Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Long-term Optical Variability Properties of γ-Ray-bright Blazars

Optical observations of a sample of 12 γ-ray-bright blazars from four optical data archives—American Association of Variable Star Observers, Small and Moderate Aperture Research Telescope System, Catalina, and Steward Observatory—are compiled to create densely sampled light curves spanning more than a decade. As a part of the blazar multiwavelength studies, several methods of analysis, e.g., flux distribution and rms–flux relation, are performed on the observations with the aim to compare the results with the similar ones in the γ-ray band presented in Bhatta & Dhital. It is found that, similar to the γ-ray band, blazars display significant variability in the optical band that can be characterized with lognormal flux distribution and a power-law dependence of rms on flux. It could be an indication of a possible inherent linear rms–flux relation, yet the scatter in the data does not allow to rule out other possibilities. When comparing variability properties in the two bands, the blazars in the γ-rays are found to exhibit stronger variability with a steeper possible linear rms–flux relation and a flux distribution that is more skewed toward higher fluxes. The cross-correlation study shows that except for source 3C 273, the overall optical and the γ-ray emission in the sources are highly correlated, suggesting a cospatial existence of the particles responsible for both the optical and γ-ray emission. Moreover, sources S5 0716+714, Mrk 421, Mrk 501, PKS 1424-418, and PKS 2155-304 revealed possible evidence of quasiperiodic oscillations in the optical emission with the characteristic timescales, which are comparable to those in the γ-ray band detected in our previous work.
Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.
Neural Architecture Search in operational context: a remote sensing case-study

Deep learning has become in recent years a cornerstone tool fueling key innovations in the industry, such as autonomous driving. To attain good performances, the neural network architecture used for a given application must be chosen with care. These architectures are often handcrafted and therefore prone to human biases and sub-optimal selection. Neural Architecture Search (NAS) is a framework introduced to mitigate such risks by jointly optimizing the network architectures and its weights. Albeit its novelty, it was applied on complex tasks with significant results - e.g. semantic image segmentation. In this technical paper, we aim to evaluate its ability to tackle a challenging operational task: semantic segmentation of objects of interest in satellite imagery. Designing a NAS framework is not trivial and has strong dependencies to hardware constraints. We therefore motivate our NAS approach selection and provide corresponding implementation details. We also present novel ideas to carry out other such use-case studies.
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection

Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. However, modeling human language at higher-levels of context (i.e., sequences of messages) is under-explored. In stance detection and other social media tasks where the goal is to predict an attribute of a message, we have contextual data that is loosely semantically connected by authorship. Here, we introduce Message-Level Transformer (MeLT) -- a hierarchical message-encoder pre-trained over Twitter and applied to the task of stance prediction. We focus on stance prediction as a task benefiting from knowing the context of the message (i.e., the sequence of previous messages). The model is trained using a variant of masked-language modeling; where instead of predicting tokens, it seeks to generate an entire masked (aggregated) message vector via reconstruction loss. We find that applying this pre-trained masked message-level transformer to the downstream task of stance detection achieves F1 performance of 67%.
Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks

Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of search algorithms and search constraints, accompanied by benchmark algorithms and tasks. We implement an algorithm inspired by zeroth order optimization-based attacks and compare with the benchmark results in the TextAttack framework. Surprisingly, we find that optimization-based methods do not yield any improvement in a constrained setup and slightly benefit from approximate gradient information only in unconstrained setups where search spaces are larger. In contrast, simple heuristics exploiting nearest neighbors without querying the target function yield substantial success rates in constrained setups, and nearly full success rate in unconstrained setups, at an order of magnitude fewer queries. We conclude from these results that current TextAttack benchmark tasks are too easy and constraints are too strict, preventing meaningful research on black-box adversarial text attacks.
Stratifying integral representations of finite groups

We classify the localizing tensor ideals of the integral stable module category for any finite group G. This results in a generic classification of Z[G]-lattices of finite and infinite rank and globalizes the modular case established in celebrated work of Benson, Iyengar, and Krause. Further consequences include a verification of the generalized telescope conjecture in this context, a tensor product formula for integral cohomological support, as well as a generalization of Quillen’s stratification theorem for group cohomology. Our proof makes use of novel descent techniques for stratification in tensortriangular geometry that are of independent interest.
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition

Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, automotive or aerospace, it is often challenging to bridge the gap between an apparently optimal policy learned by an agent and its real-world deployment, due to the uncertainties and risk associated with it. Broadly speaking RL agents face two kinds of uncertainty, 1. aleatoric uncertainty, which reﬂects randomness or noise in the dynamics of the world, and 2. epistemic uncertainty, which reﬂects the bounded knowledge of the agent due to model limitations and ﬁnite amount of information/data the agent has acquired about the world. These two types of uncertainty carry fundamentally different implications for the evaluation of performance and the level of risk or trust. Yet these aleatoric and epistemic uncertainties are generally confounded as standard and even distributional RL is agnostic to this difference. Here we propose how a distributional approach (UA-DQN) can be recast to render uncertainties by decomposing the net ef-fects of each uncertainty . We demonstrate the operation of this method in grid world examples to build intuition and then show a proof of concept application for an RL agent operating as a clinical decision support system in critical care.
Toward practical quantum embedding simulation of realistic chemical systems on near-term quantum computers

Quantum computing has recently exhibited great potential in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimization. Progress has been made in simulating small molecules, such as LiH and hydrogen chains of up to 12 qubits, by using quantum algorithms such as variational quantum eigensolver (VQE). Yet, originating from the limitations of the size and the fidelity of near-term quantum hardware, the accurate simulation of large realistic molecules remains a challenge. Here, integrating an adaptive energy sorting strategy and a classical computational method—the density matrix embedding theory, which respectively reduces the circuit depth and the problem size, we present a means to circumvent the limitations and demonstrate the potential of near-term quantum computers toward solving real chemical problems. We numerically test the method for the hydrogenation reaction of C6H8 and the equilibrium geometry of the C18 molecule, using basis sets up to cc-pVDZ (at most 144 qubits). The simulation results show accuracies comparable to those of advanced quantum chemistry methods such as coupled-cluster or even full configuration interaction, while the number of qubits required is reduced by an order of magnitude (from 144 qubits to 16 qubits for the C18 molecule) compared to conventional VQE. Our work implies the possibility of solving industrial chemical problems on near-term quantum devices.
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs

Machine/deep-learning (ML/DL) based techniques are emerging as a driving force behind many cutting-edge technologies, achieving high accuracy on computer vision workloads such as image classification and object detection. However, training these models involving large parameters is both time-consuming and energy-hogging. In this regard, several prior works have advocated for sparsity to speed up the of DL training and more so, the inference phase. This work begins with the observation that during training, sparsity in the forward and backward passes are correlated. In that context, we investigate two types of sparsity (input and output type) inherent in gradient descent-based optimization algorithms and propose a hardware micro-architecture to leverage the same. Our experimental results use five state-of-the-art CNN models on the Imagenet dataset, and show back propagation speedups in the range of 1.69$\times$ to 5.43$\times$, compared to the dense baseline execution. By exploiting sparsity in both the forward and backward passes, speedup improvements range from 1.68$\times$ to 3.30$\times$ over the sparsity-agnostic baseline execution. Our work also achieves significant reduction in training iteration time over several previously proposed dense as well as sparse accelerator based platforms, in addition to achieving order of magnitude energy efficiency improvements over GPU based execution.
Ethics of AI: A Systematic Literature Review of Principles and Challenges

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.
Age of Information in Multi-source Updating Systems Powered by Energy Harvesting

This paper considers a multi-source updating system in which a transmitter node powered by energy harvesting (EH) sends status updates about multiple sources of information to a destination, where the freshness of status updates is measured in terms of Age of Information (AoI). The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. Further, the transmitter can harvest energy only when its server is idle. Unlike most of the existing queueing-theoretic analyses of AoI that focus on characterizing its average when the transmitter has a reliable energy source and is hence not powered by EH (referred henceforth as a non-EH transmitter), our analysis is focused on understanding the distributional properties of AoI in multi-source systems through the characterization of its moment generating function (MGF). In particular, we use the stochastic hybrid systems (SHS) framework to derive closed-form expressions of the average/MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our results is demonstrated by recovering several existing results as special cases.
Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews

Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-tasks of aspect-based sentiment analysis. The goal in ACD is to specify which aspect of the entity comes up in opinion while ACP aims to specify the polarity of each aspect category from the ACD task. The previous works mostly propose separate solutions for these two sub-tasks. This paper focuses on the ACD and ACP sub-tasks to solve both problems simultaneously. The proposed method carries out multi-label classification where four different deep models were employed and comparatively evaluated to examine their performance. A dataset of Persian reviews was collected from CinemaTicket website including 2200 samples from 14 categories. The developed models were evaluated using the collected dataset in terms of example-based and label-based metrics. The results indicate the high applicability and preference of the CNN and GRU models in comparison to LSTM and Bi-LSTM.
TruthfulQA: Measuring How Models Mimic Human Falsehoods

We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while human performance was 94%. Models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training distribution. We suggest that scaling up models alone is less promising for improving truthfulness than fine-tuning using training objectives other than imitation of text from the web.
Deep Exploration of the Planets HR 8799 b, c, and d with Moderate-resolution Spectroscopy

The four directly imaged planets orbiting the star HR 8799 are an ideal laboratory to probe atmospheric physics and formation models. We present more than a decade’s worth of Keck/OSIRIS observations of these planets, which represent the most detailed look at their atmospheres to date by its resolution and signal-to-noise ratio. We present the first direct detection of HR 8799 d, the second-closest known planet to the star, at moderate spectral resolution with Keck/OSIRIS (K band; R ≈ 4000). Additionally, we uniformly analyze new and archival OSIRIS data (H and K band) of HR 8799 b, c, and d. First, we show detections of water (H2O) and carbon monoxide (CO) in the three planets and discuss the ambiguous case of methane (CH4) in the atmosphere of HR 8799 b. Then, we report radial-velocity (RV) measurements for each of the three planets. The RV measurement of HR 8799 d is consistent with predictions made assuming coplanarity and orbital stability of the HR 8799 planetary system. Finally, we perform a uniform atmospheric analysis on the OSIRIS data, published photometric points, and low-resolution spectra. We do not infer any significant deviation from the stellar value of the carbon-to-oxygen ratio (C/O) of the three planets, which therefore does not yet yield definitive information about the location or method of formation. However, constraining the C/O for all the HR 8799 planets is a milestone for any multiplanet system, and particularly important for large, widely separated gas giants with uncertain formation processes.
Subspace Learning for Personalized Federated Optimization

As data is generated and stored almost everywhere, learning a model from a data-decentralized setting is a task of interest for many AI-driven service providers. Although federated learning is settled down as the main solution in such situations, there still exists room for improvement in terms of personalization. Training federated learning systems usually focuses on optimizing a global model that is identically deployed to all client devices. However, a single global model is not sufﬁcient for each client to be personalized on their performance as local data assumes to be not identically distributed across clients. We propose a method to address this situation through the lens of ensemble learning based on the construction of a low-loss subspace continuum that generates a high-accuracy ensemble of two endpoints (i.e. global model and local model). We demonstrate that our method achieves consistent gains both in personalized and unseen client evaluation settings through extensive experiments on several standard benchmark datasets.
ER-IQA: Boosting Perceptual Quality Assessment Using External Reference Images

Recently, image quality assessment (IQA) has achieved remarkable progress with the success of deep learning. However, the strict pre-condition of full-reference (FR) methods has limited its application in real scenarios. And the no-reference (NR) scheme is also inconvenient due to its unsatisfying performance as a result of ignoring the essence of image quality. In this paper, we introduce a brand new scheme, namely external-reference image quality assessment (ER-IQA), by introducing external reference images to bridge the gap between FR and NR-IQA. As the first implementation and a new baseline of ER-IQA, we propose a new Unpaired-IQA network to process images in a content-unpaired manner. A Mutual Attention-based Feature Enhancement (MAFE) module is well-designed for the unpaired features in ER-IQA. The MAFE module allows the network to extract quality-discriminative features from distorted images and content variability-robust features from external reference ones. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art NR-IQA methods, verifying the effectiveness of ER-IQA and the possibility of narrowing the gap of the two existing categories.
Intermediate scattering potential strength in electron-irradiated 
YBa2Cu3O7−δ
 from London penetration depth measurements

Kyuil Cho, 2 M. Kończykowski, S. Teknowijoyo, 2 S. Ghimire, 2 M. A. Tanatar, 2 Vivek Mishra, and R. Prozorov 2 Ames Laboratory, Ames, IA 50011, USA Department of Physics & Astronomy, Iowa State University, Ames, IA 50011, USA Laboratoire des Solides Irradies, École Polytechnique, CNRS, CEA, Institut Polytechnique de Paris, F-91128 Palaiseau, France Kavli Institute for Theoretical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China (Dated: 18 November 2021)
Unit Selection with Causal Diagram

The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the "benefit function" - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold.
On the chromatic number of graphons

We extend Bollobas' classical result on the chromatic number of a binomial random graph to the exchangeable random graph model $\mathcal{G}(n,W)$ defined by a graphon $W:[0,1]^2 \rightarrow [0,1]$, which is a symmetric measurable function. In the case when $W$ can be approximated by block graphons in $\mathcal{L}^{\infty}$-norm, we show that asymptotically optimal value of the number of colours required for $\mathcal{G}(n,W)$ is determined by colouring strategies that use a finite number of different types of colour classes. Furthermore, if $W$ is a block graphon with $k\times k$ blocks then $k$ types of colour classes are sufficient. We also show that if $W$ is block-increasing or block-Lipschitz then such colouring strategies that use $k$ types determine the chromatic number up to a multiplicative error of order $O(k^{-1})$.