Miracle-dz/UniFAR-FTUs huggingface dataset:
Going... going... wrong: a test of the level-k (and cognitive hierarchy) models of bidding behaviour

In this paper, we design and implement an experiment aimed at testing the level-k model of auctions. We begin by asking which (simple) environments can best disentangle the level-k model from its leading rival, Bayes-Nash equilibrium. We find two environments that are particularly suited to this purpose: an all-pay auction with uniformly distributed values, and a first-price auction with the possibility of cancelled bids. We then implement both of these environments in a virtual laboratory in order to see which theory can best explain observed bidding behaviour. We find that, when plausibly calibrated, the level-k model substantially under-predicts the observed bids and is clearly out-performed by equilibrium. Moreover, attempting to fit the level-k model to the observed data results in implausibly high estimated levels, which in turn bear no relation to the levels inferred from a game known to trigger level-k reasoning. Finally, subjects almost never appeal to iterated reasoning when asked to explain how they bid. Overall, these findings suggest that, despite its notable success in predicting behaviour in other strategic settings, the level-k model (and its close cousin cognitive hierarchy) cannot explain behaviour in auctions.
Identifying Social Effects with Economic Experiments

A growing literature indicates that social effects, in which the behavior of an individual reflects that of hers peers or reference group, can have many and far-reaching economic implications. However, identification of social effects is difficult because behavior of an individual and her reference group will tend to be correlated even in the absence of social effects. In an effort to resolve these problems, this study presents a novel experimental design based on the implementation of multiple, socially heterogeneous, anonymous experimental sessions within a set of 30 small Honduran communities. By randomly assigning subjects to roles and experimental sessions we radically improve the identification of social effects. Our results reveal the existence of significant social effects even when no strategic interaction or coordination within experimental sessions was possible. For example, people tend to be more trusting when surrounded by men. At an individual level, people who are more well established in the community are more generous and trusting, and they are even more generous and trusting when surrounded by other well-established people. While some of our results are consistent with attempts at imitation, the evidence reveals the presence of non-imitation contextual effects also. That is, the social context might affect individual behavior through several mechanisms simultaneously. Our results show not only that simple experimental designs can help resolve difficult identification issues, but also that the heterogeneity naturally found in the population at large is crucial in this task. To our knowledge, this is the first time that these kind of social effects have been successfully identified in the experimental literature. JEL Codes: C93, Z13 0* Castillo: School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA 30332, USA; Carter: Department of Agricultural and Applied Economics, University of Wisconsin-Madison, 427 Lorch Street, Madison, WI 53706-1503, USA.
EFFECT OF WORKPLACE STATUS ON GREEN CREATIVITY: AN EMPIRICAL STUDY

In organizations, green creativity of the employees is an essential part of implementing sustainable development. Our research proposed that workplace status has a significant impact on green creativity. Through an empirical study, we came to the conclusion that an increase in workplace status can lead to green creativity and prosocial motivation mediates the process. Since workplace status promotes the attention for group interests, employees will evaluate their self-worth considering the sustainable development of the environment. Based on the analysis framework of moderated mediation model, this study indicated that organization identification had a positive moderating effect on the relationship between workplace status and green creativity, and it is the boundary condition for workplace status to be effective.
Velocity Dispersion in Sandstone Under Partially Saturated Conditions

Seismic Rock Physics is an interdisciplinary study as a “bridge” linking seismic data (attribute parameters) to oil reservior property (elastic parameters). At present, the ultrasonic transmission method is the mostly used laboratory measurement technique to determine the acoustic properties of fluid-saturated reservoir rocks, and further dispersion studies are implemented via the ultrasonic method-based measurements. However, the frequency of seismic data is less than 100Hz, which is obviously different from the laboratory ultrasonic band, and the seismic response characteristics of the reservoir rock under different frequencies also show different characteristics. Many theories have been developed to predict and interpret the seismic wave dispersion in fluid-saturated reservoir rocks. These theoretical models play important roles in understanding the dispersion mechanism on seismic wave propagation, however, still remain unconstrained by experimental data due to the scarcity of laboratory measurements, especially at low seismic frequency range.
Modi(cid:12)ed tumor diagnosis by classi(cid:12)cation and use of canonical correlation and support vector machines methods

The main objective of this research is to investigate techniques for classifying tumor grades based on image processing. The algorithms used to classify tumors are introduced, and their performance in the experimental results are evaluated. In the proposed algorithm, (cid:12)rst, the scan images of the lung are pre-processed and then, the histogram, texture, and geometric features are extracted. These characteristics are then employed in Support Vector Machines (SVM) and Canonical Correlation Analysis (CCA) classi(cid:12)ers to diagnose tumors and classify benign and malignant types. These integrated approaches to investigation of medical images are vital tools for improving the diagonalization accuracy. In the current research, experimental and simulated medical images are employed. The outcomes of the developed techniques in this research are compared with those found in the literature review to con(cid:12)rm the e(cid:14)cacy and reliability of the proposed approach in diagnosing and classifying tumors. In addition to high accuracy in diagnosis, this method is also a low-cost and low-risk method. Owing to its very high sensitivity, this method has the desired values of two criteria of precision and speci(cid:12)city as well as the small number of features used for classi(cid:12)cation; therefore, the developed method was proposed as an e(cid:14)cient and appropriate one for tumor classi(cid:12)cation.
Histological and ultrastructural studies of the pancreas of native rabbits (Oryctolagus cuniculus)

The pancreas of male adult native rabbit was studied by using light microscope (LM) and transmission electron microscope (TEM). The pancreas possessed both exocrine and endocrine portions. Exocrine portion included both acinar and ductal cells. The acinar cells were pyramidal in shaped which formed the lining epithelia of secretory units (acini). The apical cytoplasm of acinar cells contained unequal size and variable electron opacity of zymogen granules, while the basal part occupied by welldeveloped rough endoplasm reticulum (rER). Short microvilli projected from the free surfaces of both acinar and ductal cells. The lateral cell membranes of adjacent acinar and ductal cells adhere apically by junctional complex, which was composed of zonulae occludens and zonulae adherens. There was notable interdigitating between the lateral cell membranes of neighboring ductal cells that were located beneath the junctional complex. The oval or irregular pancreatic islets consisted of β, α and δ endocrine cells. Both βand α-cells could be demonstrated by using LM and TEM, whereas δ-cells visualized with TEM only. The cytoplasm of β-cells contained purple granules and α-cells showed yellowish granules in their cytoplasm when stained with aldehyde fuchsine method. At ultrastructure level, the cytoplasmic granules of β-cells were identified by homogenous electron dense core with clear hallo zone, the numerous granules of α-cells had very electron dense granules and δ-cells contained few granules similar to those of α-cells. βor α-cells inserted as a single cell and in a partial manner between the ductal cells of some ducts.
Channel Estimation and Data Detection Analysis of Massive MIMO With 1-Bit ADCs

We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i. d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation considering the state-of-the-art linear minimum MSE estimator and the class of scaled least-squares estimators. For the data detection, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement minimum distance detection and, potentially, to design the set of transmit symbols. Our analytical findings explicitly depend on key system parameters such as the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length, thus enabling a precise characterization of the performance of the channel estimation and the data detection with 1-bit ADCs. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level significantly enhances the system performance.
The Dayside Ionopause of Mars: Solar Wind Interaction, Pressure Balance, and Comparisons With Venus

Due to the lower ionospheric thermal pressure and existence of the crustal magnetism at Mars, the Martian ionopause is expected to behave differently from the ionopause at Venus. We study the solar wind interaction and pressure balance at the ionopause of Mars using both in situ and remote sounding measurements from the Mars Advanced Radar for Subsurface and Ionosphere Sounding instrument on the Mars Express orbiter. We show that the magnetic pressure usually dominates the thermal pressure to hold off the solar wind at the ionopause at Mars, with only 13% of the cases where the ionospheric thermal pressure plays a more important role in pressure balance. This percentage at Venus, however, is up to 65%. We also find that the ionopause altitude at Mars decreases as the normal component of the solar wind dynamic pressure increases, similar to the altitude variation of the ionopauses at Venus. Moreover, our results show that the ionopause thickness at Mars and Venus is mainly determined by the ion gyromotion and is equivalent to about five ion gyroradii.
The TREX Survey: Kinematical Complexity Throughout M33's Stellar Disk and Evidence for a Stellar Halo

We present initial results from a large spectroscopic survey of stars throughout M33's stellar disk. We analyze a sample of 1667 red giant branch (RGB) stars extending to projected distances of ∼11 kpc from M33's center (∼18 kpc, or ∼10 scale lengths, in the plane of the disk). The line-of-sight velocities of RGB stars show the presence of two kinematical components. One component is consistent with rotation in the plane of M33's H i disk and has a velocity dispersion (∼19 km s−1), consistent with that observed in a comparison sample of younger stars, while the second component has a significantly higher velocity dispersion. A two-component fit to the RGB velocity distribution finds that the high-dispersion component has a velocity dispersion of 59.3−2.5+2.6 km s−1 and rotates very slowly in the plane of the disk (consistent with no rotation at the <1.5σ level), which favors interpreting it as a stellar halo rather than a thick disk population. A spatial analysis indicates that the fraction of RGB stars in the high-velocity-dispersion component decreases with increasing radius over the range covered by the spectroscopic sample. Our spectroscopic sample establishes that a significant high-velocity-dispersion component is present in M33's RGB population from near M33's center to at least the radius where M33's H i disk begins to warp at 30′ (∼7.5 kpc) in the plane of the disk. This is the first detection and spatial characterization of a kinematically hot stellar component throughout M33's inner regions.
Angle Diversity Trasmitter For High Speed Data Center Uplink Communications

This paper proposes an uplink optical wireless communication (OWC) link design that can be used by data centers to support communication in spine and leaf architectures between the top of rack leaf switches and large spine switches whose access points are mounted in the ceiling. The use of optical wireless links reduces cabling and allows easy reconfigurability for example when data centres expand. We consider three racks in a data center where each rack contains an Angle Diversity Transmitter (ADT) positioned on the top of the rack to realize the uplink function of a “top-of-the-rack (ToR)” or a “leaf” switch. Four receivers are considered to be installed on the ceiling where each is connected to a spine switch. Two types of optical receivers are studied which are a Wide Field-of- View Receiver (WFOVR) and an Angle Diversity Receiver (ADR). The performance of the proposed system is evaluated when the links run at data rates higher than 19 Gbps. The results indicate that the proposed approach achieves excellent performance using simple On-Off Keying (OOK) modulation.
On Label Shift in Domain Adaptation via Wasserstein Distance

We study the label shift problem between the source and target domains in general domain adaptation (DA) settings. We consider transformations transporting the target to source domains, which enable us to align the source and target examples. Through those transformations, we define the label shift between two domains via optimal transport and develop theory to investigate the properties of DA under various DA settings (e.g., closed-set, partial-set, open-set, and universal settings). Inspired from the developed theory, we propose Label and Data Shift Reduction via Optimal Transport (LDROT) which can mitigate the data and label shifts simultaneously. Finally, we conduct comprehensive experiments to verify our theoretical findings and compare LDROT with state-of-the-art baselines.
Adaptive Importance Sampling meets Mirror Descent : a Bias-variance Tradeoff

Adaptive importance sampling is a widely spread Monte Carlo technique that uses a re-weighting strategy to iteratively estimate the so-called target distribution. A major drawback of adaptive importance sampling is the large variance of the weights which is known to badly impact the accuracy of the estimates. This paper investigates a regularization strategy whose basic principle is to raise the importance weights at a certain power. This regularization parameter, that might evolve between zero and one during the algorithm, is shown (i) to balance between the bias and the variance and (ii) to be connected to the mirror descent framework. Using a kernel density estimate to build the sampling policy, the uniform convergence is established under mild conditions. Finally, several practical ways to choose the regularization parameter are discussed and the benefits of the proposed approach are illustrated empirically.
2D-2FA: A New Dimension in Two-Factor Authentication

We propose a two-factor authentication (2FA) mechanism called 2D-2FA to address security and usability issues in existing methods. 2D-2FA has three distinguishing features: First, after a user enters a username and password on a login terminal, a unique identifier is displayed to her. She inputs the same identifier on her registered 2FA device, which ensures appropriate engagement in the authentication process. Second, a one-time PIN is computed on the device and automatically transferred to the server. Thus, the PIN can have very high entropy, making guessing attacks infeasible. Third, the identifier is also incorporated into the PIN computation, which renders concurrent attacks ineffective. Third-party services such as push-notification providers and 2FA service providers, do not need to be trusted for the security of the system. The choice of identifiers depends on the device form factor and the context. Users could choose to draw patterns, capture QR codes, etc. We provide a proof of concept implementation, and evaluate performance, accuracy, and usability of the system. We show that the system offers a lower error rate (about half) and better efficiency (2-3 times faster) compared to the commonly used PIN-2FA. Our study indicates a high level of usability with a SUS of 75, and a high perception of efficiency, security, accuracy, and adoptability.
Mobile ion in a Fermi sea

Esben Rohan Christensen, Arturo Camacho-Guardian, and Georg M. Bruun 3 Center for Complex Quantum Systems, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, DK-8000 Aarhus C, Denmark. T.C. M. Group, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, U.K. Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China. (Dated: November 1, 2021)
Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels

This paper focuses on camouflaged object detection (COD), which is a task to detect objects hidden in the background. Most of the current COD models aim to highlight the target object directly while outputting ambiguous camouflaged boundaries. On the other hand, the performance of the models considering edge information is not yet satisfactory. To this end, we propose a new framework that makes full use of multiple visual cues, i.e., saliency as well as edges, to refine the predicted camouflaged map. This framework consists of three key components, i.e., a pseudo-edge generator, a pseudo-map generator, and an uncertainty-aware refinement module. In particular, the pseudo-edge generator estimates the boundary that outputs the pseudo-edge label, and the conventional COD method serves as the pseudo-map generator that outputs the pseudo-map label. Then, we propose an uncertainty-based module to reduce the uncertainty and noise of such two pseudo labels, which takes both pseudo labels as input and outputs an edge-accurate camouflaged map. Experiments on various COD datasets demonstrate the effectiveness of our method with superior performance to the existing state-of-the-art methods.
Nonsmooth mean field games with state constraints

In this paper, we consider a mean field game model inspired by crowd motion where agents aim to reach a closed set, called target set, in minimal time. Congestion phenomena are modeled through a constraint on the velocity of an agent that depends on the average density of agents around their position. The model is considered in the presence of state constraints: roughly speaking, these constraints may model walls, columns, fences, hedges, or other kinds of obstacles at the boundary of the domain which agents cannot cross. After providing a more detailed description of the model, the paper recalls some previous results on the existence of equilibria for such games and presents the main difficulties that arise due to the presence of state constraints. Our main contribution is to show that equilibria of the game satisfy a system of coupled partial differential equations, known mean field game system, thanks to recent techniques to characterize optimal controls in the presence of state constraints. These techniques not only allow to deal with state constraints but also require very few regularity assumptions on the dynamics of the agents.
LegalNLP - Natural Language Processing methods for the Brazilian Legal Language

We present and make available pre-trained language models (Phraser, Word2Vec, Doc2Vec, FastText, and BERT) for the Brazilian legal language, a Python package with functions to facilitate their use, and a set of demonstrations/tutorials containing some applications involving them. Given that our material is built upon legal texts coming from several Brazilian courts, this initiative is extremely helpful for the Brazilian legal field, which lacks other open and specific tools and language models. Our main objective is to catalyze the use of natural language processing tools for legal texts analysis by the Brazilian industry, government, and academia, providing the necessary tools and accessible material.
A Pre-processing Method for Fairness in Ranking

Fair ranking problems arise in many decision-making processes that often necessitate a trade-off between accuracy and fairness. Many existing studies have proposed correction methods such as adding fairness constraints to a ranking model's loss. However, the challenge of correcting the data bias for fair ranking remains, and the trade-off of the ranking models leaves room for improvement. In this paper, we propose a fair ranking framework that evaluates the order of training data in a pairwise manner as well as various fairness measurements in ranking. This study is the first proposal of a pre-processing method that solves fair ranking problems using the pairwise ordering method with our best knowledge. The fair pairwise ordering method is prominent in training the fair ranking models because it ensures that the resulting ranking likely becomes parity across groups. As far as the fairness measurements in ranking are represented as a linear constraint of the ranking models, we proved that the minimization of loss function subject to the constraints is reduced to the closed solution of the minimization problem augmented by weights to training data. This closed solution inspires us to present a practical and stable algorithm that iterates the optimization of weights and model parameters. The empirical results over real-world datasets demonstrated that our method outperforms the existing methods in the trade-off between accuracy and fairness over real-world datasets and various fairness measurements.
10 Security and Privacy Problems in Self-Supervised Learning

Self-supervised learning has achieved revolutionary progress in the past several years and is commonly believed to be a promising approach for general-purpose AI. In particular, self-supervised learning aims to pre-train an encoder using a large amount of unlabeled data. The pre-trained encoder is like an “operating system” of the AI ecosystem. Specifically, the encoder can be used as a feature extractor for many downstream tasks with little or no labeled training data. Existing studies on self-supervised learning mainly focused on pre-training a better encoder to improve its performance on downstream tasks in non-adversarial settings, leaving its security and privacy in adversarial settings largely unexplored. A security or privacy issue of a pre-trained encoder leads to a single point of failure for the AI ecosystem. In this book chapter, we discuss 10 basic security and privacy problems for the pre-trained encoders in self-supervised learning, including six confidentiality problems, three integrity problems, and one availability problem. For each problem, we discuss potential opportunities and challenges. We hope our book chapter will inspire future research on the security and privacy of self-supervised learning.