Miracle-dz/UniFAR-FTUs huggingface dataset:
Liquid Welfare Guarantees for No-Regret Learning in Sequential Budgeted Auctions

We study the liquid welfare in sequential first-price auctions with budget-limited buyers. We focus on first-price auctions, which are increasingly commonly used in many settings, and consider liquid welfare, a natural and well-studied generalization of social welfare for the case of budget-constrained buyers. We use a behavioral model for the buyers, assuming a learning style guarantee: the resulting utility of each buyer is within a γ factor (where γ ≥ 1) of the utility achievable by shading her value with the same factor at each iteration. Under this assumption, we show a γ + 1/2 + O(1/γ) price of anarchy for liquid welfare assuming buyers have additive valuations. This positive result is in stark contrast to sequential second-price auctions, where even with γ = 1, the resulting liquid welfare can be arbitrarily smaller than the maximum liquid welfare, even though the latter can be achieved by a constant shading factor. We prove a lower bound of γ on the liquid welfare loss under the above assumption in first-price auctions, making our bound asymptotically tight. For the case when γ = 1 our theorem implies a price of anarchy upper bound that is about 2.41; we show a lower bound of 2 for that case. We also give a learning algorithm that the players can use to achieve the guarantee needed for our liquid welfare result. Our algorithm achieves utility within a γ = O(1) factor of the optimal utility even when a buyer's values and the bids of the other buyers are chosen adversarially, assuming the buyer's budget grows linearly with time. The competitiveness guarantee of the learning algorithm deteriorates somewhat as the budget grows slower than linearly with time. Finally, we extend our liquid welfare results for the case where buyers have submodular valuations over the set of items they win across iterations with a slightly worse price of anarchy bound of γ + 1 + O(1/γ) compared to the guarantee for the additive case.
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

Recent advances in the capacity of large language models to generate human-like text have resulted in their increased adoption in user-facing settings. In parallel, these improvements have prompted a heated discourse around the risks of societal harms they introduce, whether inadvertent or malicious. Several studies have explored these harms and called for their mitigation via development of safer, fairer models. Going beyond enumerating the risks of harms, this work provides a survey of practical methods for addressing potential threats and societal harms from language generation models. We draw on several prior works’ taxonomies of language model risks to present a structured overview of strategies for detecting and ameliorating different kinds of risks/harms of language generators. Bridging diverse strands of research, this survey aims to serve as a practical guide for both LM researchers and practitioners, with explanations of different strategies’ motivations, their limitations, and open problems for future research.
Privacy-Preserving Multiview Matrix Factorization for Recommender Systems

With an increasing focus on data privacy, there have been pilot studies on recommender systems in a federated learning (FL) framework, where multiple parties collaboratively train a model without sharing their data. Most of these studies assume that the conventional FL framework can fully protect user privacy. However, there are serious privacy risks in matrix factorization in federated recommender systems based on our study. This article first provides a rigorous theoretical analysis of the server reconstruction attack in four scenarios in federated recommender systems, followed by comprehensive experiments. The empirical results demonstrate that the FL server could infer users' information with accuracy <inline-formula><tex-math notation="LaTeX">$>80\%$</tex-math></inline-formula> based on the uploaded gradients from FL nodes. The robustness analysis suggests that our reconstruction attack analysis outperforms the random guess by <inline-formula><tex-math notation="LaTeX">$>30\%$</tex-math></inline-formula> under Laplace noises with <inline-formula><tex-math notation="LaTeX">$b\leq 0.5$</tex-math></inline-formula> for all scenarios. Then, the article proposes a new privacy-preserving framework based on a threshold variant of homomorphic encryption, privacy-preserving multiview matrix factorization (PrivMVMF), to enhance user data privacy protection in federated recommender systems. The proposed PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.
Serum Array; En metode for høykapasitetsanalyser av serumprøver

Bakgrunnen for prosjektet var behovet for a etablere en metode for hoykapasitetsanalyser (mange prover  fa malinger) av proteiner, pa innsamlet materiale fra norske biobanker. Norsk mikromatrisek .
Counting statistics of single electron transport in bilayer graphene quantum dots

We measure telegraph noise of current fluctuations in an electrostatically defined quantum dot in bilayer graphene by real-time detection of single electron tunneling with a capacitively coupled neighboring quantum dot. Suppression of the second and third cumulant (related to shot noise) in a tunable graphene quantum dot is demonstrated experimentally. With this method we demonstrate the ability to measure very low current and noise levels. Furthermore, we use this method to investigate the first spin excited state, an essential prerequisite to measure spin relaxation.
Continued Fraction Representations of the Generalized Operator Entropy

Abstract The direct calculation of the generalized operator entropy proves to be difficult due to the appearance of rational exponents of matrices. The main motivation of this work is to overcome these difficulties and to present a practical and efficient method for this calculation using its representation by the matrix continued fraction. At the end of our work, we deduce a continued fraction expansion of the Bregman operator divergence. Some numerical examples illustrating the theoretical result are discussed.
Radar observations in Singapore and their implications for the TRMM precipitation radar retrieval algorithms

This paper presents the results and analyses of Doppler radar measurements taken as part of a long‐term campaign conducted in Singapore. Eight months of data taken in the zenith‐pointing mode have been analyzed, in order to examine the validity of the assumptions underlying the retrieval algorithm proposed for the precipitation radar on board the TRMM satellite. Stratiform and convective events were analyzed separately. The former exhibits a clear melting layer in the 4–5 km height region, defined by an enhancement in the radar reflectivity, an increase in the mean fall velocity from 1–2 m/s in the snow region just above the melting layer to over 8 m/s in the rain region just below it. The corresponding width increases from less than 1 m/s to greater than 1 m/s. The variation in all three parameters can be accurately represented by the Noncoalescence‐Nonbreakup melting layer model used for the satellite radar retrieval algorithms. Also examined was the monthly variation of the reflectivity‐height profiles. In the rain region below the melting layer, a consistent decrease in the radar reflectivity with decreasing height is seen, indicating that the drop size distribution (DSD) cannot be assumed constant over all heights. However, if only the parameter N0 of the DSD is assumed constant, then good agreement is obtained between the mean velocity estimated from the reflectivity data and the measured velocity throughout the height profile. The agreement is also obtained in the spectrum width profile in the rain region. Convective precipitation on the other hand, does not exhibit a melting layer, and its averaged dBZ and velocity profiles cannot be characterized even under the constant N0 assumption. Its averaged reflectivity‐height profiles show more variability than stratiform precipitation. For such cases, factors such as up and down drafts and nonstandard atmosphere need to be taken into account. The implication for the so‐called N0 adjustment method is that while it can be applied to stratiform precipitation, the convective events need additional information, such as dual‐frequency radar measurements, in order to improve the accuracy of the rainfall estimates.
Query Rewriting for Effective Misinformation Discovery

We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms. We introduce an adaptable rewriting strategy, where editing actions for queries containing claims (e.g., swap a word with its synonym; change verb tense into present simple) are automatically learned through offline reinforcement learning. Our model uses a decision transformer to learn a sequence of editing actions that maximizes query retrieval metrics such as mean average precision. We conduct a series of experiments showing that our query rewriting system achieves a relative increase in the effectiveness of the queries of up to 42%, while producing editing action sequences that are human interpretable.
A Serverless Publish/Subscribe System

Operating a scalable and reliable server application, such as publish/subscribe (pub/sub) systems, requires tremendous development efforts and resources. The emerging serverless paradigm simplifies the development and deployment of highly available applications by delegating most operational concerns to the cloud providers. The serverless paradigm describes a programming model where the developers break the application downs into smaller microservices that run on the cloud in response to events. This paper proposes designing a serverless pub/sub system based on the IBM Bluemix cloud platform. Our pub/sub system performs topic-based, content-based, and function-based matchings. The function-based matching is a novel matching approach where the subscribers can define a highly customizable subscription function that the broker applies to the publications in the cloud. The evaluations of the designed system verify the practicality of the designed system. However, the vendor-specific constraints of the IBM Bluemix resources are a bottleneck to the scalability of the broker.
Learning Active Camera for Multi-Object Navigation

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently with camera sensors only. Existing navigation methods mainly focus on fixed cameras and few attempts have been made to navigate with active cameras. As a result, the agent may take a very long time to perceive the environment due to limited camera scope. In contrast, humans typically gain a larger field of view by looking around for a better perception of the environment. How to make robots perceive the environment as efficiently as humans is a fundamental problem in robotics. In this paper, we consider navigating to multiple objects more efficiently with active cameras. Specifically, we cast moving camera to a Markov Decision Process and reformulate the active camera problem as a reinforcement learning problem. However, we have to address two new challenges: 1) how to learn a good camera policy in complex environments and 2) how to coordinate it with the navigation policy. To address these, we carefully design a reward function to encourage the agent to explore more areas by moving camera actively. Moreover, we exploit human experience to infer a rule-based camera action to guide the learning process. Last, to better coordinate two kinds of policies, the camera policy takes navigation actions into account when making camera moving decisions. Experimental results show our camera policy consistently improves the performance of multi-object navigation over four baselines on two datasets.
Autoencoder-Aided Visualization of Collections of Morse Complexes

Though analyzing a single scalar field using Morse complexes is well studied, there are few techniques for visualizing a collection of Morse complexes. We focus on analyses that are enabled by looking at a Morse complex as an embedded domain decomposition. Specifically, we target 2D scalar fields, and we encode the Morse complex through binary images of the boundaries of decomposition. Then we use image-based autoencoders to create a feature space for the Morse complexes. We apply additional dimensionality reduction methods to construct a scatterplot as a visual interface of the feature space. This allows us to investigate individual Morse complexes, as they relate to the collection, through interaction with the scatterplot. We demonstrate our approach using a synthetic data set, microscopy images, and time-varying vorticity magnitude fields of flow. Through these, we show that our method can produce insights about structures within the collection of Morse complexes.
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes

Despite their importance for assessing reliability of predictions, uncertainty quantification (UQ) measures for machine learning models have only recently begun to be rigorously characterized. One prominent issue is the curse of dimensionality: it is commonly believed that the marginal likelihood should be reminiscent of cross-validation metrics and that both should deteriorate with larger input dimensions. We prove that by tuning hyperparameters to maximize marginal likelihood (the empirical Bayes procedure), the performance, as measured by the marginal likelihood, improves monotonically} with the input dimension. On the other hand, we prove that cross-validation metrics exhibit qualitatively different behavior that is characteristic of double descent. Cold posteriors, which have recently attracted interest due to their improved performance in certain settings, appear to exacerbate these phenomena. We verify empirically that our results hold for real data, beyond our considered assumptions, and we explore consequences involving synthetic covariates.
Continuous-in-time Limit for Bayesian Bandits

This paper revisits the bandit problem in the Bayesian setting. The Bayesian approach formulates the bandit problem as an optimization problem, and the goal is to find the optimal policy which minimizes the Bayesian regret. One of the main challenges facing the Bayesian approach is that computation of the optimal policy is often intractable, especially when the length of the problem horizon or the number of arms is large. In this paper, we first show that under a suitable rescaling, the Bayesian bandit problem converges toward a continuous Hamilton-Jacobi-Bellman (HJB) equation. The optimal policy for the limiting HJB equation can be explicitly obtained for several common bandit problems, and we give numerical methods to solve the HJB equation when an explicit solution is not available. Based on these results, we propose an approximate Bayes-optimal policy for solving Bayesian bandit problems with large horizons. Our method has the added benefit that its computational cost does not increase as the horizon increases.
CP violation in Bs mixing at LHCb

The CP violating phase $\phi_s$ can be measured in the interference between mixing and decay of $B_s^0$ mesons decaying to CP eigenstates. The phase $\phi_s$ the decay width difference $\Delta\Gamma_s$ and the average decay width $\Gamma_s$ have been measured at LHCb, using the full 1 fb$^{-1}$ of $pp$ collisions data at a centre-of-mass energy $\sqrt{s} $ = 7 TeV collected during the 2011 LHC run.
Commutativity and Disentanglement from the Manifold Perspective

In this paper, we interpret disentanglement as the discovery of local charts of the data manifold and trace how this definition naturally leads to an equivalent condition for disentanglement: commutativity between factors of variation. We study the impact of this manifold framework to two classes of problems: learning matrix exponential operators and compressing data-generating models. In each problem, the manifold perspective yields interesting results about the feasibility and fruitful approaches their solutions. We also link our manifold framework to two other common disentanglement paradigms: group theoretic and probabilistic approaches to disentanglement. In each case, we show how these frameworks can be merged with our manifold perspective. Importantly, we recover commutativity as a central property in both alternative frameworks, further highlighting its importance in disentanglement.
Mechanical features based object recognition

Current robotic haptic object recognition relies on statistical measures derived from movement dependent interaction signals such as force, vibration or position. Mechanical properties that can be identified from these signals are intrinsic object properties that may yield a more robust object representation. Therefore, this paper proposes an object recognition framework using multiple representative mechanical properties: the coefficient of restitution, stiffness, viscosity and friction coefficient. These mechanical properties are identified in real-time using a dual Kalman filter, then used to classify objects. The proposed framework was tested with a robot identifying 20 objects through haptic exploration. The results demonstrate the technique's effectiveness and efficiency, and that all four mechanical properties are required for best recognition yielding a rate of 98.18 $\pm$ 0.424 %. Clustering with Gaussian mixture models further shows that using these mechanical properties results in superior recognition as compared to using statistical parameters of the interaction signals.
Equilibration and thermalization in quantum systems

1 Remarks on the foundations of statistical mechanics 1.1 Canonical approaches 1.1.1 Boltzmann and the H-Theorem 1.1.2 Gibbs' ensemble approach 1.1. 3 (Quasi-)ergodicity 1.1. 4 Jaynes' maximum entropy approach 1.2 Closing remarks 2 Pure state quantum statistical mechanics 2.1 Preliminaries and notation 2.1.1 Hilbert space and state vectors 2.1.2 Observables and states 2.1. 3 Measurements and completely positive maps 2.1. 4 Norms, distance measures and distinguishability 2.1. 5 Entropy 2.1. 6 Time evolution 2.1. 7 Time averages and dephasing 2.1. 8 Composite quantum systems and reduced states 2.1. 9 Correlations and entanglement 2.1.10 Gibbs states 2.1.11 Microcanonical states 2.2 Equilibration 2.2.1 Notions of equilibration 2.2.2 Equilibration on average 2.2.3 Equilibration during intervals 2.2.4 A conjecture concerning equilibration 2.2.5 Other notions of equilibration 2.3 A quantum maximum entropy principle 2.4 Decoherence 2.5 Typicality 2.6 Time scales for equilibration on average 2.7 Thermalization 2.7. 1 What is thermalization? 2.7. 2 Thermalization under assumptions on the eigenstates 2.7. 3 Thermalization under assumptions on the initial state 2.7. 4 Hybrid approaches and other notions of thermalization 2.8 Absence of thermalization 2.8. 1 Violation of subsystem initial state independence 2.8. 2 A numerical investigation of the violation of initial state independence 2.9 Integrability 2.9. 1 In classical mechanics 2.9. 2 In quantum mechanics 2.10 Decay of correlations and stability of thermal states 3 Conclusions Bibliography A Back matter A.1 Acknowledgements A.2 Abstract A.3 Zusammenfassung A.4 Eigenstandigkeitserklarung A.5 Liste der Publikationen des Verfassers A.6 Lebenslauf
On Generalized Integrals and Ramanujan-Jacobi Special Functions

In this article we consider new generalized functions for evaluating integrals and roots of functions. The construction of these generalized functions is based on Rogers-Ramanujan continued fraction, the Ramanujan-Dedekind eta, the elliptic singular modulus and other similar functions. We also provide modular equations of these new generalized functions and remark some interesting properties.
A unified supernova catalogue

In this paper a new supernova catalogue containing data for 5526 extragalactic supernovae that were discovered up to 2010 December 31 is presented. It combines several catalogues that are currently available online in a consistent and traceable way. During the comparison of the catalogues inconsistent entries were identified and resolved where possible. Remaining inconsistencies are marked transparently and can be easily identified. Thus it is possible to select a high-quality sample in a most simple way. Where available, redshift-based distance estimates to the supernovae were replaced by journal-refereed distances. Examples of statistical studies that are now possible with this new catalogue are presented in this paper.
On the algebraic curves for circular and folded strings in $\textrm{AdS}_5\times S^5$

There have been recent advances in the construction of algebraic curves for certain classes of string solutions in the context of the AdS/CFT correspondence. In this paper we obtain the Lax operators and associated spectral curves for circular and folded string solutions in $\,\textrm{AdS}_5\times S^5$. In addition, we provide an original approach for the reconstruction of string solutions in $\,S^3$ from their corresponding curves.
Immunosuppressive Therapies in Ear, Nose, and Throat Involvement in Antineutrophil Cytoplasmic Antibody–Associated Vasculitis: Results From a Multicenter Retrospective Cohort Study

Objective The aim of this study was to evaluate the response of ear, nose, and throat (ENT) symptoms to different immunosuppressive therapies in patients with antineutrophil cytoplasmic antibody–associated vasculitis (AAV). Methods In this cohort study, patients with AAV treated between January 2010 and April 2020 at 2 Dutch hospitals were included. Clinical, histological, and laboratory data were collected retrospectively. ENT involvement was defined as follows: (1) ≥ 1 ENT symptom according to the Birmingham Vasculitis Activity Score (version 3; BVAS3), and/or (2) presence of saddle nose deformity. Associations between therapy and ENT activity were assessed using logistic regression analysis. Results A total of 320 patients with AAV were included, of whom 209 (65.3%) had ENT involvement at some point throughout the disease course. In these 209 patients, median age at disease onset was 52.0 years (IQR 40.0-62.0) and 45.5% were male. Median BVAS3 was 12.0 (IQR 6.0-18.0) at diagnosis. Despite immunosuppressive therapy, 50% (n = 77) of the patients had ENT symptoms at relapse and 29.1% (n = 59) had ENT activity at their last visit. No statistically significant difference in ENT activity at last visit was observed between patients treated with oral or intravenous cyclophosphamide (CYC, n = 137) compared to rituximab (RTX, n = 55; adjusted odds ratio 0.59, 95% CI 0.33-1.06; P = 0.08). Lower age at disease onset and female sex were independently associated with ENT activity at last follow-up. Conclusion In this cohort, CYC and RTX therapy had similar therapeutic effects on ENT symptoms in AAV. Persistent ENT activity is a common feature despite immunosuppressive therapy.