Miracle-dz/UniFAR-FTUs huggingface dataset:
Note on interacting holographic dark energy with a Hubble-scale cutoff

Holographic dark energy with the Hubble radius as infrared cutoff has been considered as a candidate to explain the late-time cosmic acceleration and it can solve the coincidence problem. In this scenario, a non-zero equation of state is only possible if there is an interaction between dark energy and cold dark matter. In this paper, a set of phenomenological interactions is assumed and a detailed analysis of the possible values of the coupling constants is carried out, however the resulting matter power spectrum and cosmic microwave background temperature and polarization power spectra have a shape very far from the observed ones. These results rule out any value for the free parameters and it seems to indicate that the assumed interacting holographic dark energy with a Hubble-scale cutoff is not viable to explain the accelerated expansion of the Universe, when cosmological data are taken into account.
KE-RCNN: Unifying Knowledge-Based Reasoning Into Part-Level Attribute Parsing

Part-level attribute parsing is a fundamental but challenging task, which requires the region-level visual understanding to provide explainable details of body parts. Most existing approaches address this problem by adding a regional convolutional neural network (RCNN) with an attribute prediction head to a two-stage detector, in which attributes of body parts are identified from localwise part boxes. However, localwise part boxes with limit visual clues (i.e., part appearance only) lead to unsatisfying parsing results, since attributes of body parts are highly dependent on comprehensive relations among them. In this article, we propose a knowledge-embedded RCNN (KE-RCNN) to identify attributes by leveraging rich knowledge, including implicit knowledge (e.g., the attribute “above-the-hip” for a shirt requires visual/geometry relations of shirt-hip) and explicit knowledge (e.g., the part of “shorts” cannot have the attribute of “hoodie” or “lining”). Specifically, the KE-RCNN consists of two novel components, that is: 1) implicit knowledge-based encoder (IK-En) and 2) explicit knowledge-based decoder (EK-De). The former is designed to enhance part-level representation by encoding part–part relational contexts into part boxes, and the latter one is proposed to decode attributes with a guidance of prior knowledge about part–attribute relations. In this way, the KE-RCNN is plug-and-play, which can be integrated into any two-stage detectors, for example, Attribute-RCNN, Cascade-RCNN, HRNet-based RCNN, and SwinTransformer-based RCNN. Extensive experiments conducted on two challenging benchmarks, for example, Fashionpedia and Kinetics-TPS, demonstrate the effectiveness and generalizability of the KE-RCNN. In particular, it achieves higher improvements over all existing methods, reaching around 3% of ${\mathrm{ AP}}^{\mathrm{ all}}_{\rm IoU+F_{1}}$ on Fashionpedia and around 4% of ${\mathrm{ Acc}}_{p}$ on Kinetics-TPS. Code and models are publicly available at: https://github.com/sota-joson/KE-RCNN.
Critical Investigation of Failure Modes in Physics-informed Neural Networks

Several recent works in scientific machine learning have revived interest in the application of neural networks to partial differential equations (PDEs). A popular approach is to aggregate the residual form of the governing PDE and its boundary conditions as soft penalties into a composite objective/loss function for training neural networks, which is commonly referred to as physics-informed neural networks (PINNs). In the present study, we visualize the loss landscapes and distributions of learned parameters and explain the ways this particular formulation of the objective function may hinder or even prevent convergence when dealing with challenging target solutions. We construct a purely data-driven loss function composed of both the boundary loss and the domain loss. Using this data-driven loss function and, separately, a physics-informed loss function, we then train two neural network models with the same architecture. We show that incomparable scales between boundary and domain loss terms are the culprit behind the poor performance. Additionally, we assess the performance of both approaches on two elliptic problems with increasingly complex target solutions. Based on our analysis of their loss landscapes and learned parameter distributions, we observe that a physics-informed neural network with a composite objective function formulation produces highly non-convex loss surfaces that are difficult to optimize and are more prone to the problem of vanishing gradients.
One U (1) to Rule them All: In the Realm of Leptoquarks, the Axion Shines

In this paper, axion models supplemented by leptoquarks and diquarks are systematically analyzed. Turning on some couplings to and among these latter states permits to unify the PQ symmetry with baryon ( B ) and lepton ( L ) numbers, such that the axion becomes associated to the spontaneous breaking of the three U (1) symmetries. All possible four- and six-fermion patterns of B and L violation are discussed, including those inducing proton decay, with ∆ B = 1 and ∆ L = ± 1 , ± 3, neutron-antineutron oscillations with ∆ B = 2, and Majorana neutrino masses with ∆ L = 2. Scenarios in which one or two axion ﬁelds necessarily appear in any B and/or L violating operators are also constructed. Nucleon decays would then necessarily involve an axion in the ﬁnal state, while neutron-antineutron oscillations would only happen in an axionic background. This could have implications for the neutron lifetime puzzle, and more generally, opens the door to new phenomenological and cosmological applications.
µBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem

Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus revenue on their platforms. In this paper, we are presenting our solution to Multilingual Abusive Comment Identification Challenge on Moj, an Indian video-sharing social networking service, powered by ShareChat and IIIT- Delhi for the 2nd Workshop on Emerging Advances in Multimodal AI (EAM) at IEEE BigMM, 2021. The challenge dealt with detecting abusive comments, in 13 regional Indic languages, on the videos on Moj platform. Our solution utilizes the novel µBoost, an ensemble of CatBoost classifier models and Multilingual Representations for Indian Languages (MURIL) model, to produce SOTA performance on Indic text classification tasks. We were able to achieve a mean F1-score of 89.286 on the test data, an improvement over baseline MURIL model with a F1-score of 87.48.
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems

Hazard and Operability Analysis (HAZOP) is a powerful safety analysis technique with a long history in industrial process control domain. With the increasing use of Machine Learning (ML) components in cyber physical systems--so called Learning-Enabled Systems (LESs), there is a recent trend of applying HAZOP-like analysis to LESs. While it shows a great potential to reserve the capability of doing sufficient and systematic safety analysis, there are new technical challenges raised by the novel characteristics of ML that require retrofit of the conventional HAZOP technique. In this regard, we present a new Hierarchical HAZOP-Like method for LESs (HILLS). To deal with the complexity of LESs, HILLS first does"divide and conquer"by stratifying the whole system into three levels, and then proceeds HAZOP on each level to identify (latent-)hazards, causes, security threats and mitigation (with new nodes and guide words). Finally, HILLS attempts at linking and propagating the causal relationship among those identified elements within and across the three levels via both qualitative and quantitative methods. We examine and illustrate the utility of HILLS by a case study on Autonomous Underwater Vehicles, with discussions on assumptions and extensions to real-world applications. HILLS, as a first HAZOP-like attempt on LESs that explicitly considers ML internal behaviours and its interactions with other components, not only uncovers the inherent difficulties of doing safety analysis for LESs, but also demonstrates a good potential to tackle them.
Fully Privacy-Preserving Federated Representation Learning via Secure Embedding Aggregation

We consider a federated representation learning framework, where with the assistance of a central server, a group ofN distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e.g., users in a social network). Under this framework, for the key step of aggregating local embeddings trained at the clients in a private manner, we develop a secure embedding aggregation protocol named SecEA, which provides information-theoretical privacy guarantees for the set of entities and the corresponding embeddings at each client simultaneously, against a curious server and up to T < N/2 colluding clients. As the first step of SecEA, the federated learning system performs a private entity union, for each client to learn all the entities in the system without knowing which entities belong to which clients. In each aggregation round, the local embeddings are secretly shared among the clients using Lagrange interpolation, and then each client constructs coded queries to retrieve the aggregated embeddings for the intended entities. We perform comprehensive experiments on various representation learning tasks to evaluate the utility and efficiency of SecEA, and empirically demonstrate that compared with embedding aggregation protocols without (or with weaker) privacy guarantees, SecEA incurs negligible performance loss (within 5%); and the additional computation latency of SecEA diminishes for training deeper models on larger datasets.
Fine tuning of transmission features in nanoporous anodic alumina distributed Bragg reflectors

This study introduces an innovative apodisation strategy to tune the filtering features of distributed Bragg reflectors based on nanoporous anodic alumina (NAA-DBRs). The effective medium of NAA-DBRs, which is modulated in a stepwise fashion by a pulse-like anodisation approach, is apodised following a logarithmic negative function to engineer the transmission features of NAA-DBRs. We investigate the effect of various apodisation parameters such as apodisation amplitude difference, anodisation period, current density offset and pore widening time, to tune and optimise the optical properties of NAA-DBRs in terms of central wavelength position, full width at half maximum and quality of photonic stop band. The transmission features of NAA-DBRs are shown to be fully controllable with precision across the spectral regions by means of the apodisation parameters. Our study demonstrates that an apodisation strategy can significantly narrow the width and enhance the quality of the characteristic photonic stop band of NAA-DBRs. This rationally designed anodisation approach based on the combination of apodisation and stepwise pulse anodisation enables the development of optical filters with tuneable filtering features to be integrated into optical technologies acting as essential photonic elements in devices such as optical sensors and biosensors.
Efficacy of Asynchronous GPS Spoofing Against High Volume Consumer GNSS Receivers

The vulnerability of the Global Positioning System (GPS) against spoofing is known for quite some time. Also, the positioning and navigation of most semi-autonomous and autonomous drones are dependent on Global Navigation Satellite System (GNSS) signals. In prior work, simplistic or asynchronous GPS spoofing was found to be a simple, efficient, and effective cyber attack against L1 GPS or GNSS dependent commercial drones. In this paper, first we make some important observations on asynchronous GPS spoofing attacks on drones presented in prior research literature. Then, we design an asynchronous GPS spoofing attack plan. Next, we test the effectiveness of this attack against GNSS receivers (high volume consumer devices based on Android mobile phones) of different capabilities and a commercial drone (DJI Mavic 2 Pro) under various conditions. Finally, we present several novel insights based on the results of the tests.
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction

Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and separate components, DSI uses a single transformer model to perform both indexing and retrieval. In this paper, we identify and tackle an important issue of current DSI models: the data distribution mismatch that occurs between the DSI indexing and retrieval processes. Specifically, we argue that, at indexing, current DSI methods learn to build connections between the text of long documents and the identifier of the documents, but then retrieval of document identifiers is based on queries that are commonly much shorter than the indexed documents. This problem is further exacerbated when using DSI for cross-lingual retrieval, where document text and query text are in different languages. To address this fundamental problem of current DSI models, we propose a simple yet effective indexing framework for DSI, called DSI-QG. When indexing, DSI-QG represents documents with a number of potentially relevant queries generated by a query generation model and re-ranked and filtered by a cross-encoder ranker. The presence of these queries at indexing allows the DSI models to connect a document identifier to a set of queries, hence mitigating data distribution mismatches present between the indexing and the retrieval phases. Empirical results on popular mono-lingual and cross-lingual passage retrieval datasets show that DSI-QG significantly outperforms the original DSI model.
Hybridization of Evolutionary Algorithm and Deep Reinforcement Learning for Multiobjective Orienteering Optimization

Multiobjective orienteering problems (MO-OPs) are classical multiobjective routing problems and have received much attention in recent decades. This study seeks to solve MO-OPs through a problem-decomposition framework, that is, an MO-OP is decomposed into a multiobjective knapsack problem (MOKP) and a traveling salesman problem (TSP). The MOKP and TSP are then solved by a multiobjective evolutionary algorithm (MOEA) and a deep reinforcement learning (DRL) method, respectively. While the MOEA module is for selecting cities, the DRL module is for planning a Hamiltonian path for these cities. An iterative use of these two modules drives the population toward the Pareto front of MO-OPs. The effectiveness of the proposed method is compared against NSGA-II and NSGA-III on various types of MO-OP instances. Experimental results show that our method performs best on almost all the test instances and has shown strong generalization ability.
Quantum teleportation with relativistic communication from first principles

In this work we provide a genuine relativistic quantum teleportation protocol whose classical communication component makes use of relativistic causal propagation of a quantum field. Consequently, the quantum teleportation is fully relativistic by construction. Our scheme is based on Unruh-DeWitt qubit detector model, where the quantum state being teleported is associated to an actual qubit rather than a field mode considered by Alsing and Milburn [PRL 91, 180404 (2003)]. We show that the existing works in (relativistic) quantum information, including good definitions of one-shot and asymptotic channel capacities, as well as algebraic formulation of quantum field theory, already provide us with all the necessary ingredients to construct fundamentally relativistic teleportation protocol in relatively straightforward manner.
Inferring oscillator’s phase and amplitude response from a scalar signal exploiting test stimulation

The phase sensitivity curve or phase response curve (PRC) quantifies the oscillator’s reaction to stimulation at a specific phase and is a primary characteristic of a self-sustained oscillatory unit. Knowledge of this curve yields a phase dynamics description of the oscillator for arbitrary weak forcing. Similar, though much less studied characteristic, is the amplitude response that can be defined either using an ad hoc approach to amplitude estimation or via the isostable variables. Here, we discuss the problem of the phase and amplitude response inference from observations using test stimulation. Although PRC determination for noise-free neuronal-like oscillators perturbed by narrow pulses is a well-known task, the general case remains a challenging problem. Even more challenging is the inference of the amplitude response. This characteristic is crucial, e.g. for controlling the amplitude of the collective mode in a network of interacting units—a task relevant to neuroscience. Here, we compare the performance of different techniques suitable for inferring the phase and amplitude response, particularly with application to macroscopic oscillators. We suggest improvements to these techniques, e.g. demonstrating how to obtain the PRC in case of stimuli of arbitrary shape. Our main result is a novel technique denoted by IPID-1, based on the direct reconstruction of the Winfree equation and the analogous first-order equation for isostable dynamics. The technique works for signals with or without well-pronounced marker events and pulses of arbitrary shape; in particular, we consider charge-balanced pulses typical in neuroscience applications. Moreover, this technique is superior for noisy and high-dimensional systems. Additionally, we describe an error measure that can be computed solely from data and complements any inference technique.
De-biasing Particle Filtering for a Continuous Time Hidden Markov Model With a Cox Process Observation Model

We develop a (nearly) unbiased particle filtering algorithm for a specific class of continuous-time state-space models, such that (a) the latent process $X_t$ is a linear Gaussian diffusion; and (b) the observations arise from a Poisson process with intensity $\lambda(X_t)$. The likelihood of the posterior probability density function of the latent process includes an intractable path integral. Our algorithm relies on Poisson estimates which approximate unbiasedly this integral. We show how we can tune these Poisson estimates to ensure that, with large probability, all but a few of the estimates generated by the algorithm are positive. Then replacing the negative estimates by zero leads to a much smaller bias than what would obtain through discretisation. We quantify the probability of negative estimates for certain special cases and show that our particle filter is effectively unbiased. We apply our method to a challenging 3D single molecule tracking example with a Born and Wolf observation model.
Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection

As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely available under permissive licenses, and are accessible enough to enable even small teams to be very productive. However within the research community, awareness and usage of said tools is not necessarily widespread, and researchers may be missing out on potential productivity gains from exploiting the latest tools and workflows. This paper presents a case study where we discuss our recent experience producing an end-to-end artificial intelligence application for industrial defect detection. We detail the high level deep learning libraries, containerized workflows, continuous integration/deployment pipelines, and open source code templates we leveraged to produce a competitive result, matching the performance of other ranked solutions to our three target datasets. We highlight the value that exploiting such systems can bring, even for research, and detail our solution and present our best results in terms of accuracy and inference time on a server class GPU, as well as inference times on a server class CPU, and a Raspberry Pi 4.
Mapping renormalized coupled cluster methods to quantum computers through a compact unitary representation of nonunitary operators

Non-unitary theories are commonly seen in the classical simulations of quantum systems. Among these theories, the method of moments of coupled-cluster equations (MMCCs) and the ensuing classes of the renormalized coupled-cluster (CC) approaches have evolved into one of the most accurate approaches to describe correlation effects in various quantum systems. The MMCC formalism provides an effective way for correcting energies of approximate CC formulations (parent theories) using moments, or CC equations, that are not used to determine approximate cluster amplitudes. In this paper, we propose a quantum algorithm for computing MMCC ground-state energies that provide two main advantages over classical computing or other quantum algorithms: (i) the possibility of forming superpositions of CC moments of arbitrary ranks in the entire Hilbert space and using an arbitrary form of the parent cluster operator for MMCC expansion; and (ii) significant reduction in the number of measurements in quantum simulation through a compact unitary representation for a generally non-unitary operator. We illustrate the robustness of our approach over a broad class of test cases, including ~40 molecular systems with varying basis sets encoded using 4~40 qubits, and exhibit the detailed MMCC analysis for the 8-qubit half-filled, four-site, single impurity Anderson model and 12-qubit hydrogen fluoride molecular system from the corresponding noise-free and noisy MMCC quantum computations. We also outline the extension of MMCC formalism to the case of unitary CC wave function ansatz.