Miracle-dz/UniFAR-FTUs huggingface dataset:
Fortran Arrays in C

E(cid:30)ciency of operations in computer applications can have a signi(cid:28)cant impact on their performance. When intensive calculations are involved, and numerous memory access operations are required, they can cause latency which, in turn, can be reduced by a more e(cid:30)cient implementation. The C programming language o(cid:27)ers a strategy for memory organization of arrays that can cause this kind of latency, under speci(cid:28)c circumstances. This project presents a possible solution for mitigating this drawback. This is achieved by reproducing the behaviour of these operations in the Fortran programming language, which utilizes a di(cid:27)er-ent strategy for memory organization of arrays.
Private Topic Modeling

We develop a privatised stochastic variational inference method for Latent Dirichlet Allocation (LDA). The iterative nature of stochastic variational inference presents challenges: multiple iterations are required to obtain accurate posterior distributions, yet each iteration increases the amount of noise that must be added to achieve a reasonable degree of privacy. We propose a practical algorithm that overcomes this challenge by combining: (1) an improved composition method for differential privacy, called the moments accountant, which provides a tight bound on the privacy cost of multiple variational inference iterations and thus significantly decreases the amount of additive noise; and (2) privacy amplification resulting from subsampling of large-scale data. Focusing on conjugate exponential family models, in our private variational inference, all the posterior distributions will be privatised by simply perturbing expected sufficient statistics. Using Wikipedia data, we illustrate the effectiveness of our algorithm for large-scale data.
3D model fitting for facial expression analysis under uncontrolled imaging conditions

This paper addresses the recovering of 3D pose and animation of the human face in a monocular single image under uncontrolled imaging conditions. Our goal is to fit a 3D animated model in a face image with possibly large variations of head pose and facial expressions. Our data were acquired from filmed epileptic seizures of patients undergoing investigation in the videotelemetry unit, La Timone hospital, Marseille, France.
Identifying Free Space in a Robot Bird-Eye View

Free space detection based on visual clues is an upcoming approach in robotics. Our working domain is the Virtual Rescue League of the RoboCup. In this domain efficient obstacle avoidance is crucial to find victims under challenging conditions. In this study a machine-learning approach is applied to distinguish the difference in visual appearance of obstacles and free space. Omnidirectional camera images are transformed to bird-eye view, which makes comparison with local occupancy maps possible. Bird-eye view images are automatically labeled using Laser Range information, allowing completely autonomous and continuous learning of accurate color models. Two color-based models are compared; a Histogram Method and a Gaussian Mixture Model. Both methods achieve very good performances, with results in a high precision and recall on a typical map from the Rescue League. The Gaussian Mixture Model achieves the best scores with much less parameters on this map, but is beaten by the Histogram Method on real data collected by our Nomad robot. Additionally, the importance of the right color normalization scheme and model parameters is demonstrated in this study.
Segmentation by transduction

This paper addresses the problem of segmenting an image into regions consistent with user-supplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified. Our method relies on the Laplacian graph regularizer, a powerful manifold learning tool that is based on the estimation of variants of the Laplace-Beltrami operator and is tightly related to diffusion processes. Segmentation is modeled as the task of finding matting coefficients for unclassified pixels given known matting coefficients for seed pixels. The proposed algorithm essentially relies on a high margin assumption in the space of pixel characteristics. It is simple, fast, and accurate, as demonstrated by qualitative results on natural images and a quantitative comparison with state-of-the-art methods on the Microsoft GrabCut segmentation database.
Crypto Cloud

The use of Cloud Storage Services has had an accentuated growth over the past few years. With the migration of these services, several security risks have risen, such as the lack of privacy in the stored data and the need to trust the cloud providers’ intentions. To overcome these problems, this thesis proposes Crypto Cloud, a secure cloud storage system based on the Storekeeper’s approach [11, 10]. This solution allows for the use of multiple cloud providers without renouncing privacy, guaranteeing the confidentiality and integrity of managed files, which brings new challenges related to key management and access control. To address the key management, Crypto Cloud implements the KMIP [9] protocol, which provides remote management of the users’ cryptographic keys, decoupling the users and the clouds from the responsibility of managing these keys. The component implementing the KMIP protocol takes advantage of Hardware Security Modules to handle the users’ keys and perform remote cryptographic operations. To guarantee proper certification of the users’ keys and to assure their identity when accessing the managed files, the system relies on a Public Key Infrastructure, which acts as a trusted third-party entity. The solution also follows standards widely used in web applications, such as the TLS/SSL [14, 12] protocols, for the establishment of secure communication channels between the interacting parties, and the OAuth [8] protocol, for access control and authorization of users’ requests.
MSc Resume: Social Behaviour in Non-Player Characters for Role-Playing Games

As people interact more often with virtual environments, such as, for example, computer games, there has been a focus in the study of new ways to improve the user's interaction experience. One of the important factors for a successful experience is the believability of the social context of the environment. This can be achieved by having people interacting with other people (e.g. multi-player games) but this is not always possible or is even not the best solution in some cases. Thus, in many environments, users interact with autonomous characters. There has been much work to make these autonomous characters believable, but it is usually focus on the believability of the characters by themselves, and not in their believability in a social context, which translates into poor social environments, failing to attain a social “illusion of life”. We believe that by giving to characters the ability to have deeper social interaction, not only between the user and characters, but also amongst characters, the user’s interaction experience could be improved. We propose a model to enrich such social interactions centred on the possibility of the agents to establish social relationships among themselves. Furthermore, we present a case study with a game that takes place on one of the most common scenarios of a Role Playing Game, a market, where our model was used to assess its effects on the user experience. In addition, we present the results of an experiment conducted with this game that showed that the suggested model has a positive effect in the users’ gaming experience.
Credit Crunches, Information Failures, and the Persistence of Pessimism∗

This paper examines how financial crises affect the ability of agents to learn about economic fundamentals, and how this in turn affects the transmission of financial shocks through the economy. To this end, we introduce a model where noise in the financial market drives business cycles. Agents endogenously learn about fundamentals from market prices, but financial constraints systematically destroy the informational capacity of prices in financial crises. This is because financially constrained agents stop responding to available information, reducing the efficiency of prices in aggregating information that is dispersed across the economy. As a result, times of financial crisis are marked by both endogenously increasing uncertainty and increasingly persistent pessimism, providing a powerful amplification mechanism for financial shocks. Importantly, this mechanism is inherently nonlinear. Whereas small or positive financial shocks have only a little influence on the economy, unusually adverse shocks virtually shut down market learning and result in dis-proportionately severe and persistent crashes—characterized by substantial losses in employment, output, and asset prices; and high levels of uncertainty, volatility, and risk premia.
SUPERMAN strikes again in legumes

The SUPERMAN (SUP) gene was described in Arabidopsis thaliana over 30 years ago. SUP was classified as a cadastral gene required to maintain the boundaries between reproductive organs, thus controlling stamen and carpel number in flowers. We summarize the information on the characterization of SUP orthologs in plant species other than Arabidopsis, focusing on the findings for the MtSUP, the ortholog in the legume Medicago truncatula. M. truncatula has been widely used as a model system to study the distinctive developmental traits of this family of plants, such as the existence of compound inflorescence and complex floral development. MtSUP participates in the complex genetic network controlling these developmental processes in legumes, sharing conserved functions with SUP. However, transcriptional divergence between SUP and MtSUP provided context-specific novel functions for a SUPERMAN ortholog in a legume species. MtSUP controls the number of flowers per inflorescence and the number of petals, stamens and carpels regulating the determinacy of ephemeral meristems that are unique in legumes. Results obtained in M. truncatula provided new insights to the knowledge of compound inflorescence and flower development in legumes. Since legumes are valuable crop species worldwide, with high nutritional value and important roles in sustainable agriculture and food security, new information on the genetic control of their compound inflorescence and floral development could be used for plant breeding.
Visualizing Software Models

Software development is a complex and di ﬃ cult task that requires the investment of signiﬁcant resources and carries major risks . Model driven development approaches can help these tasks, but they generate thousands of objects and relationships which are difficult to analyse without losing the context, and mostly by proprietary tools. We propose a visualizer for the data model of these applications/information systems that makes use of visualization techniques with the proper filters to allow a user to navigate between objects and abstraction levels without losing the context and/or focus. We used the Design Science Research Methodology to conduct our research. This proposal was demonstrated in a real life company that uses a tool with model-driven-development concepts to develop Information Systems and it helped to identify the objects with higher importance in the evaluated applications. We evaluated it with feedback gathered from interviews and results from the application of the visualizer in real applications. The artifacts usability was also measured through user tests and it has shown that it has a nice usability.
UvA-DARE ( Digital Academic Repository ) Ethnic school context and the national and sub-national identifications of pupils

In various European countries, policy-makers strive for educational desegregation to enhance pupils’ national identifications. Since little empirical evidence supports such a policy and social identity theorists emphasize the importance of context, this article examines the impact of ethnic school composition measured by the proportion of non-natives and ethnic heterogeneity on the national (Belgian) and sub-national (Flemish) identifications of pupils. Multi-level data analyses from the surveying of 2,845 pupils (aged 10 12) in sixty-eight Flemish primary schools reveal differential effects for natives and non-natives. While the proportion of non-natives at school is negatively associated with nonnative pupils’ identifications, it is positively related to native pupils’ identifications. In general, the ethnic heterogeneity of the school is negatively associated with pupils’ national and sub-national identifications. Our findings indicate that the relation between ethnic school composition and pupils’ identifications is mediated by the latter’s interethnic friendships. The consequences of these findings for educational policy are discussed.
Formula Student Championship: an Integrated Data Processing Module Adapted for Pit-Stop Scenarios

This document describes the proposed solution for a software framework and integrated user interface to be deployed in the existing telemetry system installed in the vehicle prototype of IST team that competes in the Formula Student Championship. The solution has two deployment devices, one located in the racing vehicle (referred to as mobile station), and the other located in the pit stop (referred to as pit station). The ultimate goal is to enable the data sent from the mobile station to be stored and displayed to users in the pit station either in real-time or at a later time. Data is acquired from several sensors installed in the car and interconnected by a CAN bus network. This project consists essentially on the evolution of a previous developed software framework to better cater the end user needs. The work comprises important improvements on several modules, such as the communication protocol used between the two stations, the database used to store all the information gathered, the required processing over the gathered data, and the adaptation of a graphical user interface to better fit the user profiles and requisites.
Opinion Page Opinion: Discovery in the Every- day Practice of Science: Abduction and the Logic of Unintended Exper- iments

Generating new ideas – innovation and novelty – is central to what those of us practicing science hope to accomplish. We call it research, but what we really aim for is new-search – discovering new things about the world and how it works. Contrary to the idealised view of science practice typically portrayed in science education, it is important for students to understand that the path to discovery tends to be an adventure, highly ambiguous and convoluted. When scientists write their research papers, they recast these adventures into stories that follow a linear path from hypothesis to discovery and, as a result, hide the real-world complexity of practice. The plot of every good science paper is the scientific method (Grinnell 2009).
Visualization of Electronic Health Record Data for Decision-Making in Diabetes and Congestive Heart Failure

Abstract Objective The aim of this study is to study the impact of graphical representation of health record data on physician decision-making to inform the design of health information technology. Materials and Methods We conducted a within participants crossover design study using a simulated electronic health record (EHR) in which we presented cases with and without visualized data designed to highlight important clinical trends or relationships, followed by assessment of the impact on decision-making about next steps for patients with chronic diseases. We then asked whether trends were observed and about usability and satisfaction using validated usability questions and asked open-ended questions as well. Time to answer questions was also collected. Results Twenty-one primary care providers participated in the study, including five for testing only and sixteen for the full study. Questions about clinical assessment or next actions were answered correctly 55% of the time. Regarding objective trends in the data, participants described noticing the trends 85% of the time. Differences in noticing trends or difficulty level of questions were not statistically significant. Satisfaction with the tool was high and participants agreed strongly that it helped them make better decisions without adding to the time it took. Discussion The simulation allowed us to test the impact of a visualization on clinician practice in a realistic setting. Designers of EHRs should consider the ways information presentation can affect decision-making. Conclusion Testing visualization tools can be done in a clinically realistic context. Providers desire visualizations and believe that they help them make better and faster decisions.
Plant Biotechnology 22(3), 249–251 (2005)

Protoplasts were isolated from embryogenic calluses of Muscari armeniacum ‘Blue Pearl’, which had been subcultured for 3 years. Protoplasts started to divide after 5–7 days of culture, and colonies consisting of 50–100 cells were produced after one month. The highest plating efficiency (10.9%) was obtained by using a medium containing 5.4 mM NAA and 4.4 mM BA, 0.5 M glucose and 2 g l 1 gellan gum. Protoplast-derived calluses produced somatic embryos at frequencies of 4.3–89.6% on media containing 0 or 0.54 mM NAA in combination with 0, 4.4, 22 or 44 mM BA, but few embryos converted into plantlets. On the other hand, over 35% of the calluses produced adventitious shoots on media containing 4.4 mM BA or 0.54 mM NAA in combination with 44 mM BA, and some of these shoots developed into plantlets following transfer to a medium without PGRs.
Finding Key Bloggers, One Post At A Time

A microelectronic package for the protection, housing, cooling and interconnection of a microelectronic chip. The package is made of a plurality of ceramic layers, each of which carries a particular electrically conductive pattern and which have interior openings therein so as to provide recesses in which the chip and discrete capacitors can be located and connected.
MYB pathways that regulate UV-B-induced anthocyanin biosynthesis in blueberry (Vaccinium corymbosum)

Ultraviolet-B (UV-B) promotes anthocyanin accumulation and improves fruit quality in plants. To explore the underlying network of MYB transcription factors that regulates UV-B-induced anthocyanin biosynthesis in blueberry (Vaccinium corymbosum), we analyzed the response of MYB transcription factor genes to UV-B treatment. Transcriptome sequencing analysis revealed that VcMYBA2 and VcMYB114 expression were upregulated and were positively correlated with the expression of anthocyanin structural genes under UV-B radiation according to weighted gene co-expression network analysis (WGCNA) data. The VcUVR8-VcCOP1-VcHY5 pathway perceives UV-B signals and promotes the expression of anthocyanin structural genes by upregulating VcMYBA2 and VcMYB114 or by regulating the VcBBXs-VcMYB pathway, ultimately promoting anthocyanin accumulation. By contrast, VcMYB4a and VcUSP1 were downregulated under UV-B treatment, and VcMYB4a expression was negatively correlated with that of anthocyanin biosynthesis genes in response to UV-B. Analysis of VcMYB4a-overexpressing and wild-type blueberry calli exposed to UV-B radiation revealed that VcMYB4a represses UV-B-induced anthocyanin accumulation. Yeast one-hybrid and dual luciferase assays showed that the universal stress protein VcUSP1 directly bound to the promoter of VcMYB4a. These results suggest that the VcUSP1-VcMYB4a pathway negatively regulates UV-B-induced anthocyanin biosynthesis and provide insight into UV-B-induced anthocyanin biosynthesis.
Fast global stereo matching via energy pyramid minimization

We define a global matching framework based on energy pyramid, the Global Matching via Energy Pyramid (GM-EP) algorithm, which estimates the disparity map from a single stereo-pair by solving an energy minimization problem. We efficiently address this minimization by globally optimizing a coarse to fine sequence of sparse Conditional Random Fields (CRF) directly defined on the energy. This global discrete optimization approach guarantees that at each scale we obtain a near optimal solution, and we demonstrate its superiority over state of the art image pyramid approaches through application to real stereo-pairs. We conclude that multiscale approaches should be build on energy pyramids rather than on image pyramids.
Social Sharing of Emotions for Intelligent Agents

In this work we explore the phenomenon of social sharing of emotions as a way to build agents that can create and maintain social relations with others that are more easily perceived by the user. We argue that agents should not only be endowed with the traditional short lived view of emotions but also with the capability to speak about not recovered past emotional experiences and respond to similar events in socio-aﬀective and cognitive terms. We present a computational model centered around on episodic memory, empathy and cognitive coping strategies. Our model was tested with users in a controlled environment. Re-sults have shown that using our model have a positive impact on the user perception of social relations between the agents involved in the social sharing of emotion phenomenon.
Auto-supervised learning in the Bayesian Programming Framework

Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and completely unsupervised learning, consist in relying on previous knowledge to acquire new skills. We propose here to realize auto-supervised learning by exploiting statistical regularities in the sensorimotor space of a robot. In our context, it corresponds to achieve feature selection in a Bayesian programming framework. We compare several feature selection algorithms and validate them on a real robotic experiment.
EEG-based Music Cross-modal Retrieval

Music Information Retrieval (MIR) is an essential subject nowadays, aiming to aid users in solving the problem of having a vast number of choices and ﬁnding music by a similarity criterion. Nonetheless, the human perception of similarity is highly dependent on the user and MIR systems lack user focus. In order to assess the perception and subjective experience of the user while listening to music, we explore the potential of using EEG signals, in addition to audio signals, to capture this information. For this, we leverage multi-view deep models to learn a shared embedding space between audio and Electroencephalography (EEG) signals, and the resulting embeddings are evaluated on cross-modal retrieval tasks, i.e., given a piece of audio we aim to retrieve the corresponding EEG and vice-versa. Two audio-EEG datasets were collected for training the proposed models: the INESC-ID dataset with 22 participants, a mean of 30 minutes of music per participant repeated twice recorded using the 16-channel OpenBCI, and the CENC dataset containing recordings of 19 participants and recorded with the BrainVision QuickAmp with an electrode-cap of 64 electrodes. Through the extensive cross-modal retrieval tasks performed, we concluded that the learned embeddings encode the intrinsic associations between audio and EEG and should reﬂect the auditory concepts that are perceived and processed by the brain, suggesting the existence of a subject-speciﬁc neural signature able to distinguish music pieces. In addition, we demonstrated the viability of using the commercial device OpenBCI for this application.