Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse PCA Beyond Covariance Thresholding

In the Wishart model for sparse PCA we are given $n$ samples $Y_1,\ldots, Y_n$ drawn independently from a $d$-dimensional Gaussian distribution $N({0, Id + \beta vv^\top})$, where $\beta>0$ and $v\in \mathbb{R}^d$ is a $k$-sparse unit vector, and we wish to recover $v$ (up to sign). We show that if $n \ge \Omega(d)$, then for every $t \ll k$ there exists an algorithm running in time $n\cdot d^{O(t)}$ that solves this problem as long as \[ \beta \gtrsim \frac{k}{\sqrt{nt}}\sqrt{\ln({2 + td/k^2})}\,. \] Prior to this work, the best polynomial time algorithm in the regime $k\approx \sqrt{d}$, called \emph{Covariance Thresholding} (proposed in [KNV15a] and analyzed in [DM14]), required $\beta \gtrsim \frac{k}{\sqrt{n}}\sqrt{\ln({2 + d/k^2})}$. For large enough constant $t$ our algorithm runs in polynomial time and has better guarantees than Covariance Thresholding. Previously known algorithms with such guarantees required quasi-polynomial time $d^{O(\log d)}$. In addition, we show that our techniques work with sparse PCA with adversarial perturbations studied in [dKNS20]. This model generalizes not only sparse PCA, but also other problems studied in prior works, including the sparse planted vector problem. As a consequence, we provide polynomial time algorithms for the sparse planted vector problem that have better guarantees than the state of the art in some regimes. Our approach also works with the Wigner model for sparse PCA. Moreover, we show that it is possible to combine our techniques with recent results on sparse PCA with symmetric heavy-tailed noise [dNNS22]. In particular, in the regime $k \approx \sqrt{d}$ we get the first polynomial time algorithm that works with symmetric heavy-tailed noise, while the algorithm from [dNNS22]. requires quasi-polynomial time in these settings.
Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958–2022 and for the professionals’ perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.
Metal line emission from galaxy haloes at z ≈ 1

We present a study of the metal-enriched halo gas, traced using Mg ii and [O ii] emission lines, in two large, blind galaxy surveys – the MUSE (Multi Unit Spectroscopic Explorer) Analysis of Gas around Galaxies (MAGG) and the MUSE Ultra Deep Field (MUDF). By stacking a sample of ≈600 galaxies (stellar masses $\rm M_*$ ≈106 − 12 $\rm M_\odot$), we characterize for the first time the average metal line emission from a general population of galaxy haloes at 0.7 ≤ z ≤ 1.5. The Mg ii and [O ii] line emission extends farther out than the stellar continuum emission, on average out to ≈25 kpc and ≈45 kpc, respectively, at a surface brightness (SB) level of 10−20 $\rm erg\, s^{-1}\, cm^{-2}\, arcsec^{-2}$. The radial profile of the Mg ii SB is shallower than that of the [O ii], suggesting that the resonant Mg ii emission is affected by dust and radiative transfer effects. The [O ii] to Mg ii SB ratio is ≈3 over ≈20 − 40 kpc, also indicating a significant in situ origin of the extended metal emission. The average SB profiles are intrinsically brighter by a factor ≈2 − 3 and more radially extended by a factor of ≈1.3 at 1.0 < z ≤ 1.5 than at 0.7 ≤ z ≤ 1.0. The average extent of the metal emission also increases independently with increasing stellar mass and in overdense group environments. When considering individual detections, we find extended [O ii] emission up to ≈50 kpc around ≈30–40 per cent of the group galaxies, and extended (≈30 − 40 kpc) Mg ii emission around two z ≈ 1 quasars in groups, which could arise from outflows or environmental processes.
Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective

In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem that is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE) have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems and prove that the EP-EC maximization problem is equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent inner-search collaborative algorithm. Extensive simulations demonstrate that the proposed framework in reducing computational complexity compared to reference schemes, and in providing various tradeoffs and optimization performance of xURLLC concerning UB-SDVP, EP, EP-EC, and EP-EE.
On Feasibility of Server-side Backdoor Attacks on Split Learning

Split learning is a collaborative learning design that allows several participants (clients) to train a shared model while keeping their datasets private. In split learning, the network is split into two halves: clients have the initial part until the cut layer, and the remaining part of the network is on the server side. In the training process, clients feed the data into the first part of the network and send the output (smashed data) to the server, which uses it as the input for the remaining part of the network. Recent studies demonstrate that collaborative learning models, specifically federated learning, are vulnerable to security and privacy attacks such as model inference and backdoor attacks. While there have been studies regarding inference attacks on split learning, it has not yet been tested for backdoor attacks. This paper performs a novel backdoor attack on split learning and studies its effectiveness. Despite traditional backdoor attacks done on the client side, we inject the backdoor trigger from the server side. We provide two attack methods: one using a surrogate client and another using an autoencoder to poison the model via incoming smashed data and its outgoing gradient toward the innocent participants. The results show that despite using strong patterns and injection methods, split learning is highly robust and resistant to such poisoning attacks. While we get the attack success rate of 100% as our best result for the MNIST dataset, in most of the other cases, our attack shows little success when increasing the cut layer.
Solving Boltzmann equation with neural sparse representation

We consider the neural sparse representation to solve Boltzmann equation with BGK and quadratic collision model, where a network-based ansatz that can approximate the distribution function with extremely high efficiency is proposed. Precisely, fully connected neural networks are employed in the time and spatial space so as to avoid the discretization in space and time. The different low-rank representations are utilized in the microscopic velocity for the BGK and quadratic collision model, resulting in a significant reduction in the degree of freedom. We approximate the discrete velocity distribution in the BGK model using the canonical polyadic decomposition. For the quadratic collision model, a data-driven, SVD-based linear basis is built based on the BGK solution. All these will significantly improve the efficiency of the network when solving Boltzmann equation. Moreover, the specially designed adaptive-weight loss function is proposed with the strategies as multi-scale input and Maxwellian splitting applied to further enhance the approximation efficiency and speed up the learning process. Several numerical experiments, including 1D wave and Sod problems and 2D wave problem, demonstrate the effectiveness of these neural sparse representation methods.
NHtapDB: Native HTAP Databases

Native database (1) provides a near-data machine learning framework to facilitate generating real-time business insight, and predefined change thresholds will trigger online training and deployment of new models, and (2) offers a mixed-format store to guarantee the performance of HTAP workloads, especially the hybrid workloads that consist of OLAP queries in-between online transactions. We make rigorous test plans for native database with an enhanced state-of-the-art HTAP benchmark.
A Note on the Efficiency of Sandwich Covariance Matrix Estimation

The sandwich estimator, also known as robust covariance matrix estimator, heteroscedasticity-consistent covariance matrix estimate, or empirical covariance matrix estimator, has achieved increasing use in the econometric literature as well as with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when the fitted parametric model fails to hold or is not even specified. Surprisingly though, there has been little discussion of properties of the sandwich method other than consistency. We investigate the sandwich estimator in quasi-likelihood models asymptotically, and in the linear case analytically. We show that under certain circumstances when the quasi-likelihood model is correct, the sandwich estimate is often far more variable than the usual parametric variance estimate. The increased variance is a fixed feature of the method and the price that one pays to obtain consistency even when the parametric model fails or when there is heteroscedasticity. We show that the additional variability directly affects the coverage probability of confidence intervals constructed from sandwich variance estimates. In fact, the use of sandwich variance estimates combined with t-distribution quantiles gives confidence intervals with coverage probability falling below the nominal value. We propose an adjustment to compensate for this fact.
2D-Empowered 3D Object Detection on the Edge

3D object detection has a pivotal role in a wide range of applications, most notably autonomous driving and robotics. These applications are commonly deployed on edge devices to promptly interact with the environment, and often require near real-time response. With limited computation power, it is challenging to execute 3D detection on the edge using highly complex neural networks. Common approaches such as offloading to the cloud brings latency overheads due to the large amount of 3D point cloud data during transmission. To resolve the tension between wimpy edge devices and compute-intensive inference workloads, we explore the possibility of transforming fast 2D detection results to extrapolate 3D bounding boxes. To this end, we present Moby, a novel system that demonstrates the feasibility and potential of our approach. Our main contributions are two-fold: First, we design a 2D-to-3D transformation pipeline that takes as input the point cloud data from LiDAR and 2D bounding boxes from camera that are captured at exactly the same time, and generate 3D bounding boxes efficiently and accurately based on detection results of the previous frames without running 3D detectors. Second, we design a frame offloading scheduler that dynamically launches a 3D detection when the error of 2D-to-3D transformation accumulates to a certain level, so the subsequent transformations can draw upon the latest 3D detection results with better accuracy. Extensive evaluation on NVIDIA Jetson TX2 with the autonomous driving dataset KITTI and real-world 4G/LTE traces shows that, Moby reduces the end-to-end latency by up to 91.9% with mild accuracy drop compared to baselines. Further, Moby shows excellent energy efficiency by saving power consumption and memory footprint up to 75.7% and 48.1%, respectively.
Cost-effective Models for Detecting Depression from Speech

Depression is the most common psychological disorder and is considered as a leading cause of disability and suicide worldwide. An automated system capable of detecting signs of depression in human speech can contribute to ensuring timely and effective mental health care for individuals suffering from the disorder. Developing such automated system requires accurate machine learning models, capable of capturing signs of depression. However, state-of-the-art models based on deep acoustic representations require abundant data, meticulous selection of features, and rigorous training; the procedure involves enormous computational resources. In this work, we explore the effectiveness of two different acoustic feature groups-conventional hand-curated and deep representation features, for predicting the severity of depression from speech. We explore the relevance of possible contributing factors to the models’ performance, including gender of the individual, severity of the disorder, content and length of speech. Our findings suggest that models trained on conventional acoustic features perform equally well or better than the ones trained on deep representation features at significantly lower computational cost, irrespective of other factors, e.g. content and length of speech, gender of the speaker and severity of the disorder. This makes such models a better fit for deployment where availability of computational resources is restricted, such as real time depression monitoring applications in smart devices.
A Text Mining Analysis of Data Protection Politics: The Case of Plenary Sessions of the European Parliament

Data protection laws and policies have been studied extensively in recent years, but little is known about the parliamentary politics of data protection. This imitation applies even to the European Union (EU) that has taken the global lead in data protection and privacy regulation. For patching this notable gap in existing research, this paper explores the data protection questions raised by the Members of the European Parliament (MEPs) in the Parliament's plenary sessions and the answers given to these by the European Commission. Over a thousand of such questions and answers are covered in a period from 1995 to early 2023. Given computational analysis based on text mining, the results indicate that (a) data protection has been actively debated in the Parliament during the past twenty years. No noticeable longitudinal trends are present; the debates have been relatively constant. As could be expected, (b) the specific data protection laws in the EU have frequently been referenced in these debates, which (c) do not seem to align along conventional political dimensions such as the left-right axis. Furthermore, (d) numerous distinct data protection topics have been debated by the parliamentarians, indicating that data protection politics in the EU go well-beyond the recently enacted regulations.
An Example Article

We investigate the linear stability analysis of a pathway-based diﬀusion model (PBDM), which characterizes the dynamics of the engineered Escherichia coli populations [X. Xue and C. Xue and M. Tang, PLoS Computational Biology , 14 (2018), pp. e1006178]. This stability analysis considers small perturbations of the density and chemical concentration around two non-trivial steady states, and the linearized equations are transformed into a generalized eigenvalue problem. By formal analysis, when the internal variable responds to the outside signal fast enough, the PBDM converges to an anisotropic diﬀusion model, for which the probability density distribution in the internal variable becomes a delta function. We introduce an asymptotic preserving (AP) scheme for the PBDM that converges to a stable limit scheme consistent with the anisotropic diffusion model. Further numerical simulations demonstrate the theoretical results of linear stability analysis, i.e., the pattern formation, and the convergence of the AP scheme.
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare

Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare. Since deploying and evaluating policies online are high-stakes in such tasks, off-policy evaluation (OPE) is crucial for inducing effective policies. In human-centric environments, however, OPE is challenging because the underlying state is often unobservable, while only aggregate rewards can be observed (students' test scores or whether a patient is released from the hospital eventually). In this work, we propose a human-centric OPE (HOPE) to handle partial observability and aggregated rewards in such environments. Specifically, we reconstruct immediate rewards from the aggregated rewards considering partial observability to estimate expected total returns. We provide a theoretical bound for the proposed method, and we have conducted extensive experiments in real-world human-centric tasks, including sepsis treatments and an intelligent tutoring system. Our approach reliably predicts the returns of different policies and outperforms state-of-the-art benchmarks using both standard validation methods and human-centric significance tests.
High resolution photoassociation spectroscopy of the excited $c^3\Sigma_{1}^+$ potential of $^{23}$Na$^{133}$Cs

We report on photoassociation spectroscopy probing the $c^3\Sigma_{1}^+$ potential of the bi-alkali NaCs molecule, identifying eleven vibrational lines between $v' = 0$ and $v' = 25$ of the excited $c^3\Sigma_{1}^+$ potential, and resolving their rotational and hyperfine structure. The observed lines are assigned by fitting to an effective Hamiltonian model of the excited state structure with rotational and hyperfine constants as free parameters. We discuss unexpected broadening of select vibrational lines, and its possible link to strong spin-orbit coupling of the $c^3\Sigma_{1}^+$ potential with the nearby $b^3\Pi_1$ and $B^1\Pi_1$ manifolds. Finally we report use of the $v' = 22$ line as an intermediate state for two-photon transfer of weakly bound Feshbach molecules to the rovibrational ground state of the $X^1\Sigma^+$ manifold.
GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search

When people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. Building this mental model can be challenging as it requires not only finding relevant documents but also synthesizing important concepts and the relationships that connect those concepts both within and across documents. This article describes a novel interactive approach designed to help users construct a mental model of an unfamiliar information space during exploratory search. We propose a new semantic search system to organize and visualize important concepts and their relations for a set of search results. A user study (n=20) was conducted to compare the proposed approach against a baseline faceted search system on exploratory literature search tasks. Experimental results show that the proposed approach is more effective in helping users recognize relationships between key concepts, leading to a more sophisticated understanding of the search topic while maintaining similar functionality and usability as a faceted search system.
Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data. Based on the above improvements, we create a novel anomaly detection strategy FastICA-TGAK-OCELM. The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies (ALFA) dataset. The experimental results show that compared with other methods, the accuracy of this method is improved by more than 30%, and point anomalies are effectively detected.
An Accurate Model to Estimate 5G Propagation Path Loss for the Indoor Environment

This paper presents a new large-scale propagation path loss model to design a fifth-generation (5G) wireless communication system for indoor environments. Simulations for the indoor environment, for all polarization at non-line-of-sight (NLOS) and line-of-sight (LOS), which are performed per meter over a distance of 47 m between each of the separated transmitter antenna (TX) and the receiver antenna (RX) positions to compare better the proposed extensive flexible path loss model with previous models. All the simulations are conducted at the Abu-rayhan buildings at the Amirkabir University of Technology. The results demonstrated that the simple presented model with a single parameter denoted ZMS can predict the expansive path loss over distance more accurately. The values of the path loss exponent (PLE) for the LOS scenario are simulated and achieved at 3.63, 1.81, and 3.42 for the V-H, V-V, and V-Omni antenna polarizations, and for NLOS is 6.11, 4.21, and 5.23 at the 28 GHz frequency for all the polarization antenna type V-H, V-V, and V-Omni, appropriately.