Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research.
MEMD-HHT based Emotion Detection from EEG using 3D CNN

In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation

In this paper, we study the problem of sparse mixed linear regression on an unlabeled dataset that is generated from linear measurements from two different regression parameter vectors. Since the data is unlabeled, our task is not only to figure out a good approximation of the regression parameter vectors but also to label the dataset correctly. In its original form, this problem is NP-hard. The most popular algorithms to solve this problem (such as Expectation-Maximization) have a tendency to stuck at local minima. We provide a novel invex relaxation for this intractable problem which leads to a solution with provable theoretical guarantees. This relaxation enables exact recovery of data labels. Furthermore, we recover a close approximation of the regression parameter vectors which match the true parameter vectors in support and sign. Our formulation uses a carefully constructed primal dual witnesses framework for the invex problem. Furthermore, we show that the sample complexity of our method is only logarithmic in terms of the dimension of the regression parameter vectors.
Positional uncertainty and quality assurance of digital elevation change detection (DECD)

: Studies on rapid change detection of large area urgently need to be extended from 2D image to digital elevation model (DEM) due to the challenge of changes caused by disasters. This research investigates positional uncertainty of digital elevation change detection (DECD) caused by different degrees of DEM complexity and DEM misregistration. Unfortunately, using three-sigma rule (3σR) for DECD is disturbed by accuracy of parameter estimation, which is affected by the outliers (i.e., varied DEM) from DEM differencing samples. Hence, to reduce the aforementioned uncertainty of DECD, we propose a new strategy of quality assurance, adaptively censored three-sigma rule (AC3σR), in which with the samples censored, outliers of global DEM differencing samples outside the standard deviations of the mean calculated by moment estimation are iteratively removed. Compared with the 3σR and censored three-sigma rule (C3σR) that is similar to AC3σR but without iteration for both simulation and real-world data experiments, the proposed global AC3σR method always exhibits the highest accuracies of DECD in terms of both the overall accuracies 0.99967, 0.98740 and kappa coefficients 0.99598, 0.81803 respectively, and the strongest robustness with a large convergence interval [0, 0.30010] under the simulated maximum registration error and most complex terrain complexity conditions.
Wood for Structural and Architectural Purposes

This paper reports the findings and conclusions of Panel II on Structural Purposes, Committee on Renewable Resources for Industrial Materials (CORRIM), National Academy of Sciences/National Research Council. The Panel examined the use of wood for structural purposes and its conversion from standing trees to primary structural commodities as of 1970, and from this base year developed projections of use to the years 1985 and 2000. Concerns of the Panel included the availability of the renewable resource, the demand for wood products, and particularly the costs in terms of manpower, energy, and capital depreciation involved in production and transportation to the point of use. Comparable data from source to end commodity were compiled for other structural materials including steel, aluminum, concrete, brick, and petrochemical derivatives. Wood products were found, with few exceptions, to be more homogeneous than nonwood-based commodities in man-hour and capital requirements. However, the most notable differences between wood-based and nonwood-based commodities are in their energy requirements. Commodities based on nonrenewable resources are appreciably more energy intensive per ton of product than are their wood-based counterparts. In part, this is the result of energy self-sufficiency in the manufacturing process attained through the use of wood residues as fuel.
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise

Stochastic first-order methods such as Stochastic Extragradient (SEG) or Stochastic Gradient Descent-Ascent (SGDA) for solving smooth minimax problems and, more generally, variational inequality problems (VIP) have been gaining a lot of attention in recent years due to the growing popularity of adversarial formulations in machine learning. However, while high-probability convergence bounds are known to reflect the actual behavior of stochastic methods more accurately, most convergence results are provided in expectation. Moreover, the only known high-probability complexity results have been derived under restrictive sub-Gaussian (light-tailed) noise and bounded domain assumption [Juditsky et al., 2011]. In this work, we prove the first high-probability complexity results with logarithmic dependence on the confidence level for stochastic methods for solving monotone and structured non-monotone VIPs with non-sub-Gaussian (heavy-tailed) noise and unbounded domains. In the monotone case, our results match the best-known ones in the light-tails case [Juditsky et al., 2011], and are novel for structured non-monotone problems such as negative comonotone, quasi-strongly monotone, and/or star-cocoercive ones. We achieve these results by studying SEG and SGDA with clipping. In addition, we numerically validate that the gradient noise of many practical GAN formulations is heavy-tailed and show that clipping improves the performance of SEG/SGDA.
The Phenomenon of Policy Churn

We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly rapid pace, changing the greedy action in a large fraction of states within a handful of learning updates (in a typical deep RL set-up such as DQN on Atari). We characterise the phenomenon empirically, verifying that it is not limited to specific algorithm or environment properties. A number of ablations help whittle down the plausible explanations on why churn occurs to just a handful, all related to deep learning. Finally, we hypothesise that policy churn is a beneficial but overlooked form of implicit exploration that casts $\epsilon$-greedy exploration in a fresh light, namely that $\epsilon$-noise plays a much smaller role than expected.
Policy Gradient Algorithms with Monte-Carlo Tree Search for Non-Markov Decision Processes

Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes a parameterized policy model for an expected return using gradient ascent. Given a well-parameterized policy model, such as a neural network model, with appropriate initial parameters, the PG algorithms work well even when environment does not have the Markov property. Otherwise, they can be trapped on a plateau or suffer from peakiness effects. As another successful RL approach, algorithms based on Monte-Carlo Tree Search (MCTS), which include AlphaZero, have obtained groundbreaking results especially on the board game playing domain. They are also suitable to be applied to non-Markov decision processes. However, since the standard MCTS does not have the ability to learn state representation, the size of the tree-search space can be too large to search. In this work, we examine a mixture policy of PG and MCTS in order to complement each other’s difﬁculties and take advantage of them. We derive conditions for asymptotic convergence with results of a two-timescale stochastic approximation and propose an algorithm that satisﬁes these conditions. The effectivity of the proposed methods is veriﬁed through numerical experiments on non-Markov decision processes.
Approximate solutions to the optimal flow problem of multi-area integrated electrical and gas systems

We formulate the optimal flow problem in a multi-area integrated electrical and gas system as a mixed-integer optimization problem by approximating the non-linear gas flows with piece-wise affine functions, thus resulting in a set of mixed-integer linear constraints. For its solution, we propose a novel algorithm that consists in one stage for solving a convexified problem and a second stage for recovering a mixed-integer solution. The latter exploits the gas flow model and requires solving a linear program. We provide an optimality certificate for the computed solution and show the advantages of our algorithm with respect to the state-of-the-art method via numerical simulations.
Revisiting the General Identifiability Problem

We revisit the problem of general identifiability originally introduced in [Lee et al., 2019] for causal inference and note that it is necessary to add positivity assumption of observational distribution to the original definition of the problem. We show that without such an assumption the rules of do-calculus and consequently the proposed algorithm in [Lee et al., 2019] are not sound. Moreover, adding the assumption will cause the completeness proof in [Lee et al., 2019] to fail. Under positivity assumption, we present a new algorithm that is provably both sound and complete. A nice property of this new algorithm is that it establishes a connection between general identifiability and classical identifiability by Pearl [1995] through decomposing the general identifiability problem into a series of classical identifiability sub-problems.
High-dimensional SO(4)-symmetric Rydberg manifolds for quantum simulation

We develop a toolbox for manipulating arrays of Rydberg atoms prepared in high-dimensional hydrogen-like manifolds in the regime of linear Stark and Zeeman effect. We exploit the SO(4) symmetry to characterize the action of static electric and magnetic fields as well as microwave and optical fields on the well-structured manifolds of states with principal quantum number n. This enables us to construct generalized large-spin Heisenberg models for which we develop state-preparation and readout schemes. Due to the available large internal Hilbert space, these models provide a natural framework for the quantum simulation of quantum field theories, which we illustrate for the case of the sine-Gordon and massive Schwinger models. Moreover, these high-dimensional manifolds also offer the opportunity to perform quantum information processing operations for qudit-based quantum computing, which we exemplify with an entangling gate and a state-transfer protocol for the states in the neighborhood of the circular Rydberg level.
Progressive Purification for Instance-Dependent Partial Label Learning

Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct. In the last few years, the instance-independent generation process of candidate labels has been extensively studied, on the basis of which many theoretical advances have been made in PLL. Nevertheless, the candidate labels are always instance-dependent in practice and there is no theoretical guarantee that the model trained on the instance-dependent PLL examples can converge to an ideal one. In this paper, a theoretically grounded and practically effective approach named POP, i.e. PrOgressive Purification for instance-dependent partial label learning, is proposed. Specifically, POP updates the learning model and purifies each candidate label set progressively in every epoch. Theoretically, we prove that POP enlarges the region appropriately fast where the model is reliable, and eventually approximates the Bayes optimal classifier with mild assumptions. Technically, POP is flexible with arbitrary PLL losses and could improve the performance of the previous PLL losses in the instance-dependent case. Experiments on the benchmark datasets and the real-world datasets validate the effectiveness of the proposed method.
Quantum phase measurement of two-qubit states in an open waveguide

We present a method for quantum state tomography of a speciﬁc class of two-qubit states in an open waveguide. The system under investigation consists of three qubits in an open waveguide, separated by a distance comparable to the wavelength of the electromagnetic ﬁeld. We show that the modulation of the frequency of the central ancillary qubit leads to a one-to-one correspondence between the phase in the oﬀ-diagonal density matrix element in the single-excitation subspace of the other two qubits and the result of projective measurements in the σ z -basis.
On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting

The availability of large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a training-from-scratch to a fine-tuning paradigm. While in some applications the goal is to"nudge"the pre-trained distribution towards preferred outputs, in others it is to steer it towards a different distribution over the sample space. Two main paradigms have emerged to tackle this challenge: Reward Maximization (RM) and, more recently, Distribution Matching (DM). RM applies standard Reinforcement Learning (RL) techniques, such as Policy Gradients, to gradually increase the reward signal. DM prescribes to first make explicit the target distribution that the model is fine-tuned to approximate. Here we explore the theoretical connections between the two paradigms, and show that methods such as KL-control developed for RM can also be construed as belonging to DM. We further observe that while DM differs from RM, it can suffer from similar training difficulties, such as high gradient variance. We leverage connections between the two paradigms to import the concept of baseline into DM methods. We empirically validate the benefits of adding a baseline on an array of controllable language generation tasks such as constraining topic, sentiment, and gender distributions in texts sampled from a language model. We observe superior performance in terms of constraint satisfaction, stability and sample efficiency.
Quantum thermodynamics with fast driving and strong coupling via the mesoscopic leads approach

Understanding the thermodynamics of driven quantum systems strongly coupled to thermal baths is a central focus of quantum thermodynamics and mesoscopic physics. A variety of different methodological approaches exist in the literature, all with their own advantages and disadvantages. The mesoscopic leads approach was recently generalised to steady state thermal machines and has the ability to replicate Landauer B\"uttiker theory in the non-interacting limit. In this approach a set of discretised lead modes, each locally damped, provide a markovian embedding for the baths. In this work we further generalise this approach to incorporate an arbitrary time dependence in the system Hamiltonian. Following a careful discussion of the calculation of thermodynamic quantities we illustrate the power of our approach by studying several driven mesoscopic examples coupled to finite temperature fermionic baths, replicating known results in various limits. In the case of a driven non interacting quantum dot we show how fast driving can be used to induce heat rectification.
Understanding Mechanisms of Raveling to Extend Open Graded Friction Course (OGFC) Service Life

To understand the mechanisms of raveling in open graded friction course (OGFC) mixtures, this project was divided into experimental measurements and finite element (FE) modeling. For the experimental part, mixtures with good and poor field performance with respect to raveling were selected and some variations were added. A field visit and forensic analysis of cores obtained from the corresponding field projects revealed poor drainability, lower binder content, and finer gradation than the mix design requirements. The aggregates and binders were characterized using standard and advanced techniques to determine their propensity to raveling. The six mixtures included in the study were also characterized in the laboratory using permeability, Cantabro loss, indirect tensile (IDT) strength, and Hamburg wheel tracking test. The Cantabro loss test was the best predictor of the durability of the mixtures when compared to observed field performance. American Association of State Highway and Transportation Officials (AASHTO) T 283 and the moisture induced stress tester (MIST) device were used to evaluate the effect of moisture on the mixtures' IDT strength and Cantabro loss. All mixtures showed minimal impact from the conditioning protocols, especially MIST conditioning. A finite element numerical model was also developed to better identify the mechanisms associated with the initiation and progression of raveling in OGFC. The two-dimensional model consisted of a moving wheel load passing over a typical OGFC layer on top of a regular pavement structure. The OGFC was modeled using microstructures obtained with X-ray computed tomography techniques. Various parameters related to the OGFC mixture (i.e., internal factors) and to climatic, traffic, and pavement conditions (i.e., external factors) were evaluated. The results were analyzed using probabilistic principles and suggested that the most relevant mixture-related factors increasing the chances of raveling are binder content and air void content. Extreme temperatures, high traffic loads, and slow speed vehicles were also identified as detrimental, while the structural capacity of the pavement located underneath the OGFC was identified as the least significant parameter in promoting raveling.
Coarse Wage-Setting and Behavioral Firms

This paper shows that the bunching of wages at round numbers is partly driven by firm coarse wage-setting. Using data on 280 million new hires from Brazil, I first establish that salaries tend to cluster at round numbers. Then, I show that firms that tend to hire workers at round-numbered salaries are less sophisticated and have worse market outcomes. Next, I develop a wage-posting model in which optimization costs lead to the adoption of coarse rounded wages and provide evidence supporting three model predictions using two research designs. Finally, I examine some consequences of coarse wage-setting for relevant economic outcomes.
Multi-stage Moving Target Defense: A Security-enhanced D-FACTS Implementation Approach

In recent studies, moving target defense (MTD) has been applied to detect false data injection (FDI) attacks using distributed flexible AC transmission system (D-FACTS) devices. However, the inherent conflict between the security goals of MTD (i.e., detecting FDI attacks) and the economic goals of D-FACTS devices (i.e., reducing power losses) would impede the application of MTD in real systems. Moreover, the detection capabilities of existing MTDs are often insufficient. This paper proposes a multi-stage MTD (MMTD) approach to resolve these two issues by adding a group of designed security-oriented schemes before D-FACTS' economic-oriented scheme to detect FDI attacks. We keep these security-oriented schemes for a very short time interval and then revert to the economic-oriented scheme for the remaining time to ensure the economic requirements. We prove that a designed MMTD can significantly improve the detection capability compared to existing one-stage MTDs. We find the supremum of MMTD's detection capability and study its relationship with system topology and D-FACTS deployment. Meanwhile, a greedy algorithm is proposed to search the MMTD strategy to reach this supremum. Simulation results show that the proposed MMTD can achieve the supremum against FDI attacks while outperforming current MTD strategies on economic indicators.
Integrating Unikernel Optimizations in a General Purpose OS

We explore if unikernel techniques can be integrated into a general-purposeOSwhilepreservingitsbattle-testedcode,de-velopmentcommunity,andecosystemoftools,applications, andhardwaresupport.Ourprototypedemonstratesbotha pathtointegrateunikerneltechniquesinLinuxandthatsuch techniquescanresultinsignificantperformanceadvantages. Withare-compilationandlinktoourmodifiedkernel,applica-tionsshowmodestperformancegains.Expertdeveloperscan optimizetheapplicationtocallinternalkernelfunctionality andoptimizeacrosstheapplication/kernelboundaryformore significantgains.Whileonlyoneprocesscanbeoptimized, standardscriptscanbeusedtolaunchit,andotherprocesses canrunalongsideit,enablingtheuseofstandarduser-level tools(prof,bash,.)andsupportforbothvirtualandphysical servers.ThechangestotheLinuxkernelaremodest(1250 LOC)andlargelypartofaconfigurationbuildtarget.
Stochastic Deep-Ritz for Parametric Uncertainty Quantification

Scientific machine learning has become an increasingly important tool in materials science and engineering. It is particularly well suited to tackle material problems involving many variables or to allow rapid construction of surrogates of material models, to name just a few. Mathematically, many problems in materials science and engineering can be cast as variational problems. However, handling of uncertainty, ever present in materials, in the context of variational formulations remains challenging for scientific machine learning. In this article, we propose a deep-learning-based numerical method for solving variational problems under uncertainty. Our approach seamlessly combines deep-learning approximation with Monte-Carlo sampling. The resulting numerical method is powerful yet remarkably simple. We assess its performance and accuracy on a number of variational problems.