Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate air quality time series analysis via a recurrent variational deep learning model

Monitoring of air pollutants across space and time is critical in understanding pollution trends and reporting air quality. The Air Quality Index (AQI) is a tool used to communicate air quality that incorporates atmospheric concentrations of five major pollution indicators: ground-level ozone, particulate matter, carbon monoxide, sulfur dioxide, and nitrogen dioxide. The ability to accurately forecast these concentrations and identify unusual levels is of particular importance. In this work, we develop a generative time series model for air quality indicators and use it for long and short-term probabilistic forecasts. Air quality data are multivariate and exhibit high variability across indicators in both space and time. Marginal indicator distributions are typically skewed and contain substantial zeros, while indicator-wise cross-correlations can be highly non-linear. We find that hourly measurements additionally exhibit substantial temporal cross-correlation, long-term dependence, and daily periodicity. To capture these complexities, we employ a recurrent extension of the variational autoencoder (VAE) to sequential data. The VAE is a generative neural network architecture capable of learning complex, high dimensional manifolds on which data are distributed. Furthermore, recurrent architectures can capture non-linear and long-term temporal qualities of time series data. We train the proposed time series model on historical air quality measurements at multiple locations and demonstrate its ability to capture observed indicator-wise and temporal complexities. We additionally use the trained model to compute probabilistic forecasts and credible intervals of air quality indicators.
Transverse flexural fracture of a plate containing an arc crack

Abstract In this paper, the transverse flexural fracture of a plate weakened by a circular arc crack is studied. The flexural loads of out‐of‐plane bending, twisting, and shearing are assumed to act upon the plate edge at infinity. On the basis of the complex variable approach for first boundary condition problems, a new conformal mapping is proposed to transform the contour surface of a circular arc crack into a unit circle. General formulations for transverse loading were applied to derive the governing boundary equation. By taking the Cauchy integral for each term of the boundary equation, the complex stress functions corresponding to the flexural loads are formulated. Also, corresponding stress intensity factors are given through a coordinate transformation followed by taking a limiting process. In addition, for the transverse flexural fracture, the mutual weakening between both crack tips is investigated.
Aesthetic Embeddedness: Towards an Aesthetic Understanding of Cultural and Artistic Entrepreneurship

ABSTRACT Aesthetics is quintessential for entrepreneurial practice and theory. Specifically, we argue that aesthetics provides a more sophisticated understanding of the embeddedness of cultural and artistic entrepreneurship (CAE). This paper is based on an aesthetic ethnography of entrepreneurial organizations in the music sector in Brazil. Our findings generate a conceptualization of aesthetic embeddedness, explaining how CAE is embedded in culture through three practices (crossing, syncretic and valuing). Crossing practices are aesthetic contagions that generate exchange. Syncretic practices are harmonizations between different elements that create coexistences during aesthetic product creation. Valuing practices are aesthetic negotiations that occur between entrepreneurs and stakeholders. As an outcome of the three practices, we discuss how aesthetic knowledge deriving from aesthetic embeddedness can be mobilized as aesthetic capital, value and innovation in entrepreneurial practice.
Parent-Adolescent Relationship Quality, Ethnic Identity, and Romantic Attachment in Asian American Emerging Adults

Given increasing diversity in the U.S., ethnocultural differences in identity formation need exploration. We use attachment theory to understand the associations among ethnic identity, parent-adolescent relationship quality, and romantic attachment quality. We examined emerging adults’ perceived relationship quality with parents during their adolescence in relation to current ethnic identity strength and romantic attachment avoidance and anxiety along with gender and ethnicity among East Asians, Southeast Asians, and South Asians (N = 358 undergraduate students). While bivariate correlations only found a positive relation to maternal connection and romantic avoidance for the overall sample, the path analyses provided some support for the main hypotheses. Emerging adults’ perceived maternal connection during adolescence was negatively associated with current romantic attachment avoidance among emerging-adult men, and perceived paternal connection during adolescence was negatively related to current romantic attachment anxiety among emerging-adult women. Ethnic identity moderated the relation between maternal connection and anxiety for women and men.
Efficient Coflow Scheduling in Hybrid-Switched Data Center Networks

To improve the application-level communication performance, scheduling of coflows, a collection of parallel flows sharing the same objective, is prevalent in modern data center networks (DCNs). Meanwhile, a hybrid-switched DCN design combining optical circuit switches (OPS) and electrical packet switches (EPS) for transmitting high-volume traffic and low-volume traffic separately has received considerable research attention recently. Efficient scheduling of coflows on hybrid network links is crucial for reducing the overall communication time. However, because of the reconfiguration delay in the circuit switch due to the ultra-high transmission rate and the limitation of bandwidth in the packet switch, coflow scheduling becomes increasingly challenging. The existing coflow scheduling algorithms in hybrid-switched DCNs are all heuristic and provide no performance guarantees. In this work, we propose an approximation algorithm with the worst-case performance guarantee of 2+ \lambda? , where \lambda? is a factor related to system parameters and demand characteristics, for single coflow scheduling in hybridswitched DCN to minimize the coflow completion time (CCT). Extensive simulations based on Facebook data traces show that our algorithm outperforms the state-of-the-art schemes Solstice by 1.14? and Reco-Sin by 1.42? in terms of minimizing CCT.
Leveraging Residue Number System for Designing High-Precision Analog Deep Neural Network Accelerators

Achieving high accuracy, while maintaining good energy efficiency, in analog DNN accelerators is challenging as high-precision data converters are expensive. In this paper, we overcome this challenge by using the residue number system (RNS) to compose high-precision operations from multiple low-precision operations. This enables us to eliminate the information loss caused by the limited precision of the ADCs. Our study shows that RNS can achieve 99% FP32 accuracy for state-of-the-art DNN inference using data converters with only $6$-bit precision. We propose using redundant RNS to achieve a fault-tolerant analog accelerator. In addition, we show that RNS can reduce the energy consumption of the data converters within an analog accelerator by several orders of magnitude compared to a regular fixed-point approach.
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding

Causal inference of exact individual treatment outcomes in the presence of hidden confounders is rarely possible. Recent work has extended prediction intervals with finite-sample guarantees to partially identifiable causal outcomes, by means of a sensitivity model for hidden confounding. In deep learning, predictors can exploit their inductive biases for better generalization out of sample. We argue that the structure inherent to a deep ensemble should inform a tighter partial identification of the causal outcomes that they predict. We therefore introduce an approach termed Caus-Modens, for characterizing causal outcome intervals by modulated ensembles. We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals, as measured by the necessary interval size to achieve sufficient coverage. The last of our three diverse benchmarks is a novel usage of GPT-4 for observational experiments with unknown but probeable ground truth.
Understanding the Application of Utility Theory in Robotics and Artificial Intelligence: A Survey

As a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field, the utility is used to evaluate the level of individual needs, preferences, and interests. Especially for decision-making and learning in multi-agent/robot systems (MAS/MRS), a suitable utility model can guide agents in choosing reasonable strategies to achieve their current needs and learning to cooperate and organize their behaviors, optimizing the system's utility, building stable and reliable relationships, and guaranteeing each group member's sustainable development, similar to the human society. Although these systems' complex, large-scale, and long-term behaviors are strongly determined by the fundamental characteristics of the underlying relationships, there has been less discussion on the theoretical aspects of mechanisms and the fields of applications in Robotics and AI. This paper introduces a utility-orient needs paradigm to describe and evaluate inter and outer relationships among agents' interactions. Then, we survey existing literature in relevant fields to support it and propose several promising research directions along with some open problems deemed necessary for further investigations.
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning

High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics -- a deep learning approach that predicts multi-omics in a cell population directly from high-content images stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cell (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictors based on the mean observed training set abundance. We observed significant predictability of abundances for 5903 (22.43%; 95% CI: 8.77%, 38.88%) and 5819 (22.11%; 95% CI: 10.40%, 38.08%) transcripts out of 26137 in M1 and M2-stimulated macrophages respectively and for 1933 (38.77%; 95% CI: 36.94%, 39.85%) and 2055 (41.22%; 95% CI: 39.31%, 42.42%) proteins out of 4986 in M1 and M2-stimulated macrophages respectively. Our results show that some transcript and protein abundances are predictable from cell imaging and that cell imaging may potentially, in some settings and depending on the mechanisms of interest and desired performance threshold, even be a scalable and resource-efficient substitute for multi-omics measurements.
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier

Network Intrusion Detection Systems (NIDS) have been extensively investigated by monitoring real network traffic and analyzing suspicious activities. However, there are limitations in detecting specific types of attacks with NIDS, such as Advanced Persistent Threats (APT). Additionally, NIDS is restricted in observing complete traffic information due to encrypted traffic or a lack of authority. To address these limitations, a Host-based Intrusion Detection system (HIDS) evaluates resources in the host, including logs, files, and folders, to identify APT attacks that routinely inject malicious files into victimized nodes. In this study, a hybrid network intrusion detection system that combines NIDS and HIDS is proposed to improve intrusion detection performance. The feature flattening technique is applied to flatten two-dimensional host-based features into one-dimensional vectors, which can be directly used by traditional Machine Learning (ML) models. A two-stage collaborative classifier is introduced that deploys two levels of ML algorithms to identify network intrusions. In the first stage, a binary classifier is used to detect benign samples. All detected attack types undergo a multi-class classifier to reduce the complexity of the original problem and improve the overall detection performance. The proposed method is shown to generalize across two well-known datasets, CICIDS 2018 and NDSec-1. Performance of XGBoost, which represents conventional ML, is evaluated. Combining host and network features enhances attack detection performance (macro average F1 score) by 8.1% under the CICIDS 2018 dataset and 3.7% under the NDSec-1 dataset. Meanwhile, the two-stage collaborative classifier improves detection performance for most single classes, especially for DoS-LOIC-UDP and DoS-SlowHTTPTest, with improvements of 30.7% and 84.3%, respectively, when compared with the traditional ML XGBoost.
Non-segmental Bayesian Detection of Multiple Change-points

We propose an original and general NOn-SEgmental (NOSE) approach for the detection of multiple change-points. NOSE identifies change-points by the non-negligibility of posterior estimates of the jump heights. Alternatively, under the Bayesian paradigm, NOSE treats the step-wise signal as a global infinite dimensional parameter drawn from a proposed process of atomic representation, where the random jump heights determine the locations and the number of change-points simultaneously. The random jump heights are further modeled by a Gamma-Indian buffet process shrinkage prior under the form of discrete spike-and-slab. The induced maximum a posteriori estimates of the jump heights are consistent and enjoy zerodiminishing false negative rate in discrimination under a 3-sigma rule. The success of NOSE is guaranteed by the posterior inferential results such as the minimaxity of posterior contraction rate, and posterior consistency of both locations and the number of abrupt changes. NOSE is applicable and effective to detect scale shifts, mean shifts, and structural changes in regression coefficients under linear or autoregression models. Comprehensive simulations and several real-world examples demonstrate the superiority of NOSE in detecting abrupt changes under various data settings.
Automatic Trade-off Adaptation in Offline RL

Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm \cite{lion} provides the user with an interface to set the trade-off between behavior cloning and optimality w.r. t. the estimated return at runtime. Experts can then use this interface to adapt the policy behavior according to their preferences and find a good trade-off between conservatism and performance optimization. Since expert time is precious, we extend the methodology with an autopilot that automatically finds the correct parameterization of the trade-off, yielding a new algorithm which we term AutoLION.
System-bath entanglement of noninteracting fermionic impurities: Equilibrium, transient, and steady-state regimes

We investigate the behavior of entanglement between a single fermionic level and a fermionic bath in three distinct thermodynamic regimes. First, in thermal equilibrium, we analyze the dependence of entanglement on the considered statistical ensemble: for the grand canonical state, it is generated only for a sufficiently strong system-bath coupling, whereas it is present for arbitrarily weak couplings for the canonical state with a fixed particle number. The threshold coupling strength, at which entanglement appears, is shown to strongly depend on the bath bandwidth. Second, we consider the relaxation to equilibrium. In this case a transient entanglement in a certain time interval can be observed even in the weak-coupling regime, when the reduced dynamics and thermodynamics of the system can be well described by an effectively classical and Markovian master equation for the state populations. At strong coupling strengths, entanglement is preserved for long times and converges to its equilibrium value. Finally, in voltage-driven junctions, a steady-state entanglement is generated for arbitrarily weak system-bath couplings at a certain threshold voltage. It is enhanced in the strong-coupling regime, and it is reduced by either the particle-hole or the tunnel coupling asymmetry.
CSREU: A Novel Dataset about Corporate Social Responsibility and Performance Indicators

Corporate Social Responsibility (CSR) has become an important topic that is gaining academic interest. This research paper presents CSREU, a new dataset with attributes of 115 European companies, which includes several performance indicators and the respective CSR disclosure scores computed using the Global Reporting Initiative (GRI) framework. We also examine the correlations between some of the financial indicators and the CSR disclosure scores of the companies. According to our results, these correlations are weak and deeper analysis is required to draw convincing conclusions about the potential impact of CSR disclosure on financial performance. We hope that the newly created data and our preliminary results will help and foster research in this field.
Quantum Mycielski Graphs

The classical Mycielski transformation allows for constructing from a given graph the new one, with an arbitrarily large chromatic number but preserving the size of the largest clique contained in it. This particular construction and its specific generalizations were widely discussed in graph theory literature. Here we propose an analog of these transformations for quantum graphs and study how they affect the (quantum) chromatic number as well as clique numbers associated with them.
Multi-Classification using One-versus-One Deep Learning Strategy with Joint Probability Estimates

The One-versus-One (OvO) strategy is an approach of multi-classification models which focuses on training binary classifiers between each pair of classes. While the OvO strategy takes advantage of balanced training data, the classification accuracy is usually hindered by the voting mechanism to combine all binary classifiers. In this paper, a novel OvO multi-classification model incorporating a joint probability measure is proposed under the deep learning framework. In the proposed model, a two-stage algorithm is developed to estimate the class probability from the pairwise binary classifiers. Given the binary classifiers, the pairwise probability estimate is calibrated by a distance measure on the separating feature hyperplane. From that, the class probability of the subject is estimated by solving a joint probability-based distance minimization problem. Numerical experiments in different applications show that the proposed model achieves generally higher classification accuracy than other state-of-the-art models.
On the constants in Mertens' theorems for primes in arithmetic progressions

A 1976 result from Norton may be used to give an asymptotic (but not explicit) description of the constant in Mertens' second theorem for primes in arithmetic progressions. Assuming the Generalised Riemann Hypothesis, we make Norton's observation explicit and extend this result to multiple progressions.
Clickbait Detection via Large Language Models

Clickbait, which aims to induce users with some surprising and even thrilling headlines for increasing click-through rates, permeates almost all online content publishers, such as news portals and social media. Recently, Large Language Models (LLMs) have emerged as a powerful instrument and achieved tremendous success in a series of NLP downstream tasks. However, it is not yet known whether LLMs can be served as a high-quality clickbait detection system. In this paper, we analyze the performance of LLMs in the few-shot and zero-shot scenarios on several English and Chinese benchmark datasets. Experimental results show that LLMs cannot achieve the best results compared to the state-of-the-art deep and fine-tuning PLMs methods. Different from human intuition, the experiments demonstrated that LLMs cannot make satisfied clickbait detection just by the headlines.
Discourse Representation Structure Parsing for Chinese

Previous work has predominantly focused on monolingual English semantic parsing. We, instead, explore the feasibility of Chinese semantic parsing in the absence of labeled data for Chinese meaning representations. We describe the pipeline of automatically collecting the linearized Chinese meaning representation data for sequential-to-sequential neural networks. We further propose a test suite designed explicitly for Chinese semantic parsing, which provides fine-grained evaluation for parsing performance, where we aim to study Chinese parsing difficulties. Our experimental results show that the difficulty of Chinese semantic parsing is mainly caused by adverbs. Realizing Chinese parsing through machine translation and an English parser yields slightly lower performance than training a model directly on Chinese data.
Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction

Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture called Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG), which estimates multiple reward terms for traffic signal control optimization using age-decaying weights. Our approach involves two types of agents: one focuses on optimizing local traffic at each intersection, while the other aims to optimize global traffic throughput. We evaluate our method using real-world traffic data collected from an Asian country's traffic cameras. Despite the inclusion of a global agent, our solution remains decentralized as this agent is no longer necessary during the inference stage. Our results demonstrate the effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all performance metrics. Additionally, our proposed system minimizes both waiting time and carbon emissions. Notably, this paper is the first to link carbon emissions and global agents in traffic signal control.