Miracle-dz/UniFAR-FTUs huggingface dataset:
On Optimal Tradeoffs between EFX and Nash Welfare

A major problem in fair division is how to allocate a set of indivisible resources among agents fairly and efficiently. The goal of this work is to characterize the tradeoffs between two well-studied measures of fairness and efficiency --- envy freeness up to any item (EFX) for fairness, and Nash welfare for efficiency --- by saying, for given constants α and β, whether there exists an α-EFX allocation that guarantees a β-fraction of the maximum Nash welfare (β-MNW). For additive valuations, we show that for any α ∈ [0,1], there exists a partial allocation that is α-EFX and 1/(α+1)-MNW. This tradeoff turns out to be tight (for every α) as demonstrated by an impossibility result that we give. We also show that for α ∈ [0, φ-1 ≃ 0.618] these partial allocations can be turned into complete allocations where all items are assigned. Furthermore, for any α ∈ [0, 1/2], we show that the tight tradeoff of α-EFX and 1/(α+1)-MNW with complete allocations holds for the more general setting of subadditive valuations. Our results improve upon the current state of the art, for both additive and subadditive valuations, and match the best-known approximations of EFX under complete allocations, regardless of Nash welfare guarantees. Notably, our constructions for additive valuations also provide EF1 and constant approximations for maximin share guarantees.
Characterizations of Network Auctions and Generalizations of VCG

With the growth of networks, promoting products through social networks has become an important problem. For auctions in social networks, items are needed to be sold to agents in a network, where each agent can bid and also diffuse the sale information to her neighbors. Thus, the agents' social relations are intervened with their bids in the auctions. In network auctions, the classical VCG mechanism fails to retain key properties. In order to better understand network auctions, in this paper, we characterize network auctions for the single-unit setting with respect to weak budget balance, individual rationality, incentive compatibility, efficiency, and other properties. For example, we present sufficient conditions for mechanisms to be efficient and (weakly) incentive compatible. With the help of these properties and new concepts such as rewards, participation rewards, and so on, we show how to design efficient mechanisms to satisfy incentive compatibility as much as possible, and incentive compatibility mechanisms to maximize the revenue. Our results provide insights into understanding auctions in social networks.
Algorithms for computation of stresses and elasticity moduli in terms of Seth–Hill's family of generalized strain tensors

The paper discusses algorithms for the computation of stresses and elasticity moduli for stored energy functions which are given in terms of a Seth–Hill-type generalized strain measure. The key contribution is distinct computational representations of a chain rule representation for the stresses and moduli which may serve as a basic tool for an effective numerical implementation of complex elasticity models. The representations are formulated in the Lagrangian geometric setting based on a spectral decomposition of the right Cauchy–Green tensor and contain particular representations for the case of equal eigenvalues. Two algorithmic boxes are pointed out for three- and two-dimensional problems, respectively. Furthermore, a distinct algorithmic setting is developed for applications of the Seth–Hill-type measures to the isochoric part of the deformation based on a spectral decomposition of the unimodular part of the right Cauchy–Green tensor. The computation of the stress response in connection with generalized Hookean laws is considered as a simple application. Copyright © 2001 John Wiley & Sons, Ltd.
FederatedTrust: A Solution for Trustworthy Federated Learning

The rapid expansion of the Internet of Things (IoT) and Edge Computing has presented challenges for centralized Machine and Deep Learning (ML/DL) methods due to the presence of distributed data silos that hold sensitive information. To address concerns regarding data privacy, collaborative and privacy-preserving ML/DL techniques like Federated Learning (FL) have emerged. However, ensuring data privacy and performance alone is insufficient since there is a growing need to establish trust in model predictions. Existing literature has proposed various approaches on trustworthy ML/DL (excluding data privacy), identifying robustness, fairness, explainability, and accountability as important pillars. Nevertheless, further research is required to identify trustworthiness pillars and evaluation metrics specifically relevant to FL models, as well as to develop solutions that can compute the trustworthiness level of FL models. This work examines the existing requirements for evaluating trustworthiness in FL and introduces a comprehensive taxonomy consisting of six pillars (privacy, robustness, fairness, explainability, accountability, and federation), along with over 30 metrics for computing the trustworthiness of FL models. Subsequently, an algorithm named FederatedTrust is designed based on the pillars and metrics identified in the taxonomy to compute the trustworthiness score of FL models. A prototype of FederatedTrust is implemented and integrated into the learning process of FederatedScope, a well-established FL framework. Finally, five experiments are conducted using different configurations of FederatedScope to demonstrate the utility of FederatedTrust in computing the trustworthiness of FL models. Three experiments employ the FEMNIST dataset, and two utilize the N-BaIoT dataset considering a real-world IoT security use case.
Autocodificadores Variacionales (VAE) Fundamentos Teóricos y Aplicaciones

VAEs are probabilistic graphical models based on neural networks that allow the coding of input data in a latent space formed by simpler probability distributions and the reconstruction, based on such latent variables, of the source data. After training, the reconstruction network, called decoder, is capable of generating new elements belonging to a close distribution, ideally equal to the original one. This article has been written in Spanish to facilitate the arrival of this scientific knowledge to the Spanish-speaking community.
Generalized second law of thermodynamics for the matter creation scenario and emergence of phantom regime

This work is focused on the exploration of the thermodynamics foundations of the matter creation scenario when a generalized form of the second law of thermodynamics for this scheme is implemented. In this scenario we consider an expanding cosmology in which the created matter is trapped by the apparent horizon. The scheme leads to phantom evolution but at first glance it lacks of physical consistency. However, the inclusion of chemical potential into the description solves the thermodynamics issues of the model and determines the behavior of the cosmic fluid, in other words, the cosmic fluid now can behave as phantom dark energy or as quintessence one.
New Dualities in Linear Systems and Optimal Output Control under Bounded Disturbances

In this paper, we introduce novel equations that are dual to the ones of the well-known invariant ellipsoids method. These equations yield ellipsoids with newly established geometrical interpretations and connections to linear system norms. The established duality leads to the optimal synthesis results for state-feedback control, filtering, and output-feedback control problems in the presence of bounded disturbances. The proposed output-feedback control solution is demonstrated to be optimal and surpass prior sub-optimal results.
Machine Learning for Cutting Planes in Integer Programming: A Survey

We survey recent work on machine learning (ML) techniques for selecting cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes of cuts, the task of choosing a set of cuts to add to the linear programming (LP) relaxation at a given node of the branch-and-bound (B&B) tree has defied both formal and heuristic solutions to date. ML offers a promising approach for improving the cut selection process by using data to identify promising cuts that accelerate the solution of MILP instances. This paper presents an overview of the topic, highlighting recent advances in the literature, common approaches to data collection, evaluation, and ML model architectures. We analyze the empirical results in the literature in an attempt to quantify the progress that has been made and conclude by suggesting avenues for future research.
Isometry groups of inductive limits of metric spectral triples and Gromov–Hausdorff convergence

In this paper, we study the groups of isometries and the set of bi‐Lipschitz automorphisms of spectral triples from a metric viewpoint, in the propinquity framework of Latrémolière. In particular, we prove that these groups and sets are compact in the automorphism group of the spectral triple C∗$C^*$ ‐algebra with respect to the Monge–Kantorovich metric, which induces the topology of pointwise convergence. We then prove a necessary and sufficient condition for the convergence of the actions of various groups of isometries, in the sense of the covariant version of the Gromov–Hausdorff propinquity, a noncommutative analogue of the Gromov–Hausdorff distance, when working in the context of inductive limits of quantum compact metric spaces and metric spectral triples. We illustrate our work with examples including AF algebras and noncommutative solenoids.
Bayesian Covariance Estimation for Multi-group Matrix-variate Data

Multi-group covariance estimation for matrix-variate data with small within group sample sizes is a key part of many data analysis tasks in modern applications. To obtain accurate group-specific covariance estimates, shrinkage estimation methods which shrink an unstructured, group-specific covariance either across groups towards a pooled covariance or within each group towards a Kronecker structure have been developed. However, in many applications, it is unclear which approach will result in more accurate covariance estimates. In this article, we present a hierarchical prior distribution which flexibly allows for both types of shrinkage. The prior linearly combines shrinkage across groups towards a shared pooled covariance and shrinkage within groups towards a group-specific Kronecker covariance. We illustrate the utility of the proposed prior in speech recognition and an analysis of chemical exposure data.
Metropolis Theorem and Its Applications in Single Image Detail Enhancement

Traditional image detail enhancement is local filter-based or global filter-based. In both approaches, the original image is first divided into the base layer and the detail layer, and then the enhanced image is obtained by amplifying the detail layer. Our method is different, and its innovation lies in the special way to get the image detail layer. The detail layer in our method is obtained by updating the residual features, and the updating mechanism is usually based on searching and matching similar patches. However, due to the diversity of image texture features, perfect matching is often not possible. In this paper, the process of searching and matching is treated as a thermodynamic process, where the Metropolis theorem can minimize the internal energy and get the global optimal solution of this task, that is, to find a more suitable feature for a better detail enhancement performance. Extensive experiments have proven that our algorithm can achieve better results in quantitative metrics testing and visual effects evaluation. The source code can be obtained from the link.
Longitudinal sampling and aliasing in multi-slice helical computed tomography

Helical pitches 3 and 6 are sometimes called "preferred" in 4-slice helical computed tomography (CT), because it is believed that the effective longitudinal sampling intervals at these pitches are equivalent to those in single-slice helical CT operating at pitches 1 and 2, respectively. While these equivalences have been supported by comparative studies of slice-sensitivity profiles in single- and multi-slice helical CT, artifacts have been observed in pitch-3 and pitch-6 multi-slice images that were not evident in their purported single-slice counterparts. These differences have inspired us to look more closely at longitudinal sampling and aliasing in multi-slice helical CT. In doing so, we have found that the sampling patterns in question are not strictly equivalent, and specifically that under certain conditions longitudinal aliasing is evident in multi-slice reconstructions that is not present in their single-slice counterparts. The difference between the multi-slice, pitch-3 and single-slice, pitch-1 results is attributed to the small cone angle in multi-slice helical CT, which introduces inconsistencies among the measurements of different detector rows. The difference between multi-slice, pitch-6 and single-slice, pitch-2 results is attributed to a combination of the cone angle and genuine differences in sampling patterns. It is argued, however, that the lack of strict sampling equivalence with single-slice counterparts does not necessarily undermine the claim that pitches 3 and 6 are "preferred" relative to other pitches in multi-slice helical CT.
Selectively Providing Reliance Calibration Cues With Reliance Prediction

For effective collaboration between humans and intelligent agents that employ machine learning for decision-making, humans must understand what agents can and cannot do to avoid over/under-reliance. A solution to this problem is adjusting human reliance through communication using reliance calibration cues (RCCs) to help humans assess agents' capabilities. Previous studies typically attempted to calibrate reliance by continuously presenting RCCs, and when an agent should provide RCCs remains an open question. To answer this, we propose Pred-RC, a method for selectively providing RCCs. Pred-RC uses a cognitive reliance model to predict whether a human will assign a task to an agent. By comparing the prediction results for both cases with and without an RCC, Pred-RC evaluates the influence of the RCC on human reliance. We tested Pred-RC in a human-AI collaboration task and found that it can successfully calibrate human reliance with a reduced number of RCCs.
Effect of Internal Coolant Crossflow on the Effectiveness of Shaped Film-Cooling Holes

Film-cooling was the subject of numerous studies during the past decades. However, the flow conditions on the entry side of the film-cooling hole have not received much attention up to now. A stagnant plenum which is widely used in experimental and numerical studies to feed the holes is not necessarily a right means to represent real engine conditions. For this reason, the present paper reports on an experimental study investigating the effect of a coolant crossflow feeding the holes that is oriented perpendicular to the hot gas flow direction to model a flow situation that is, for instance, of common use in the mid-portion of turbine blades. A comprehensive set of experiments was performed to evaluate the effect of perpendicular coolant supply direction on film-cooling effectiveness over a wide range of blowing ratios (M = 0.5{hor{underscore}ellipsis}2.0) and coolant crossflow Mach numbers (Ma{sub c} = 0{hor{underscore}ellipsis}0.6). The coolant to hot gas density ratio, however, was kept constant at 1.85 which can be assumed to be representative for typical gas turbine applications. Three different hole geometries, including a cylindrical hole as well as two holes with expanded exits, were considered. Particularly, two-dimensional distributions of local film-cooling effectiveness acquired by means of anmore » infrared camera system were used to give detailed insight into the governing flow phenomena. The results of the present investigation show that there is a profound effect of how the coolant is supplied to the hole on the film-cooling performance in the near hole region. Therefore, crossflow at the hole entry side must be taken into account when modeling film-cooling at engine representative conditions. « less
Practical Flaky Test Prediction using Common Code Evolution and Test History Data

Non-deterministically behaving test cases cause developers to lose trust in their regression test suites and to eventually ignore failures. Detecting flaky tests is therefore a crucial task in maintaining code quality, as it builds the necessary foundation for any form of systematic response to flakiness, such as test quarantining or automated debugging. Previous research has proposed various methods to detect flakiness, but when trying to deploy these in an industrial context, their reliance on instrumentation, test reruns, or language-specific artifacts was inhibitive. In this paper, we therefore investigate the prediction of flaky tests without such requirements on the underlying programming language, CI, build or test execution framework. Instead, we rely only on the most commonly available artifacts, namely the tests’ outcomes and durations, as well as basic information about the code evolution to build predictive models capable of detecting flakiness. Furthermore, our approach does not require additional reruns, since it gathers this data from existing test executions. We trained several established classifiers on the suggested features and evaluated their performance on a large-scale industrial software system, from which we collected a data set of 100 flaky and 100 non-flaky test- and code-histories. The best model was able to achieve an F1-score of 95.5% using only 3 features: the tests’ flip rates, the number of changes to source files in the last 54 days, as well as the number of changed files in the most recent pull request.
Restrictions on manifolds admitting certain explicit special-generic-like maps and construction of maps with the manifolds

Special-generic-like maps or SGL maps are introduced by the author motivated by observing and investigating algebraic topological or differential topological properties of manifolds via nice smooth maps whose codimensions are negative. The present paper says that manifolds admitting certain very explicit SGL maps are topologically restricted strongly and this also constructs these maps with the manifolds explicitly. Morse functions with exactly two singular points on spheres or functions in Reeb's theorem and canonical projections of naturally embedded spheres in Euclidean spaces are generali5zed as special generic maps. Their nice global structures motivate us to study such maps and the manifolds. Manifolds represented as connected sums of the product of spheres and similar ones admit such maps in considerable cases. The topologies and the differentiable structures of the manifolds of the domains of such maps are also strongly restricted: due to Saeki and Sakuma, followed by Nishioka, Wrazidlo and the author. Respecting their nice global structures, these maps are generalized and this yields SGL maps.
Doppler-Resilient Universal Filtered MultiCarrier (DR-UFMC): A Beyond-OTFS Modulation

In the past few years, some alternatives to the Orthogonal Frequency Division Multiplexing (OFDM) modulation have been considered to improve its spectral containment and its performance level in the presence of heavy Doppler shifts. This paper examines a novel modulation, named Doppler-Resilient Universal Filtered MultiCarrier (DR-UFMC), which has the objective of combining the advantages provided by the Universal Filtered MultiCarrier (UFMC) modulation (i.e., better spectral containment), with those of the Orthogonal Time Frequency Space (OTFS) modulation (i.e., better performance in time-varying environments). The paper contains the mathematical model and detailed transceiver block scheme of the newly described modulation, along with a numerical analysis contrasting DR-UFMC against OTFS, OFDM with one-tap frequency domain equalization (FDE), and OFDM with multicarrier multisymbol linear MMSE processing. Results clearly show the superiority, with respect to the cited benchmarks, of the newly proposed modulation in terms of achievable spectral efficiency. Interestingly, it is also seen that OFDM, when considered in conjunction with multicarrier multisymbol linear minimum mean squares error (MMSE) processing, performs slightly better than OTFS in terms of achievable spectral efficiency.
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation

Natural Language-conditioned reinforcement learning (RL) enables the agents to follow human instructions. Previous approaches generally implemented language-conditioned RL by providing human instructions in natural language (NL) and training a following policy. In this outside-in approach, the policy needs to comprehend the NL and manage the task simultaneously. However, the unbounded NL examples often bring much extra complexity for solving concrete RL tasks, which can distract policy learning from completing the task. To ease the learning burden of the policy, we investigate an inside-out scheme for natural language-conditioned RL by developing a task language (TL) that is task-related and unique. The TL is used in RL to achieve highly efficient and effective policy training. Besides, a translator is trained to translate NL into TL. We implement this scheme as TALAR (TAsk Language with predicAte Representation) that learns multiple predicates to model object relationships as the TL. Experiments indicate that TALAR not only better comprehends NL instructions but also leads to a better instruction-following policy that improves 13.4% success rate and adapts to unseen expressions of NL instruction. The TL can also be an effective task abstraction, naturally compatible with hierarchical RL.
Catalyzed Single-Walled Carbon Nanotube Growth by Machine Learning Molecular Dynamics Simulation

Classical molecular dynamics (MD) simulations have been performed to elucidate the mechanism of single-walled carbon nanotube (SWCNT) growth. However, discussing the chirality has been challenging due to topological defects in simulated SWCNTs. Recently, the application of neural network to interatomic potentials has been actively studied and such interatomic potentials are called neural network potentials (NNPs). NNPs have a better ability for approximate functions and can predict complex systems' energy more accurately than conventional interatomic potentials. In this study, we developed an NNP to describe SWCNT growth more accurately. We successfully demonstrated defect-free and chirality-definable growth of SWCNTs on Fe nanoparticles by MD simulation. Furthermore, it was revealed that edge vacancies of SWCNTs caused defect formation and adatom diffusion contributed to vacancy healing, preventing defect formation.