Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Effect-Size Bias in Ridehailing: Measuring Social Bias in Dynamic Pricing of 100 Million Rides

Algorithmic bias is the systematic preferential or discriminatory treatment of a group of people by an artificial intelligence system. In this work we develop a random-effects based metric for the analysis of social bias in supervised machine learning prediction models where model outputs depend on U.S. locations. We define a methodology for using U.S. Census data to measure social bias on user attributes legally protected against discrimination, such as ethnicity, sex, and religion, also known as protected attributes. We evaluate our method on the Strategic Subject List (SSL) gun-violence prediction dataset, where we have access to both U.S. Census data as well as ground truth protected attributes for 224,235 individuals in Chicago being assessed for participation in future gun-violence incidents. Our results indicate that quantifying social bias using U.S. Census data provides a valid approach to auditing a supervised algorithmic decision-making system. Using our methodology, we then quantify the potential social biases of 100 million ridehailing samples in the city of Chicago. This work is the first large-scale fairness analysis of the dynamic pricing algorithms used by ridehailing applications. An analysis of Chicago ridehailing samples in conjunction with American Community Survey data indicates possible disparate impact due to social bias based on age, house pricing, education, and ethnicity in the dynamic fare pricing models used by ridehailing applications, with effect-sizes of 0.74, 0.70, 0.34, and -0.31 (using Cohen's d) for each demographic respectively. Further, our methodology provides a principled approach to quantifying algorithmic bias on datasets where protected attributes are unavailable, given that U.S. geolocations and algorithmic decisions are provided.
Contextual Bandits with Side-Observations

We investigate contextual bandits in the presence of side-observations across arms in order to design recommendation algorithms for users connected via social networks. Users in social networks respond to their friends' activity, and hence provide information about each other's preferences. In our model, when a learning algorithm recommends an article to a user, not only does it observe his/her response (e.g. an ad click), but also the side-observations, i.e., the response of his neighbors if they were presented with the same article. We model these observation dependencies by a graph $\mathcal{G}$ in which nodes correspond to users, and edges correspond to social links. We derive a problem/instance-dependent lower-bound on the regret of any consistent algorithm. We propose an optimization (linear programming) based data-driven learning algorithm that utilizes the structure of $\mathcal{G}$ in order to make recommendations to users and show that it is asymptotically optimal, in the sense that its regret matches the lower-bound as the number of rounds $T\to\infty$. We show that this asymptotically optimal regret is upper-bounded as $O\left(|\chi(\mathcal{G})|\log T\right)$, where $|\chi(\mathcal{G})|$ is the domination number of $\mathcal{G}$. In contrast, a naive application of the existing learning algorithms results in $O\left(N\log T\right)$ regret, where $N$ is the number of users.
Detecting Problem Statements in Peer Assessments

Effective peer assessment requires students to be attentive to the deficiencies in the work they rate. Thus, their reviews should identify problems. But what ways are there to check that they do? We attempt to automate the process of deciding whether a review comment detects a problem. We use over 18,000 review comments that were labeled by the reviewees as either detecting or not detecting a problem with the work. We deploy several traditional machine-learning models, as well as neural-network models using GloVe and BERT embeddings. We find that the best performer is the Hierarchical Attention Network classifier, followed by the Bidirectional Gated Recurrent Units (GRU) Attention and Capsule model with scores of 93.1% and 90.5% respectively. The best non-neural network model was the support vector machine with a score of 89.71%. This is followed by the Stochastic Gradient Descent model and the Logistic Regression model with 89.70% and 88.98%.
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted from a real-world business solution. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a large number (50 to 1000) of products with shared capacity, (ii) we consider a multi-node supply chain consisting of a warehouse which supplies three stores, (iii) the warehouse, stores, and transportation from warehouse to stores have finite capacities, (iv) warehouse and store replenishment happen at different time scales and with realistic time lags, and (v) demand for products at the stores is stochastic. We describe a novel formulation in a multi-agent (hierarchical) reinforcement learning framework that can be used for parallelised decision-making, and use the advantage actor critic (A2C) algorithm with quantised action spaces to solve the problem. Experiments show that the proposed approach is able to handle a multi-objective reward comprised of maximising product sales and minimising wastage of perishable products.
Condition numbers for the cube. I: Univariate polynomials and hypersurfaces

The condition-based complexity analysis framework is one of the gems of modern numerical algebraic geometry and theoretical computer science. One of the challenges that it poses is to expand the currently limited range of random polynomials that we can handle. Despite important recent progress, the available tools cannot handle random sparse polynomials and Gaussian polynomials, that is polynomials whose coefficients are i.i. d. Gaussian random variables. We initiate a condition-based complexity framework based on the norm of the cube, that is a step in this direction. We present this framework for real hypersurfaces. We demonstrate its capabilities by providing a new probabilistic complexity analysis for the Plantinga-Vegter algorithm, which covers both random sparse (alas a restricted sparseness structure) polynomials and random Gaussian polynomials. We present explicit results with structured random polynomials for problems with two or more dimensions. Additionally, we provide some estimates of the separation bound of a univariate polynomial in our current framework.
Properties of brightest group galaxies in cosmic web filaments

Context. The cosmic web, a complex network of galaxy groups and clusters connected by filaments, is a dynamical environment in which galaxies form and evolve. However, the impact of cosmic filaments on the properties of galaxies is difficult to study because of the much more influential local (galaxy-group scale) environment. Aims. The aim of this paper is to investigate the dependence of intrinsic galaxy properties on distance to the nearest cosmic web filament, using a sample of galaxies for which the local environment is easily assessable. Methods. Our study is based on a volume-limited galaxy sample with Mr ≤ −19 mag, drawn from the SDSS DR12. We chose brightest group galaxies (BGGs) in groups with two to six members as our probes of the impact of filamentary environment because their local environment can be determined more accurately. We use the Bisous marked point process method to detect cosmic-web filaments with radii of 0.5−1.0 Mpc and measure the perpendicular filament spine distance (Dfil) for the BGGs. We limit our study to Dfil values up to 4 Mpc. We use the luminosity density field as a tracer of the local environment. To achieve uniformity of the sample and to reduce potential biases we only consider filaments longer than 5 Mpc. Our final sample contains 1427 BGGs. Results. We note slight deviations between the galaxy populations inside and outside the filament radius in terms of stellar mass, colour, the 4000 Å break, specific star formation rates, and morphologies. However, all these differences remain below 95% confidence and are negligible compared to the effects arising from local environment density. Conclusions. Within a 4 Mpc radius of the filament axes, the effect of filaments on BGGs is marginal. The local environment is the main factor in determining BGG properties.
New Axymyiidae (Insecta: Diptera) from the Mesozoic of East Siberia

ABSTRACT The new species of Axymyiidae Juraxymyia krzeminskii sp. n. and Sinaxymyia szadziewskii sp. n. from the Lower Cretaceous beds of Transbaikalia and Juraxymyia evae sp. n. from the Lower Cretaceous beds (Upper Jurassic not excluded) of Yakutia are described.
The Casimir effect for nonlinear sigma models and the Mermin–Wagner–Hohenberg–Coleman theorem

The quantum vacuum (Casimir) energy arising from noninteracting massless quanta is known to induce a long-range force, while decays exponentially for massive fields and separations larger than the inverse mass of the quanta involved. Here, we show that the interplay between dimensionality and nonlinearities in the field theory alters this behaviour in a nontrivial way. We argue that the changes are intimately related to the Mermin–Wagner–Hohenberg–Coleman theorem, and illustrate this situation using a nonlinear sigma model as a working example. We compute the quantum vacuum energy, which consists of the usual Casimir contribution plus a semiclassical contribution, and find that the vacuum-induced force is long-ranged at large distance, while displays a complex behaviour at small separations. Finally, even for this relatively simple set-up, we show that nonlinearities are generally responsible for modulations in the force as a function of the coupling constant and the temperature.
Toward a Quantitative General Equilibrium Asset Pricing Model with Strategic Competition

We develop a novel general-equilibrium framework with concentrated industries, consumer inertia, and strategic competition, in which competition intensity endogenously intensifies as the expected growth declines or the discount rate rises, because firms compete more aggressively for current cash flows by undercutting each other as the present value of future cooperation decreases. We quantitatively decompose the beta of stock returns with the consumption risk into two components, reflecting news about the expected growth and discount rate. In equilibrium, more profitable industries have higher exposure to the discount-rate component of consumption risk because of higher consumer inertia, while value industries have higher exposure to the expected-growth component. More important, after controlling for the book-tomarket ratio, the gross profitability premium becomes more pronounced. Finally, we exploit exogenous variation in market structures and growth loadings, such as large tariff cuts and natural disasters, to test the core competition and growth mechanisms directly.
Semantic Graph-enhanced Visual Network for Zero-shot Learning

Zero-shot learning uses semantic attributes to connect the search space of unseen objects. In recent years, although the deep convolutional network brings powerful visual modeling capabilities to the ZSL task, its visual features have severe pattern inertia and lack of representation of semantic relationships, which leads to severe bias and ambiguity. In response to this, we propose the Graph-based Visual-Semantic Entanglement Network to conduct graph modeling of visual features, which is mapped to semantic attributes by using a knowledge graph, it contains several novel designs: 1. it establishes a multi-path entangled network with the convolutional neural network (CNN) and the graph convolutional network (GCN), which input the visual features from CNN to GCN to model the implicit semantic relations, then GCN feedback the graph modeled information to CNN features; 2. it uses attribute word vectors as the target for the graph semantic modeling of GCN, which forms a self-consistent regression for graph modeling and supervise GCN to learn more personalized attribute relations; 3. it fuses and supplements the hierarchical visual-semantic features refined by graph modeling into visual embedding. By promoting the semantic linkage modeling of visual features, our method outperforms state-of-the-art approaches on multiple representative ZSL datasets: AwA2, CUB, and SUN.
DINO: Distributed Newton-Type Optimization Method

We present a novel communication-efficient Newton-type algorithm for finite-sum optimization over a distributed computing environment. Our method, named DINO, overcomes both theoretical and practical shortcomings of similar existing methods. Under minimal assumptions, we guarantee global sub-linear convergence of DINO to a first-order stationary point for general non-convex functions and arbitrary data distribution over the network. Furthermore, for functions satisfying Polyak-Lojasiewicz (PL) inequality, we show that DINO enjoys a linear convergence rate. Our proposed algorithm is practically parameter free, in that it will converge regardless of the selected hyper-parameters, which are easy to tune. Additionally, its sub-problems are simple linear least-squares, for which efficient solvers exist. Numerical simulations demonstrate the efficiency of DINO as compared with similar alternatives.
Plasmalemmal μ‐opioid receptor distribution mainly in nondopaminergic neurons in the rat ventral tegmental area

Opiate‐evoked reward and motivated behaviors reflect, in part, the enhanced release of dopamine produced by activation of the μ‐opioid receptor (μOR) in the ventral tegmental area (VTA). We examined the functional sites for μOR activation and potential interactions with dopaminergic neurons within the rat VTA by using electron microscopy for the immunocytochemical localization of antipeptide antisera raised against μOR and tyrosine hydroxylase (TH), the synthesizing enzyme for catecholamines. The cellular and subcellular distribution of μOR was remarkably similar in the two major VTA subdivisions, the paranigral (VTApn) and parabrachial (VTApb) nuclei. In each region, somatodendritic profiles comprised over 50% of the labeled structures. μOR immunolabeling was often seen at extrasynaptic/perisynaptic sites on dendritic plasma membranes, and 10% of these dendrites contained TH. μOR‐immunoreactivity was also localized to plasma membranes of axon terminals and small unmyelinated axons, none of which contained TH. The μOR‐immunoreactive axon terminals formed either symmetric or asymmetric synapses that are typically associated with inhibitory and excitatory amino acid transmitters. Their targets included unlabeled (30%), μOR‐labeled (25%), and TH‐labeled (45%) dendrites. Our results suggest that μOR agonists in the VTA affect dopaminergic transmission mainly indirectly through changes in the postsynaptic responsivity and/or presynaptic release from neurons containing other neurotransmitters. They also indicate, however, that μOR agonists directly affect a small population of dopaminergic neurons expressing μOR on their dendrites in VTA and/or terminals in target regions. Synapse 41:311–328, 2001. © 2001 Wiley‐Liss, Inc.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency of model pre-training and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and pre-trained models will be made publicly available at this https URL.
A comparative analysis of e-scooter and e-bike usage patterns: Findings from the City of Austin, TX

Abstract E-scooter-sharing and e-bike-sharing systems are accommodating and easing the increased traffic in dense cities and are expanding considerably. However, these new micro-mobility transportation modes raise numerous operational and safety concerns. This study analyzes e-scooter and dockless e-bike sharing system user behavior. We investigate how average trip speed change depending on the day of the week and the time of the day. We used a dataset from the city of Austin, TX from December 2018 to May 2019. Results show that the trip average speed for e-bikes ranges between 3.01 and 3.44 m/s, which is higher than that for e-scooters (2.19 to 2.78 m/s). Results also show a similar speed pattern for the average speed of e-bikes and e-scooters throughout the days of the week and a different speed pattern for the average speed of e-bikes and e-scooters over the hours of the day. We found that users tend to ride e-bikes and e-scooters with a slower average speed for recreational purposes compared to when they are ridden for commuting purposes. Results also show that the different users’ speeds in this mixed traffic stream of Austin City should be considered in the policymaking process. It must be recognized that the disparity in speeds might increase the odds of creating the conditions for a crash between different modes in roads. This study is a building block in this field, which serves as a first of its kind, and sheds the light of significant new understanding of this emerging class of shared-road users.
Deep Graph Contrastive Representation Learning

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level. Specifically, we generate two graph views by corruption and learn node representations by maximizing the agreement of node representations in these two views. To provide diverse node contexts for the contrastive objective, we propose a hybrid scheme for generating graph views on both structure and attribute levels. Besides, we provide theoretical justification behind our motivation from two perspectives, mutual information and the classical triplet loss. We perform empirical experiments on both transductive and inductive learning tasks using a variety of real-world datasets. Experimental experiments demonstrate that despite its simplicity, our proposed method consistently outperforms existing state-of-the-art methods by large margins. Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
Investigating Transient Characteristics of Volatile Hysteresis and Self-Heating of PrMnO3-Based RRAM

PrMnO3 (PMO)-based resistive random access memory (RRAM) shows selector-less behavior due to high-nonlinearity. Recently, the nonlinearity, along with volatile hysteresis, is demonstrated and utilized as a compact oscillator to enable highly scaled oscillatory neurons, which enable oscillatory neuromorphic systems found in the human cortex. Hence, it is vital to understand the physical mechanisms behind such a volatile hysteretic behavior to provide useful insights in developing a device for various neuromorphic applications. In this article, we present a detailed investigation of the transient characteristics and propose a physical mechanism to replicate the observations by simulations. First, we investigate the transient switching dynamics of the hysteresis with the voltage ramp rate. We observe that the voltage window initially increases and later decreases as the ramp rate is increased—while the current window reduces monotonically. Second, an analytical electrothermal model based on space charge limited current (SCLC) and 1-D Fourier heat equation with a lumped device model is proposed to model the codependent heat and current flow. Finally, we show that the interplay between the self-heating due to the current and the current dependence on the temperature is accurately modeled to reproduce the hysteresis dependence on the various voltage ramp rate. Such a detailed understanding of the device PMO RRAM volatile hysteresis may enable efficient device design required in neuromorphic computing applications.
Anomalous magnetic suppression of spin relaxation in a two-dimensional electron gas in a GaAs/AlGaAs quantum well

Motion of an electron and precession of its spin are tightly interrelated via the spin-orbit interaction. In a two-dimensional magnetized system, the electron occupies a cyclotron orbit, which limits spin deviation and suppresses spin relaxation. Starting with this classical scenario at low magnetic fields, the authors unexpectedly encounter a peculiar spin relaxation at higher fields. It reveals an anomaly in the electron diffusion that was previously observed in magnetized plasmas and has found no unique explanation so far. In the quantum limit, the authors find a strong enhancement of the longitudinal spin relaxation time when Landau levels are fully occupied by electrons.
On the Color–Metallicity Relation of the Red Clump and the Reddening toward the Magellanic Clouds

The zero point of the reddening toward the Large Magellanic Cloud (LMC) has been the subject of some dispute. Its uncertainty propagates as a systematic error for methods that measure the extragalactic distance scale through knowledge of the absolute extinction of LMC stars. In an effort to resolve this issue, we used three different methods to calibrate the most widely used metric to predict LMC extinction, the intrinsic color of the red clump, (V − I) RC,0, for the inner ∼3° of that galaxy. The first approach was to empirically calibrate the color zero points of the BaSTI isochrones over a wide metallicity range of Δ[Fe/H] ≈ 1.10 using measurements of red clump stars in 47 Tuc, the solar neighborhood, and NGC 6791. From these efforts, we also measure these properties of the solar neighborhood red clump, (V − I, G BP − K s , G − K s , G RP − K s , J − K s , H − K s , M I , M Ks ) RC,0 = (1.02, 2.75, 2.18, 1.52, 0.64, 0.15, −0.23, −1.63). The second and third methods were to compare the observed colors of the red clump to those of Cepheids and RR Lyrae in the LMC. With these three methods, we estimated the intrinsic color of the red clump of the LMC to be (V − I) RC,0,LMC = {≈0.93, 0.91 ± 0.02, 0.89 ± 0.02}, respectively, and similarly, using the first and third methods, we estimated (V − I) RC,0,SMC = {≈0.85, 0.84 ± 0.02}, respectively, for the Small Magellanic Cloud. We estimate the luminosities to be M I,RC,LMC = −0.26 and M I,RC,SMC = −0.37. We show that this has important implications for recent calibrations of the tip of the red giant branch in the Magellanic Clouds used to measure H 0.