385 research outputs found
Characterization of Er in porous Si
The fabrication of porous Si-based Er-doped light emitting devices is a very promising developing field for all-silicon light emitters. However, while luminescence of Er-doped porous silicon devices has been demonstrated, very little attention has been devoted to the doping process itself. We have undertaken a detailed study of this process examining the porous silicon matrix from several points of view, during and after the doping. In particular, we have found that the Er doping process shows a threshold level which, as evidenced by the cross correlation of the various techniques used, does depend on the sample thickness and on the doping parameters
Optical properties of multilayered porous silicon
International audienceWe present a short review of some optical devices based on multilayered porous silicon, which can be easily obtained by varying the formation current during the etching process. These include Bragg reflectors and Fabry–Pérot microcavities, which can be adjusted from the visible to the near infrared. The interface roughness, tragic in the case of multilayers, is studied. It can be drastically reduced when changing the electrolyte viscosity. The high reflectivities obtained in this way are measured by Cavity Ring–Down Spectroscopy. Problems occurring when realising thin layers and an efficient way to adjust precisely the optical thicknesses of the thin layers constituting the multilayered structure are also presented. Finally we present a method of calculation of the emission which takes absorption into account and is able to explain the angular dependence of the luminescence
Using an artificial financial market for assessing the impact of Tobin-like transaction taxes
The Tobin tax is a solution proposed by many economists for limiting the speculation in foreign exchange and stock markets and for making these markets stabler. In this paper we present a study on the effects of a transaction tax on one and on two related markets, using an artificial financial market based on heterogeneous agents. The microstructure of the market is composed of four kinds of traders: random traders, fundamentalists, momentum traders and contrarians, and the resources allocated to them are limited. In each market it is possible to levy a transaction tax. In the case of two markets, each trader can choose in which market to trade, and an attraction function is defined that drives their choice based on perceived profitability. We performed extensive simulations and found that the tax actually increases volatility and decreases trading volumes. These findings are discussed in the paper
GLocalX - From Local to Global Explanations of Black Box AI Models
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications
Sintesi e valutazione biologica di nuovi "lead compounds" per la terapia antitubercolare. Derivati del 2-(benzotriazol-1(2)-il)-3- acrilonitrile e delle chinossaline 1,4-diossido
L'insorgenza sempre piĂą elevata di ceppi di M. tuberculosis chemioresistenti ai farmaci abitualmente
impiegati in terapia (MDR) ha spronato la ricerca di nuovi "lead compounds" per lo sviluppo di molecole
che, agendo con meccanismo diverso da quello dei farmaci attualmente impiegati in terapia, possano
contribuire a superare il problema della farmacoresistenza. In quest'ambito le nostre ricerche si sono orientate
verso due differenti direzioni. La prima prende lo spunto dai risultati ottenuti in passato da vari gruppi di
ricerca con alcuni derivati di sintesi di sistemi eterociclici aromatici quali benzimidazolo, benzotiazolo,
benzoisotiazolo e benzofurano, che avevano dimostrato attivitĂ Â antimicobatterica in vitro soprattutto
nei confronti di ceppi MDR.
La seconda linea di ricerca si è basata sulla nota attività  antibatterica e soprattutto antimicotica
della chinossalina 1,4-diossido, con la consapevolezza che spesso l'attività  antimicobatterica è
associata a quella antifungina
Imidazolo [1,2-a] e 1,2,4-triazolo[4,3-a] chinossaline analoghe degli antifolici metotrexato e trimetrexato
Abbiamo progettato una nuova serie di chinossaline, nelle quali l’anello pirrolico è stato sostituito con un’anello imidazolico
o con uno triazolico lasciando nelle posizioni 2, 5, 6, 7 e 8 dell’anello chinossalinico gli stessi
sostituenti precedentemente esaminati.
Di questi composti verranno descritti la sintesi e i risultati farmacologici relativi alla loro attivitĂ
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Authorship Analysis, also known as stylometry, has been an essential aspect
of Natural Language Processing (NLP) for a long time. Likewise, the recent
advancement of Large Language Models (LLMs) has made authorship analysis
increasingly crucial for distinguishing between human-written and AI-generated
texts. However, these authorship analysis tasks have primarily been focused on
written texts, not considering spoken texts. Thus, we introduce the largest
benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).
HANSEN encompasses meticulous curation of existing speech datasets accompanied
by transcripts, alongside the creation of novel AI-generated spoken text
datasets. Together, it comprises 17 human datasets, and AI-generated spoken
texts created using 3 prominent LLMs: ChatGPT, PaLM2, and Vicuna13B. To
evaluate and demonstrate the utility of HANSEN, we perform Authorship
Attribution (AA) & Author Verification (AV) on human-spoken datasets and
conducted Human vs. AI spoken text detection using state-of-the-art (SOTA)
models. While SOTA methods, such as, character ngram or Transformer-based
model, exhibit similar AA & AV performance in human-spoken datasets compared to
written ones, there is much room for improvement in AI-generated spoken text
detection. The HANSEN benchmark is available at:
https://huggingface.co/datasets/HANSEN-REPO/HANSEN.Comment: 9 pages, EMNLP-23 findings, 5 pages appendix, 6 figures, 17 table
Interactions between calliphoridae dipters and Helicodiceros muscivorus
This article reports on the experimental results of a research programme dealing with the reproductive strategies of Helicodiceros muscivorus (L. fil.) Engler(Araceae: Aroideae). In particular, the role played by the odorous mixture emanated by the vegetable species as olfactory information received by the insects, and the importance of that specific biological activity in governing the behavioural choices made by the pollinating insects is studied
Multidisciplinary study of biological parameters and fatigue evolution in quay crane operators
In intermodal terminals the handling of containers and the number of accidents still depends on a wide range of
human errors due
to fatigue despite the automation level reached nowadays. For this reason it is very important to increase knowledge about the
factors affecting the propensity of operators to make
errors, increasing the chance of accidents happening. The
aim of this work is
to propose a novel approach to assess fatigue and performance lev
els in quay crane operators as a function of physiological
parameters and of the many varying boundary conditions encountered in daily work. During their work, quay crane
operators
have to deal with variable environmental conditions, such as task
type, wind speed and direction, lighting conditions that redu
ce
visibility that can require an exacting level
of attention. In the trial eight operators have been examined in a session lastin
g four
hours. All actual conditions are reproduced through a fully imme
rsive quay crane simulator. The operator completes the assigned
task (the same for each one) and can see through four wide monito
rs a high quality virtual reality view of the simulation. Most
biological parameters are acquired using different devices including a Holter ECG monitor, electromyographic monitoring the
four trunk muscles most involved in the test, eye tracker and seat
-
body pressure interface for both
seat pan and backrest. Changes
in physiological parameters have been monitored during the trial and interesting correlations with performance levels and
boundary conditions ha
ve been f
ound for each operator, in accord
ance with their age and skills.
The present study can form the
basis for further investigations aimed at developing a cost effective, reliable and robust system for monitoring increasing fat
igue
and for predicting the critical conditions
that may result in an acciden
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