNe Ia group, with the Si II λ6355 velocity near the B-band maximum estimated to be 15,200 ± 480 km s−1. At the epochs around the near-infrared secondary peak, the R and I bands show an excess of ∼0.2 mag level compared to the light curves of the normal velocity (NV) SNe Ia. Further inspection of the samples of HV and NV SNe Ia indicates that the excess is a generic feature among HV SNe Ia, different from NV SNe Ia. There is also a hint that the excess is seen in the V band, both in SN 2017fgc and other HV SNe Ia, which behaves like a less prominent shoulder in the light curve. The excess is not obvious in the B band (and unknown in the U band), and the color is consistent with the fiducial SN color. This might indicate the excess is attributed to the bolometric luminosity, not in the color. This excess is less likely caused by external effects, like an echo or change in reddening but could be due to an ionization effect, which reflects an intrinsic, either distinct or continuous, difference in the ejecta properties between HV and NV SNe Ia.
Responsible AI: Gender bias assessment in emotion recognition

Rapid development of artificial intelligence (AI) systems amplify many concerns in society. These AI algorithms inherit different biases from humans due to mysterious operational flow and because of that it is becoming adverse in usage. As a result, researchers have started to address the issue by investigating deeper in the direction towards Responsible and Explainable AI. Among variety of applications of AI, facial expression recognition might not be the most important one, yet is considered as a valuable part of human-AI interaction. Evolution of facial expression recognition from the feature based methods to deep learning drastically improve quality of such algorithms. This research work aims to study a gender bias in deep learning methods for facial expression recognition by investigating six distinct neural networks, training them, and further analysed on the presence of bias, according to the three definition of fairness. The main outcomes show which models are gender biased, which are not and how gender of subject affects its emotion recognition. More biased neural networks show bigger accuracy gap in emotion recognition between male and female test sets. Furthermore, this trend keeps for true positive and false positive rates. In addition, due to the nature of the research, we can observe which types of emotions are better classified for men and which for women. Since the topic of biases in facial expression recognition is not well studied, a spectrum of continuation of this research is truly extensive, and may comprise detail analysis of state-of-the-art methods, as well as targeting other biases.
SSD: A Unified Framework for Self-Supervised Outlier Detection

We ask the following question: what training information is required to design an effective outlier/out-of-distribution (OOD) detector, i.e., detecting samples that lie far away from the training distribution? Since unlabeled data is easily accessible for many applications, the most compelling approach is to develop detectors based on only unlabeled in-distribution data. However, we observe that most existing detectors based on unlabeled data perform poorly, often equivalent to a random prediction. In contrast, existing state-of-the-art OOD detectors achieve impressive performance but require access to fine-grained data labels for supervised training. We propose SSD, an outlier detector based on only unlabeled in-distribution data. We use self-supervised representation learning followed by a Mahalanobis distance based detection in the feature space. We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin. Additionally, SSD even achieves performance on par, and sometimes even better, with supervised training based detectors. Finally, we expand our detection framework with two key extensions. First, we formulate few-shot OOD detection, in which the detector has access to only one to five samples from each class of the targeted OOD dataset. Second, we extend our framework to incorporate training data labels, if available. We find that our novel detection framework based on SSD displays enhanced performance with these extensions, and achieves state-of-the-art performance. Our code is publicly available at https://github.com/inspire-group/SSD.
Hidden qubit cluster states

Continuous-variable cluster states (CVCSs) can be supplemented with Gottesman-Kitaev-Preskill (GKP) states to form a hybrid cluster state with the power to execute universal, fault-tolerant quantum computing in a measurement-based fashion. As the resource states that comprise a hybrid cluster state are of a very different nature, a natural question arises: Why do GKP states interface so well with CVCSs? To answer this question, we apply the recently introduced subsystem decomposition of a bosonic mode, which divides a mode into logical and gauge-mode subsystems, to three types of cluster state: CVCSs, GKP cluster states, and hybrid continuous-variable (CV)-GKP cluster states. We find that each of these contains a “hidden” qubit cluster state across their logical subsystems, which lies at the heart of their utility for measurement-based quantum computing. To complement the analytical approach, we introduce a simple graphical description of these CV-mode cluster states that depicts precisely how the hidden qubit cluster states are entangled with the gauge modes, and we outline how these results would extend to the case of finitely squeezed states. This work provides important insight that is both conceptually satisfying and helps to address important practical issues like when a simpler resource (such as a Gaussian state) can stand in for a more complex one (like a GKP state), leading to more efficient use of the resources available for CV quantum computing.
Graph Signal Processing: A Signal Representation Approach to Convolution and Sampling

—The paper presents sampling in GSP as 1) linear operations (change of bases) between signal representations and 2) downsampling as linear shift invariant ﬁltering and reconstruction (interpolation) as ﬁltering, both in the spectral domain. To achieve this, it considers a spectral shift 𝑀 that leads to a spectral graph signal processing theory, GSP sp , dual to GSP but that starts from the spectral domain and 𝑀 . The paper introduces alternative signal representations, convolution of graph signals for these alternative representations, presenting a fast GSP convolution that uses the DSP FFT algorithm, and sampling as solutions of algebraic linear systems of equations.
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus

Word embeddings are a basic building block of modern NLP pipelines. Efforts have been made to learn rich, efficient, and interpretable embeddings for large generic datasets available in the public domain. However, these embeddings have limited applicability for small corpora from specific domains such as automotive, manufacturing, maintenance and support, etc. In this work, we present a comprehensive notion of interpretability for word embeddings and propose a novel method to generate highly interpretable and efficient embeddings for a domain-specific small corpus. We report the evaluation results of our resulting word embeddings and demonstrate their novel features for enhanced interpretability.
Multi-Agent Algorithms for Collective Behavior: A structural and application-focused atlas

The goal of this paper is to provide a survey and application-focused atlas of collective behavior coordination algorithms for multi-agent systems. We survey the general family of collective behavior algorithms for multi-agent systems and classify them according to their underlying mathematical structure. In doing so, we aim to capture fundamental mathematical properties of algorithms (e.g., scalability with respect to the number of agents and bandwidth use) and to show how the same algorithm or family of algorithms can be used for multiple tasks and applications. Collectively, this paper provides an application-focused atlas of algorithms for collective behavior of multi-agent systems, with three objectives: 1. to act as a tutorial guide to practitioners in the selection of coordination algorithms for a given application; 2. to highlight how mathematically similar algorithms can be used for a variety of tasks, ranging from low-level control to high-level coordination; 3. to explore the state-of-the-art in the field of control of multi-agent systems and identify areas for future research.
Token-wise Curriculum Learning for Neural Machine Translation

Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of"easy"samples from training data at the early training stage. This is not always achievable for low-resource languages where the amount of training data is limited. To address such limitation, we propose a novel token-wise curriculum learning approach that creates sufficient amounts of easy samples. Specifically, the model learns to predict a short sub-sequence from the beginning part of each target sentence at the early stage of training, and then the sub-sequence is gradually expanded as the training progresses. Such a new curriculum design is inspired by the cumulative effect of translation errors, which makes the latter tokens more difficult to predict than the beginning ones. Extensive experiments show that our approach can consistently outperform baselines on 5 language pairs, especially for low-resource languages. Combining our approach with sentence-level methods further improves the performance on high-resource languages.
A Survey of Hand Crafted and Deep Learning Methods for Image Aesthetic Assessment

—Automatic image aesthetics assessment is a computer vision problem that deals with the categorization of images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adhere to the key principles of photography (balance, rhythm, harmony, contrast, unity, look, feel, tone and texture). Owing to its diverse applications in many areas, automatic image aesthetic assessment has gained signiﬁcant research attention in recent years. This paper presents a literature review of the recent techniques of automatic image aesthetics assessment. A large number of traditional hand crafted and deep learning based approaches are reviewed. Key problem aspects are discussed such as why some features or models perform better than others and what are the limitations. A comparison of the quantitative results of different methods is also provided at the end.
On the Mattila–Sjölin distance theorem for product sets

Let A be a compact set in R$\mathbb {R}$ , and E=Ad⊂Rd$E=A^d\subset \mathbb {R}^d$ . We know from the Mattila–Sjölin's theorem if dimH(A)>d+12d$\dim _H(A)>\frac{d+1}{2d}$ , then the distance set Δ(E)$\Delta (E)$ has non‐empty interior. In this paper, we show that the threshold d+12d$\frac{d+1}{2d}$ can be improved whenever d⩾5$d\geqslant 5$ .
Luminescent Waveguides In-situ Integrated with Organic Solar Cells for Internet of Things

Transparent electronics offer exciting light harvesting solutions for generation of electrical power via demonstration of ‘see-through’ optoelectronic devices. The wireless energy harvesting ability can empower unplugged and battery-free operations for Internet of Things (IoT) devices. In this study, we report a transparent, luminescent, and elastomeric optical waveguide incorporating quantum dots that is in-situ coupled with organic solar cell array made of P3HT:PC61BM bulk heterojunction. CdSe@ZnS QDs have a photoluminescence quantum yield (PLQY) of 91% and are synthetically engineered to match their photoluminescence spectra with the photo-response of P3HT:PC61BM solar cells for efficient energy harvesting. Integrated devices can generate sufficient power for the active radio frequency identification (RFID) tags to send signals in the communication distance of 35 meters at low illumination level of 0.1-sun and ~0.1 km under 1-sun condition, respectively, which is sufficient for indoor and outdoor communications. Advantageously, the combination of organic solar cells with the waveguide during the elastomer curing leads to the elimination of the undesired post-fabrication processes such as alignment of the solar cells with waveguide and gluing the separate parts with curable polymers. This study paves the way toward using luminescence as transparent, efficient, and configurable energy harvesting solutions for IoT applications.
MAORY: A Multi-conjugate Adaptive Optics RelaY for ELT

MAORY is designed to support two different instruments, each with the same optical quality and with a gravity-invariant port. One of these two instruments will be the Multi-adaptive optics Imaging CamerA for Deep Observations (MICADO) near-infrared camera (Davies et al., 2018), while the second one is as yet undefined. The SCAO module is being developed within the MICADO consortium with contributions from MAORY and is described in Davies et al. (p. 17). The MAORY project is now in its Phase B stage and is progressing towards its Preliminary Design Review in early 2021.
Observation of heat scaling across a first-order quantum phase transition in a spinor condensate

Heat generated as a result of the breakdown of an adiabatic process is one of the central concepts of thermodynamics. In isolated systems, the heat can be defined as an energy increase due to transitions between distinct energy levels. Across a second-order quantum phase transition (QPT), the heat is predicted theoretically to exhibit a power-law scaling, but it is a significant challenge for an experimental observation. In addition, it remains elusive whether a power-law scaling of heat can exist for a first-order QPT. Here we experimentally observe a power-law scaling of heat in a spinor condensate when a system is linearly driven from a polar phase to an antiferromagnetic (AFM) phase across a first-order QPT. We experimentally evaluate the heat generated during two non-equilibrium processes by probing the atom number on a hyperfine energy level. The experimentally measured scaling exponents agree well with our numerical simulation results. Our work therefore opens a new avenue to experimentally and theoretically exploring the properties of heat in non-equilibrium dynamics.