Ensemble equivalence for distinguishable particles

Statistics of distinguishable particles has become relevant in systems of colloidal particles and in the context of applications of statistical mechanics to complex networks. When studying these type of systems with the standard textbook formalism, non-physical results such as non-extensive entropies are obtained. In this paper, we will show that the commonly used expression for the partition function of a system of distinguishable particles leads to huge fluctuations of the number of particles in the grand canonical ensemble and, consequently, to non-equivalence of statistical ensembles. We will see how a new proposed definition for the entropy of distinguishable particles by Swendsen [J. Stat. Phys. 107, 1143 (2002)] solves the problem and restores ensemble equivalence. We also show that the new proposal for the partition function does not produce any inconsistency for a system of distinguishable localized particles, where the monoparticular partition function is not extensive

Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments

As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by joining 3D perception with force/torque sensing, with emphasis on finding a practical solution with a feasible implementation in a domestic setting. Our algorithm obtains a point cloud representation of the working environment. From this point cloud, the garment is segmented and a custom Wrinkleness Local Descriptor (WiLD) is computed to determine the location of the present wrinkles. Using this descriptor, the most suitable ironing path is computed and, based on it, the manipulation algorithm performs the force-controlled ironing operation. Experiments have been performed with a humanoid robot platform, proving that our algorithm is able to detect successfully wrinkles present in garments and iteratively reduce the wrinkleness using an unmodified iron.Comment: Accepted and to be published on the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) that will be held in Vancouver, Canada, September 24-28, 201

A seat at the table: the Student Trustee at the University of Massachusetts system, 1969–present

The purpose of this qualitative study was to explore the developing role of the Student Trustee. Utilizing a case study design and document analysis, this descriptive study examined the comments of 143 Student Trustees in Board meetings of the University of Massachusetts (UMass) System, the first in the nation to require Student Trustees, from 1970-–2015. The research questions sought to uncover the origins of the Student Trustee at the UMass System as well as how the role developed over time. The study concluded that Student Trustees provide a unique perspective that offers meaningful contributions to the discourse and decision-making processes of university Boards. The legislation that placed the first Student Trustee on the UMass Board was the result of contentious campus protests fueled by student dissatisfaction with higher education’s response to the Vietnam War, racism, and sexism, among other issues. Governor Francis Sargent proposed and signed that legislation in 1969 as a means to “move protest from confrontation to dialogue.” Student Trustees found success pushing the Board in a more progressive direction – adopting co-ed dormitories, providing greater due process in conduct matters, and asserting that students have primary responsibility over student policies and related matters. Student Trustees also pressed the Board to divest from companies operating in apartheid South Africa, and even to grant students an eight-day reprieve from papers and exams so they could campaign in the 1970 congressional elections. The role of the Student Trustee has expanded since Cynthia Olken took her place as the first Student Trustee in 1970. There are now five Student Trustees representing each of the five campuses in the UMass System. The two with voting power operate as regular board members and have the ability to serve on all committees, while the other three are ex officio non-voting members and can only attend open meetings of the full Board of Trustees. While more than half of the 143 Student Trustees made five or fewer remarks during their time on the board, there were many who spoke out frequently on issues related to finance, governance, and academics. Through their half-century of efforts, Student Trustees have earned a seat at the table and the praise of many university presidents, chancellors, and Board chairs that have used words like helpful, valuable, and significant to describe their contributions. As former UMass President Jack Wilson once exclaimed, “Having student representation on this Board is important.

¿Sueñan los jueces con sentencias electrónicas?

The voluntarist disruptive hopes in the full application of artificial intelligence (AI) in the field of the Judiciary does not enervate an inescapable –and inevitable– reality such as the reformulated and growing link between AI and the Administration of Justice. And more specifically, between the algorithm and thejudicial decision. However, throughout the text, it will be shown that, to this day, the direct replacement of human activity in the judicial decision is purely chimerical in the short and medium term. Another thing is the activities related to the judicial prediction of the private platforms that use AI, fully developed and implemented (with some criminal reluctance as in France). Along with this prescient commercial activity, only in two areas can a timid disruption in the judicial field be rigorously affirmed: the possibilities offered by simple automation in the process and the use of automatic data processing technologies in the audiovisualfield. Disadvantages related to potentially discriminatory bias; the pure search for the technological imitation of the guidelines of human behavior; the denaturation of the heuristic principle or technical incapacity, such as the still incomplete processing of human language, are too many servitudes to consider robotjudges even more than a fiction.La voluntaristas esperanzas disruptivas en la plena aplicación de la inteligencia artificial (IA) en el ámbito del Poder Judicial no enerva una realidad ineludible –e inevitable- como es la reformulada y creciente vinculación entre la IA y la Administración de Justicia. Y más concretamente, entre el algoritmo y la decisión judicial. Ahora bien, a lo largo del texto, se pondrá de manifiesto que, al día de hoy, la sustitución directa de la actividad humana en la decisión judicial es puramente quimérica a corto y medio plazo. Otra cosa son las actividades relacionadas con la predicción judicial al socaire de las plataformas privadas que emplean la IA, plenamente desarrolladas e implantadas (con algunas reticencias penales como en Francia). Junto a esta actividad comercial presciente, únicamente en dos ámbitos puede afirmarse con rigor una tímida disrupción en el ámbito judicial: las posibilidades que ofrece la automatización simple en el proceso y el empleo de tecnologías de procesamiento automático de datos en el ámbito audiovisual. Inconvenientes relacionados con el sesgo potencialmente discriminatorio; la pura búsqueda de la imitación tecnológica de las pautas de comportamiento humano; la desnaturalización del principio heurístico o incapacidad técnicas como el todavía incompleto procesamiento del lenguaje humano, son demasiadas servidumbres para no considerar aún más que una ficción a los jueces robots

Deep robot sketching: an application of deep Q-learning networks for human-like sketching

© 2023 The Authors. Published by Elsevier B.V. This research has been financed by ALMA, ‘‘Human Centric Algebraic Machine Learning’’, H2020 RIA under EU grant agreement 952091; ROBOASSET, ‘‘Sistemas robóticos inteligentes de diagnóstico y rehabilitación de terapias de miembro superior’’, PID2020-113508RBI00, financed by AEI/10.13039/501100011033; ‘‘RoboCity2030-DIHCM, Madrid Robotics Digital Innovation Hub’’, S2018/NMT-4331, financed by ‘‘Programas de Actividades I+D en la Comunidad de Madrid’’; ‘‘iREHAB: AI-powered Robotic Personalized Rehabilitation’’, ISCIIIAES-2022/003041 financed by ISCIII and UE; and EU structural fundsThe current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs.Sección Deptal. de Arquitectura de Computadores y Automática (Físicas)Fac. de Ciencias FísicasTRUEUnión Europea. H2020Ministerio de Ciencia e Innovación (MICINN)/ AEI/10.13039/501100011033;Comunidad de MadridInstituto de Salud Carlos III (ISCIII)/UEROBOTICSLABpu

Combining absolute and relative information in studies on food quality

A common problem in food science concerns the assessment of the quality of food samples. Typically, a group of panellists is trained exhaustively on how to identify different quality indicators in order to provide absolute information, in the form of scores, for each given food sample. Unfortunately, this training is expensive and time-consuming. For this very reason, it is quite common to search for additional information provided by untrained panellists. However, untrained panellists usually provide relative information, in the form of rankings, for the food samples. In this paper, we discuss how both scores and rankings can be combined in order to improve the quality of the assessment

A Neural TTS System with Parallel Prosody Transfer from Unseen Speakers

Modern neural TTS systems are capable of generating natural and expressive speech when provided with sufficient amounts of training data. Such systems can be equipped with prosody-control functionality, allowing for more direct shaping of the speech output at inference time. In some TTS applications, it may be desirable to have an option that guides the TTS system with an ad-hoc speech recording exemplar to impose an implicit fine-grained, user-preferred prosodic realization for certain input prompts. In this work we present a first-of-its-kind neural TTS system equipped with such functionality to transfer the prosody from a parallel text recording from an unseen speaker. We demonstrate that the proposed system can precisely transfer the speech prosody from novel speakers to various trained TTS voices with no quality degradation, while preserving the target TTS speakers' identity, as evaluated by a set of subjective listening experiments.Comment: Presented at Interspeech 202

Making Differential Privacy Easier to Use for Data Controllers and Data Analysts using a Privacy Risk Indicator and an Escrow-Based Platform

Differential privacy (DP) enables private data analysis but is hard to use in practice. For data controllers who decide what output to release, choosing the amount of noise to add to the output is a non-trivial task because of the difficulty of interpreting the privacy parameter $\epsilon$. For data analysts who submit queries, it is hard to understand the impact of the noise introduced by DP on their tasks. To address these two challenges: 1) we define a privacy risk indicator that indicates the impact of choosing $\epsilon$ on individuals' privacy and use that to design an algorithm that chooses $\epsilon$ automatically; 2) we introduce a utility signaling protocol that helps analysts interpret the impact of DP on their downstream tasks. We implement the algorithm and the protocol inside a new platform built on top of a data escrow, which allows the controller to control the data flow and achieve trustworthiness while maintaining high performance. We demonstrate our contributions through an IRB-approved user study, extensive experimental evaluations, and comparison with other DP platforms. All in all, our work contributes to making DP easier to use by lowering adoption barriers

Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Most deployed data discovery systems, such as Google Datasets, and open data portals only support keyword search. Keyword search is geared towards general audiences but limits the types of queries the systems can answer. We propose a new system that lets users write natural language questions directly. A major barrier to using this learned data discovery system is it needs expensive-to-collect training data, thus limiting its utility. In this paper, we introduce a self-supervised approach to assemble training datasets and train learned discovery systems without human intervention. It requires addressing several challenges, including the design of self-supervised strategies for data discovery, table representation strategies to feed to the models, and relevance models that work well with the synthetically generated questions. We combine all the above contributions into a system, Solo, that solves the problem end to end. The evaluation results demonstrate the new techniques outperform state-of-the-art approaches on well-known benchmarks. All in all, the technique is a stepping stone towards building learned discovery systems. The code is open-sourced at https://github.com/TheDataStation/soloComment: To appear at Sigmod 202