3,028 research outputs found
An assessment of the impact of possible CAP reform scenarios on Romanian agriculture
No full textUsing a simplified model, with key-variable the prices of two different possible scenarios of CAP reform after 2013 (moderate and radical), this paper present a comparison between the price effects of implementation of each reform scenario at 2015 horizon on Romanian agriculture. This short analysis shows that, under the presented hypotheses, the net welfare effect, due to the price changes, for the selected products, is positive in both reform scenarios, yet greater in the case of the radical reform. Integrated in the large context of Romanian development, it seems that the influence of CAP reform upon agriculture and rural areas will be most likely a gradual one: an interpenetration between the two scenarios is foreseeable, starting with the moderate reform that will dominate the period around 2013, the reform measures acquiring a more radical character afterwards.CAP reform, Romania, welfare effects, Agricultural and Food Policy,
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
No full textA key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e. hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015
Rich, Sturmian, and trapezoidal words
No full textIn this paper we explore various interconnections between rich words, Sturmian words, and trapezoidal words. Rich words, first introduced by the second and third authors together with J. Justin and S. Widmer, constitute a new class of finite and infinite words characterized by having the maximal number of palindromic factors. Every finite Sturmian word is rich, but not conversely. Trapezoidal words were first introduced by the first author in studying the behavior of the subword complexity of finite Sturmian words. Unfortunately this property does not characterize finite Sturmian words. In this note we show that the only trapezoidal palindromes are Sturmian. More generally we show that Sturmian palindromes can be characterized either in terms of their subword complexity (the trapezoidal property) or in terms of their palindromic complexity. We also obtain a similar characterization of rich palindromes in terms of a relation between palindromic complexity and subword complexity
Using contrastive learning to inject domain-knowledge into neural networks for recognizing emotions
No full textWith application contexts ranging from psychophysiology to neuromarketing, electroencephalography (EEG)-based
emotion recognition is a fundamental technology for affective
computing. In this context, EEG signals can be processed via
artificial neural networks (NNs) to achieve accurate recognition
of users’ emotions. Still, NNs are rarely employed in realworld decision-making processes, since their internal model
works as a hardly trustable black box. A NN’s reasoning can
be explained in a human-comprehensible manner by exploring
its latent space to understand if some domain knowledge is
actually represented and exploited for the classification. Those
approaches assume that a trained NN autonomously organizes
its latent space according to some domain concepts to process the
data via human-like reasoning. However, there is no guarantee
that such an assumption holds, since the latent space is not
built for this aim. On the other hand, forcing the organization
of the latent space (e.g. via contrastive learning) can result
in poor recognition performances due to information loss. To
guarantee great recognition performances and provide a domainknowledge-driven organization of NNs’ latent space, we combine
the well-known training procedure based on a categorical crossentropy loss with a supervised contrastive learning approach for
continuous values labels. The proposed approach (i) enables the
explanation of NN’s reasoning in terms of the importance of
high-level domain concepts in the final classification, and (ii)
results in a recognition performance comparable to or better than the one achieved via an approach based solely on maximizing
recognition. The proposed approach is tested on the publicly
available MAHNOB datase
Characterization Results for the Poset Based Representation of Topological Relations - I: Introduction and Models
No full text@article{DBLP:journals/informaticaSI/ForlizziN99,
author = {Luca Forlizzi and
Enrico Nardelli},
title = {Characterization Results for the Poset Based Representation
of Topological Relations - I: Introduction and Models.},
journal = {Informatica (Slovenia)},
volume = {23},
number = {2},
year = {1999},
bibsource = {DBLP, http://dblp.uni-trier.de}
Characterization Results for the Poset Based Representation of Topological Relations - II: Intersection and Union
No full text@article{DBLP:journals/informaticaSI/ForlizziN00,
author = {Luca Forlizzi and
Enrico Nardelli},
title = {Characterization Results for the Poset Based Representation
of Topological Relations - II: Intersection and Union.},
journal = {Informatica (Slovenia)},
volume = {24},
number = {1},
year = {2000},
bibsource = {DBLP, http://dblp.uni-trier.de}
System-on-chip Computing and Interconnection Architectures for Telecommunications and Signal Processing
No full textThis dissertation proposes novel architectures and design techniques targeting SoC building blocks for telecommunications and signal processing applications.
Hardware implementation of Low-Density Parity-Check decoders is approached at both the algorithmic and the architecture level. Low-Density Parity-Check codes are a promising coding scheme for future communication standards due to their outstanding error correction performance.
This work proposes a methodology for analyzing effects of finite precision arithmetic on error correction performance and hardware complexity. The methodology is throughout employed for co-designing the decoder. First, a low-complexity check node based on the P-output decoding principle is designed and characterized on a CMOS standard-cells library. Results demonstrate implementation loss below 0.2 dB down to BER of 10^{-8} and a saving in complexity up to 59% with respect to other works in recent literature. High-throughput and low-latency issues are addressed with modified single-phase decoding schedules. A new "memory-aware" schedule is proposed requiring down to 20% of memory with respect to the traditional two-phase flooding decoding. Additionally, throughput is doubled and logic complexity reduced of 12%. These advantages are traded-off with error correction performance, thus making the solution attractive only for long codes, as those adopted in the DVB-S2 standard. The "layered decoding" principle is extended to those codes not specifically conceived for this technique. Proposed architectures exhibit complexity savings in the order of 40% for both area and power consumption figures, while implementation loss is smaller than 0.05 dB.
Most modern communication standards employ Orthogonal Frequency Division Multiplexing as part of their physical layer. The core of OFDM is the Fast Fourier Transform and its inverse in charge of symbols (de)modulation. Requirements on throughput and energy efficiency call for FFT hardware implementation, while ubiquity of FFT suggests the design of parametric, re-configurable and re-usable IP hardware macrocells. In this context, this thesis describes an FFT/IFFT core compiler particularly suited for implementation of OFDM communication systems. The tool employs an accuracy-driven configuration engine which automatically profiles the internal arithmetic and generates a core with minimum operands bit-width and thus minimum circuit complexity. The engine performs a closed-loop optimization over three different internal arithmetic models (fixed-point, block floating-point and convergent block floating-point) using the numerical accuracy budget given by the user as a reference point. The flexibility and re-usability of the proposed macrocell are illustrated through several case studies which encompass all current state-of-the-art OFDM communications standards (WLAN, WMAN, xDSL, DVB-T/H, DAB and UWB). Implementations results are presented for two deep sub-micron standard-cells libraries (65 and 90 nm) and commercially available FPGA devices. Compared with other FFT core compilers, the proposed environment produces macrocells with lower circuit complexity and same system level performance (throughput, transform size and numerical accuracy).
The final part of this dissertation focuses on the Network-on-Chip design paradigm whose goal is building scalable communication infrastructures connecting hundreds of core. A low-complexity link architecture for mesochronous on-chip communication is discussed. The link enables skew constraint looseness in the clock tree synthesis, frequency speed-up, power consumption reduction and faster back-end turnarounds. The proposed architecture reaches a maximum clock frequency of 1 GHz on 65 nm low-leakage CMOS standard-cells library. In a complex test case with a full-blown NoC infrastructure, the link overhead is only 3% of chip area and 0.5% of leakage power consumption.
Finally, a new methodology, named metacoding, is proposed. Metacoding generates correct-by-construction technology independent RTL codebases for NoC building blocks. The RTL coding phase is abstracted and modeled with an Object Oriented framework, integrated within a commercial tool for IP packaging (Synopsys CoreTools suite). Compared with traditional coding styles based on pre-processor directives, metacoding produces 65% smaller codebases and reduces the configurations to verify up to three orders of magnitude
Assessing Refugees' Integration via Spatio-temporal Similarities of Mobility and Calling Behaviors
No full textIn Turkey the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing a real-world Call Details Records (CDRs) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees’ and locals’ spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample to a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allows computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: (i) correlated with the amount of interaction with locals; (ii) an effective proxy for refugee's economic capacity, thus refugee's potential employment; and (iii) able to capture events that may disrupt the integration phenomena, such as social tensions
Matching the Expert’s Knowledge via a Counterfactual-Based Feature Importance Measure
No full textTo be employed in real-world applications, explainable artificial intelligence (XAI) techniques need to provide explanations that are comprehensible to experts and decision-makers with no machine learning (ML) background, thus allowing for the validation of the ML model via their domain knowledge.
To this aim, XAI approaches based on feature importance and counterfactuals can be employed, although both have some limitations: the last provide only local explanations, whereas the first can be very computationally expensive. A less computationally-expensive global feature importance measure can be derived by considering the instances close to the model decision boundary and analyzing how often some minor changes in one feature’s values do affect the classification outcome.
However, the validation of XAI techniques in the literature rarely employs the application domain knowledge due to the burden of formalizing it, e.g., providing some degree of expected importance for each feature. Still, given an ML model, it is difficult to determine whether an XAI technique may inject a bias in the explanation (e.g., overestimating or underestimating the importance of a feature) in the absence of such ground truth.
To address this issue, we test our feature importance approach both with the UCI benchmark datasets and real-world smart manufacturing data characterized by annotations provided by domain experts about the expected importance of each feature. If compared to the state-of-the-art, the employed approach results to be reliable and convenient in terms of computation time, as well as more concordant with the expected importance provided by the domain expert
Detecting Permanent and Intermittent Purchase Hotspots via Computational Stigmergy
No full textThe analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational stigmergy. By using computational stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015
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