Miracle-dz/UniFAR-FTUs huggingface dataset:
Why the Scope of the Carbon Tax Matters – Evidence from the Swedish Residential Sector

Sweden has gradually increased its carbon tax within the past 25 years and imposes the world’s highest tax on carbon dioxide emissions today. This paper examines the impact of the Swedish carbon tax on residential carbon emissions. We perform Difference-in-Differences (DiD) regressions and Synthetic Control Methods (SCM) in order to evaluate the causal impact of carbon taxation on carbon emissions in the residential sector. Both methods provide evidence for a causal effect of the carbon tax augmentation in the early 2000s on residential carbon emissions. We find that the scope of the reduction of residential carbon emissions due to the carbon tax augmentation ranges between 200 kg (when compared to other countries with a carbon tax of more than 20 Euros implemented) and 800 kg of CO2 per capita per year (when compared to countries without a carbon tax). Hence, the evidence points towards the effectiveness of carbon taxation in reducing residential CO2 emissions and, thus, mitigating climate change.
A feasibility study for early detection of breast cancer using breast skin temperature rhythms

Breat skin temperatures were measured at 30 min intervals for four days in 10 post-menopausal women, one with benign breast disease and two with clinically healthy breasts. The rhythm characteristics of breast skin temperature for the area of skin over the tumour, between 3 and 5 cm in diameter, were compared with a similar site on the contralateral breast using methods of time series analysis. Results sugges that differences exist between the thermal characteristics of left and right breasts in health subject and that these differences tend to be reinforced by a primary cancer in the left breast but reduced, or even reversed, by one in the right breast. It may be possible to exploit these effects to detect breast cancer at an early stage particularly when the device known as the "chronobra" is available for the automatic collection of breast skin temperature data.
Support Vector Pruning with SortedVotes for Large-Scale Datasets

High computational costs and large memory requirements which are particularly relevant in embedded systems often result from a large support vector (SV) set of a non linear kernel support vector machine (SVM). We propose effective and straight forward methods, an improved RANSAC-SVM algorithm, called RANSAC-SV, and a novel algorithm SortedVotes, for pruning support vectors of a non linear kernel support vector machine. We introduce a lower bound for the number of SVs defined by the classification accuracy of a linear SVM. Like RANSAC, our algorithm SortedVotes adjusts a parameter set in terms of an optimization goal. Thus, its application is not restricted to support vector pruning. Applied to SVMs, SortedVotes prunes an SV set initially obtained after training by weighting each SV with respect to a performance measure of the SVM classification and by sorting the SVs according to this weight. The final SV set contains the SVs with optimal weights. Taking the RANSAC-SVM algorithm as a reference, we evaluate our modified version RANSAC-SV and SortedVotes on the INRIAand on the MNIST-database using a radial basis function (RBF) kernel. SortedVotes outperforms both algorithms at the lower bound for both databases. Whereas in our experiments RANSACSVM does not at all prune SVs without loss in classification accuracy, for the MNIST database, lossless pruning rates of 10% and 65% are obtained with RANSAC-SV and SortedVotes, respectively.
- 09-03 New Challenges for Strategy Development in Irish Higher Education Institutions

Strategic planning has become an integral part of the management of higher education institutions worldwide. The experiences of all but one of the Republic of Ireland’s 21 Universities and Institutes of Technology with strategic planning are explored from 2000-2010. At the starting point in 2000 only two Institutions had a documented strategic plan. Ten years later all had undergone one or more iterations of strategic planning. Qualitative research methods were used including document analysis of n=42 strategic plans, n=12 institutional review reports and n=26 interviews with senior managers with responsibility for strategic planning. The paper considers whether the strategic planning models used in the past decade will be able to meet the challenges presented by unprecedented economic circumstances and the new national strategy for higher education in Ireland. Significant deficits are identified with respect to strategic planning for institutional diversity, performance-related funding and external stakeholder engagement. Fundamental questions are raised in relation to the approaches taken to strategy development.
IEEE-GDL CCD Smart Buildings Introduction

As part of the activities of the IEEE-GDL CCD working group of physical infrastructure, this whitepaper is intented to be an initial guide to understand the layers, taxonomy of services and best practices for the development of smart buildings. Open standards are claimed in order to increase interoperability between layers and services. Moreover, two buildings in Guadalajara city, one new and another to renew, are described as a proof of concept under development and being part of the strategy to develop the smart city infrastructure based in a master plan. A discussion will be addressed in order to identify the areas of innovation and opportunities for the smart buildings as the contribution of this document.
Deep Multitask Acoustic Echo Cancellation

Acoustic echo cancellation or suppression methods aim to suppress the echo originated from acoustic coupling between loudspeakers and microphones. Conventional approaches estimate echo using adaptive filtering. Due to the nonlinearities in the acoustic path of far-end signal, further post-processing is needed to attenuate these nonlinear components. In this paper, we propose a novel architecture based on deep gated recurrent neural networks to estimate the near-end signal from the microphone signal. The proposed architecture is trained using multitask learning to learn the auxiliary task of estimating the echo in order to improve the main task of estimating the clean near-end speech signal. Experimental results show that our proposed deep learning based method outperforms the existing methods for unseen speakers in terms of the echo return loss enhancement (ERLE) for single-talk periods and the perceptual evaluation of speech quality (PESQ) score for double-talk periods.
Financial constraints and the interdependence of corporate fi nancial decisions A cross-country study

Purpose – This paper aims to examine the interdependence of financial decisions (investment, financing, dividends and cash-holding) under financial constraints. Design/methodology/approach – The authors specify and estimate a system of simultaneous equations with panel data and firm fixed effects by three-stage least squares in a sample of firms from 62 countries from 1996 to 2010. Findings – The main findings largely corroborate previous studies regarding the interdependence of financial decisions. The authors also find evidence suggesting that financial constraints have a major impact on firms’ financial decisions. The results also suggest that financial constraints manifest themselves in virtually all firms, indicating that such constraints are a matter of degree and not of kind. Research limitations/implications – Implications regarding the impact of cash flows on investment and cash-holding decisions are only partially confirmed. Practical implications – The results are consistent with the hypothesis that financial constraints distort the financial policies of firms. For the purpose of formulating policies that reduce these distortions, the authors emphasize the role of the availability of internal funds and the recoverable fraction of assets in easing financial constraints, thus allowing for greater investment on the part of firms. Social implications – The results suggest that regulators should promote policies that reduce the dependence of corporate investment on internally generated cash flows. Originality/value – Unlike previous studies, the authors account for the direct impact endogenous variables could have on each other. In addition, they explore the impact of each country’s particular legal environment on the pledgeability of assets at the company level.
DETECTING PHONETIC VARIATION VERSUS PHONEMIC DIFFERENCES

Second-language (L2) speech perception research has largely focused on learning to detect phonetic variation which contributes to phonemic differences. However, phonetic variation may emerge for other reasons, e.g., different speakers. In native language acquisition, speech perception is flexible enough to permit speaker-related phonetic variation while at the same time preserving phonemic identity. In L2 speech learning, it remains unclear how speakerrelated phonetic variation is learned or processed. To test how monolinguals and L2 learners handle speaker-related phonetic variation in detecting phonemic differences, the present study investigated the discrimination of novel words spoken in a familiar and unfamiliar accent. Compared to monolinguals, near-native L2 learners made slightly more errors overall and were quicker to attribute large phonetic differences to phonemic differences rather than speaker-related differences. This highlights the challenging task of learning naturally permissible variation in L2 phonemic categories.
dministration of the inverse benzodiazepine agonist MRK-016 escues acquisition and memory consolidation following peripheral dministration of bacterial endotoxin

Recent evidence suggests that inflammation-induced decrements in cognitive function can be mitigated via manipulation of excitatory or inhibitory transmission. We tested the ability of the inverse benzodiazepine agonist, MRK-016 (MRK) to protect against LPS-induced deficits in memory acquisition and consolidation, using a contextual fear conditioning (CFC) paradigm. In Experiment One, mice received lipopolysaccharide (LPS) and/or MRK injections prior to CFC training, and were then tested 24 h after training. In Experiment Two, animals received similar treatment injections immediately after training, and were tested 24 h later. Additionally, hippocampal samples were collected 4 h after LPS injections and immediately after testing, to evaluate brain-derived neurotrophic factor (BDNF) and insulin-like growth factor 1 (IGF-1) mRNA expression. Results indicate that MRK can protect against LPS-induced learning/memory decrements in both paradigms. We also found, in both paradigms, that animals treated with LPS/Saline expressed significantly less BDNF mRNA when compared to Saline/Saline-treated animals GF-1 4 h after LPS administration, but that MRK did not restore BDNF expression levels. Further, treatment administrations had no effect on IGF-1 mRNA expression at any collection time-point. In summary, MRK-016 can protect against LPS-induced deficits in memory acquisition and consolidation, in this hippocampus-dependent paradigm, though this protection occurs independently of recovery of BDNF
Live Container Migration : Opportunities and Challenges

The usage of container for hosting applications is getting popular due to its inherent capability to isolate resources among different containers within the same kernel. With growing popularity of container technology, demand for containers to be more available and scalable is increasing. Solution to this problem can be swift migration of a container from one machine to another without obstructing operations of the applications. This paper reviews and explores opportunities and challenges of such live container migration.
HARES: Hardened Anti-Reverse Engineering System

This paper provides a technical overview of the HARES software protection research eﬀort performed by Assured Information Security. HARES is an anti reverse-engineering technique that uses on-CPU encryption [7] in conjunction with Intel x86 TLB-splitting [12] in order to signiﬁcantly increase the eﬀort required to obtain the clear-text assembly instructions that comprise the target x86 application. Performance and use-cases of the system are presented, and a number of weaknesses and future works are discussed. Related works are compared and contrasted with HARES in order to highlight its improvements over the current state-of-the-art.
End-to-End 3D Hand Pose Estimation from Stereo Cameras

This work proposes an end-to-end approach to estimate full 3D hand pose from stereo cameras. Most existing methods of estimating hand pose from stereo cameras apply stereo matching to obtain depth map and use depth-based solution to estimate hand pose. In contrast, we propose to bypass the stereo matching and directly estimate the 3D hand pose from the stereo image pairs. The proposed neural network architecture extends from any keypoint predictor to estimate the sparse disparity of the hand joints. In order to effectively train the model, we propose a large scale synthetic dataset that is composed of stereo image pairs and ground truth 3D hand pose annotations. Experiments show that the proposed approach outperforms the existing methods based on the stereo depth.
Towards a Model-Driven Engineering Software Development Framework

Design and Implementation of Safety-Critical Systems is becoming very difficult becauses it involves many requirements coming from different engineering domains. Due to the increase of complexity, software of such systems can no longer be produced with traditional methods, which show their limit over time. In that context, new development approaches have to be introduced to avoid actual development traps and pitfalls. Among them, the Model-Driven Engineering approach consists at representing system artifacts with models and autogenerate the code by refining them from high-level concepts down to the code. However, as for every new approach, it also brings new problems such as requirements consistency among the different notations (models) as well as integration issues (for example, making sure that implementation code from different models will behave correctly when merged on a single execution platform). This article presents our experience for integrating Guidance and Navigation Control (GNC) algorithms designed with Application Models (Simulink) with Architecture Models (AADL). The process relies on code generator for both models and integrate it on a typical execution platform. In particular, we focus on the challenges of the integration, illustrating the practical problems we faced for producing a space system using a Model-Driven Engineering Approach. Keywords-AADL, TASTE, Simulink, MDE
Crowdsourced Microservices Behavior-Driven development Applied to Microtask Programming

Behavior-Driven Development (BDD) as a popular agile software development processes commonly used in microservices development. Microservices decompose large and complex web back-ends into single-purpose services, enabling complex back-ends to be built, reused and deployed quickly. Besides, workflows in microtasks crowdsourcing organizes complex work, decomposing large tasks into small, relatively independent microtasks. In this paper, we introduce a BDD-based workflow for implementing microservices through microtasks crowdsourcing. In our approach, a client, acting on behalf of a software team, describes a microservice as a set of endpoints with paths, requests, and responses. A crowd then follows the BDD-based workflow and implements the endpoints, identifying individual endpoint behaviors which they test, write, and debug, creating new functions and interacting with persistence APIs as needed. To evaluate our approach, we conducted a feasibility study in which a small crowd worked to implement a small todo microservice, then we built a test suite and evaluated the final output of the crowd. The crowd created an implementation with only four defects, completing 350 microtasks and implementing 13 functions. We discuss the implications of these findings for incorporating crowdsourcing into software projects.
Texture-based Tissue Characterization : A Novel Predictor for Brain Injury ?

Premature infants are prone to white matter damage (WMD), which is associated with cerebral palsy (CP) and cognitive impairment. Ultrasound (US) is the preferred imaging modality to detect WMD. To improve on existing diagnostic rates, quantitative measures incorporating new information are needed. We are investigating US texture measures as new indicators of white matter health. We are developing techniques which enhance image texture differences that may reflect pathological changes. Earlier experiments using data from 30 patients indicate that tissue types may be segmented based on texture measures. Present experiments using images from 18 patients (12 with normal outcome, 6 who developed CP) reveal that the measures from CP patients form separate populations from healthy patients. Texture measures were obtained without compensating for operator-dependent machine settings and without suppressing speckle. Digitized analogue ultrasound films are used as the input data since modern digital ultrasound machines have bandwidth restrictions. The effect of image resolution reduction on the texturebased methods is part of an on-going investigation.
Consonant Classification in Mandarin Based on the Depth Image Feature: A Pilot Study

The consonant is an important element in Mandarin, and various categories of consonant generation effectuate various facial expressions. Specifically, there are changes in facial muscles when speaking, and these changes are closely related to pronunciation; the facial muscles are associated with these hidden articulators, and the effects on the facial changes can be seen as 3D changes. However, in most studies, 2D images are used to analyze facial features when people talk. The 2D images serve to provide information in two dimensions (xand y-axis); however, subtle deep motions (z-axis changes) of facial muscles when speaking can be difficult to detect accurately. Hence, the depth feature of the face (the point cloud feature in this study) was used to investigate the potential for consonant recognition, recorded by a time-of-flight 3D camera. In this study, we propose an algorithm to recognize the seven categories of Mandarin consonants using the depth features of the speaker’s face. The proposed system yielded suitable classification accuracy for the recognition of seven categories of Mandarin consonants. This result implies that depth features can be used for speech-processing applications.
Cutaneous sarcoidosis in a melanoma scar: A case report

Sarcoidosis is an inflammatory multisystemic disease of unknown etiology with multiple presentations of cutaneous lesions. It characteristically infiltrates scars due to several kind of trauma such as surgery, tattoo and even herpes zoster. We present a case of a 65-year-old woman with progressive distal paresthesia and motor weakness. She was referred to our dermatology clinic for a new violaceous nodular plaque within an old melanoma scar on the posterior neck. On positron emission tomography–computed tomography, there were multiple enlarged thoracic lymphadenopathy and a fluorodeoxyglucose-avid cutaneous lesion on the posterior neck, right in the melanoma scar. Cutaneous and nerve biopsies showed non-necrotising granulomas, supporting the diagnosis of systemic sarcoidosis and excluding melanoma recurrence with metastasis. It is the first case of cutaneous sarcoidosis arising within a melanoma scar. Interestingly, patients with melanoma have a higher risk of sarcoidosis.
Collagen-Covered Autologous Chondrocyte Implantation Versus Autologous Matrix-Induced Chondrogenesis: A Randomized Trial Comparing 2 Methods for Repair of Cartilage Defects of the Knee

Background: Autologous matrix-induced chondrogenesis (AMIC) is a single-stage alternative to autologous chondrocyte implantation for treatment of localized cartilage defects of the knee. To our knowledge, no randomized controlled trial exists comparing the 2 methods. Purpose: To evaluate any difference in the outcome of AMIC as compared with collagen-covered autologous chondrocyte implantation (ACI-C). Study Design: Randomized controlled trial; Level of evidence, 2. Methods: A prospective randomized controlled clinical trial was designed to assess any differences in the outcomes between ACI-C and AMIC for the treatment of ≥1 chondral or osteochondral defects of the distal femur and/or patella. The inclusion period was set to 3 years, and the aim was to include 80 patients (40 in each group). Patient inclusion was broad, with few exclusion criteria. The primary outcome was change in Knee injury and Osteoarthritis Outcome Score (KOOS) at 2 years as compared with baseline. The secondary outcomes were the number of failures in each group at 2 years and the change in KOOS subscale, Lysholm, and pain visual analog scale (VAS) scores at 2 years as compared with baseline. A 2-sample t test with a significance level of P < . 05 was used to compare the change in score from baseline between groups. Results: A total of 41 patients over 3 years were included in the study: 21 in the ACI-C group and 20 in the AMIC group. All the patients had prior surgery to the index knee. At 2-year follow-up, the clinical scores for both groups improved significantly from baseline. No significant differences between groups were seen in the change from baseline for KOOS (AMIC, 18.1; ACI-C, 10.3), any of the KOOS subscales, the Lysholm score (AMIC, 19.7; ACI-C, 17.0), or the VAS pain score (AMIC, 30.6; ACI-C, 19.6). Two patients in the AMIC group had progressed to a total knee replacement by the 2-year follow-up as compared with none in the ACI-C group. Conclusion: At 2-year follow-up, no significant differences were found regarding outcomes between ACI-C and AMIC. Mid- and long-term results will be important. Registration: NCT01458782 (ClinicalTrials.gov identifier).
Disinhibited Revenge – An fNIRS Study on Forgiveness and Cognitive Control

The ability to reconcile is a key factor for a cooperative and successful life. Among the many factors that have an impact on how people negotiate social contracts, poor cognitive control (which is inversely linked to impulsivity) may exert negative effects on forgiveness. To investigate the neurobiological basis of this proposition, subjects with high vs. low impulsivity scores completed an ultimatum game (UG) and a dictator game (DG). First, the participants played an UG where they had to accept or reject offers from fair or unfair opponents. Afterward, the roles changed, and a DG was played. Here, subjects had the opportunity to forgive or take revenge on unfair opponents by the allocation of a fair/unfair amount of money. During this task, activity of the dorsolateral prefrontal cortex (DLPFC) was assessed via functional near-infrared spectroscopy (fNIRS). Highly impulsive subjects were significantly more revenge-seeking than individuals with a low impulsivity. This behavioral difference was reflected in the activation pattern of the left DLPFC, where higher activation in trials with unfair opponents was found, but only in the highly impulsive group. This result is discussed as an indicator of more revenge-driven behavior in highly impulsive individuals, since activity in the left DLPFC is associated with retaliation.