Miracle-dz/UniFAR-FTUs huggingface dataset:
Renyi Differential Privacy Mechanisms for Posterior Sampling

Using a recently proposed privacy definition of Renyi Differential Privacy (RDP), we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In particular, we focus on sampling from an exponential family and specific generalized linear models, such as logistic regression. We propose novel RDP mechanisms as well as offering a new RDP analysis for an existing method in order to add value to the RDP framework. Each method is capable of achieving arbitrary RDP privacy guarantees, and we offer experimental results of their efficacy.
Real-Time Processing of Heterogeneous Data in Sensor-Based Systems

Nowadays the applications for real time processing of large amounts of data are encountered increasingly more frequently, as there are lots of system's types that can generate large comprehensive information in a relatively short time. In this paper we focus on sensor-based systems. Such systems may be found in several important domains, such as smart farming, medical field, water management, or smart cities. The proposed solution in this paper has the capacity to analyze data streams from different sensors but also considers historical data, in order to provide alerts or invoke different services. This is a new approach, as, to our knowledge, none of the existing stream-processing solutions support combining streaming with batch processing data. We tested our solution with data from sensors and actuators, using a smart farm test scenario.
Insulin Signaling in the Peripheral and Central Nervous System Regulates Female Sexual Receptivity during Starvation in Drosophila

Many animals adjust their reproductive behavior according to nutritional state and food availability. Drosophila females for instance decrease their sexual receptivity following starvation. Insulin signaling, which regulates many aspects of insect physiology and behavior, also affects reproduction in females. We show that insulin signaling is involved in the starvation-induced reduction in female receptivity. More specifically, females mutant for the insulin-like peptide 5 (dilp5) were less affected by starvation compared to the other dilp mutants and wild-type flies. Knocking-down the insulin receptor, either in all fruitless-positive neurons or a subset of these neurons dedicated to the perception of a male aphrodisiac pheromone, decreased the effect of starvation on female receptivity. Disrupting insulin signaling in some parts of the brain, including the mushroom bodies even abolished the effect of starvation. In addition, we identified fruitless-positive neurons in the dorso-lateral protocerebrum and in the mushroom bodies co-expressing the insulin receptor. Together, our results suggest that the interaction of insulin peptides determines the tuning of female sexual behavior, either by acting on pheromone perception or directly in the central nervous system.
Intrathecal Administration of Proteinase-Activated Receptor-2 Agonists Produces Hyperalgesia by Exciting the Cell Bodies of Primary Sensory Neurons

Proteinase-activated receptors (PARs) are a family of G-protein-coupled receptors that are activated by endogenous serine proteinases that cleave the N-terminal domain of the receptor unmasking a “tethered ligand” sequence. Trypsin and other agonists at PAR2 act on peripheral nerves to augment the transfer of nociceptive information. We tested whether PAR2 agonists also exert a spinal pronociceptive effect by i.t. administering the selective ligand, Ser-Leu-Ile-Gly-Arg-Leu-NH2 (SLI-GRL). This produced thermal and mechanical hyperalgesia in rats and mice and augmented mechanical and thermal hyperalgesia seen in the formalin inflammatory pain test. Effects of SLIGRL were abrogated in PAR2-deficient mice and were not seen with the inactive control peptide, Leu-Arg-Gly-Ile-Leu-Ser-NH2. Surprisingly, electrophysiological studies, using whole-cell recording from rat substantia gelatinosa neurons, failed to demonstrate an increase in excitatory transmission or neuronal excitability following treatment with SLIGRL or trypsin. In fact, the actions of trypsin were consistent with a decrease in dorsal horn excitability. SLIGRL and trypsin did, however, depolarize and increase the excitability of large, medium and small primary afferent, dorsal root ganglion neurons. The effects were associated with an increase in conductance at hyperpolarized potentials and a decrease in conductance at depolarized potentials. PAR2-like immunoreactivity was found in DRG but not in spinal dorsal horn. These results suggest that activation of DRG neuron cell bodies may account for the pronociceptive actions of i.t. applied PAR2 agonists. They also imply that pathophysiological release of PAR2-activating proteases in the vicinity of DRG neurons may produce profound effects on nociceptive processing in vivo.
Large-scale music tag recommendation with explicit multiple attributes

Social tagging can provide rich semantic information for large-scale retrieval in music discovery. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic of significance for researchers. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. Many of these are underrepresented by current tag recommenders. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensuring attribute diversity. Once the user uploads or browses a song, the system recommends a list of relevant tags in each attribute independently. To the best of our knowledge, this is the first method to consider Explicit Multiple Attributes for tag recommendation. Our system is designed for large-scale deployment, on the order of millions of objects. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. We evaluate our tag recommendation system on CAL-500 and a large-scale data set ($N = 77,448$ songs) generated by crawling Youtube and Last.fm. Our results indicate that our proposed method is both effective for recommending attribute-diverse relevant tags and efficient at scalable processing.
A piecewise-affine approach to observer design for a class of nonlinear systems linear in the input with a sampled output

This paper addresses the design of continuous-time piecewise-affine (PWA) observers for a class of nonlinear systems with sampled output. Building on observer theory for PWA systems, sufficient conditions are proposed such that a continuous-time PWA observer can be used to estimate the states of a nonlinear system linear in the input with sampled output yielding a convergent state estimation error. The method for observer design is a convex optimization approach in terms of linear matrix inequalities (LMIs). It is shown that the state estimation error, which is ultimately bounded, converges to a region and the size of the region depends on the sampling time and the PWA approximation error. The size of the region decreases as the sampling time and/or the PWA approximation error decrease. Results independent of the sampling time and dependent on the sampling time are provided. An example in robotics shows the effectiveness of the proposed approach.
Bioresponsive Mesoporous Silica Nanoparticles for Triggered Drug Release

Mesoporous silica nanoparticles (MSNPs) have garnered a great deal of attention as potential carriers for therapeutic payloads. However, achieving triggered drug release from MSNPs in vivo has been challenging. Here, we describe the synthesis of stimulus-responsive polymer-coated MSNPs and the loading of therapeutics into both the core and shell domains. We characterize MSNP drug-eluting properties in vitro and demonstrate that the polymer-coated MSNPs release doxorubicin in response to proteases present at a tumor site in vivo, resulting in cellular apoptosis. These results demonstrate the utility of polymer-coated nanoparticles in specifically delivering an antitumor payload.
Knowledge Infusion

The question of how machines can be endowed with the ability to acquire and robustly manipulate commonsense knowledge is a fundamental scientific problem. Here we formulate an approach to this problem that we call knowledge infusion. We argue that robust logic offers an appropriate semantics for this endeavor because it supports provably efficient algorithms for a basic set of necessary learning and reasoning tasks. We observe that multiple concepts can be learned simultaneously from a common data set in a data efficient manner. We also point out that the preparation of appropriate teaching materials for training systems constructed according to these principles raises new challenges.
Towards Deep Learning Invariant Pedestrian Detection by Data Enrichment

Deep learning models have recently achieved the state-of-the-art results on a well-known pedestrian detection dataset. However, such images were obtained from open scenarios with fixed imaging geometry parameters, which may produce a network not suitable for detecting a person in more general settings, such as the ones found in surveillance systems. As gathering and annotating data is a highly expensive manual task, we propose a methodology for artificially augmenting the positive training set with automatically generated local image affine and perspective transforms. Furthermore, to enrich the variability of background images, we include to the negative training set images that resemble human figures automatically obtained by the proposed methodology over images from commonly found surveillance scenarios. Extensive results show that by providing the enriched data as the input to a Convolutional Neural Network it is possible to precisely detect pedestrians in a number of public datasets. The data enrichment proposed here may also be used in other detectors based on supervised learning architectures, as the process is independent from the learning algorithm employed.
Broadband Adoption| Free Library Hot Spots: Supporting Broadband Adoption in Philadelphia's Low-Income Communities

Earlier studies of broadband adoption have focused on Internet use in the home. This article suggests that Internet use outside the home can provide a context in which meaningful measures of broadband adoption can be developed. Findings are presented from a study of the Free Library of Philadelphia Hot Spots, which used an innovative community engagement strategy to bring broadband technology and library services to underserved neighborhoods. The study shows that the sense of comfort (i.e., support, trust, safety, and respect) at the Hot Spots was important to residents as a precursor to technology access and use, and it suggests that these factors be considered in broadband policies to support sustainable broadband adoption in low-income communities.
Quantifying the language of British politics, 1880-1910

Historians – influenced by the ‘linguistic turn’ – currently study political language almost exclusively qualitatively, through sharply focused case studies. This approach has many strengths but has limited power to examine national long-term political change over such vast discourses as general election campaigns. This article contends that electronic quantitative text analysis methods (inspired by corpus linguistics) could potentially reshape historians' understanding of the language of British electoral politics by providing a powerful ability holistically to assess the typicality, scope and power of key issues, ideologies, personalities and ideas across huge texts. It presents three supporting case studies – on Irish Home Rule, imperialism and William Gladstone – where a multi-million word ‘corpus’ of election speeches from this period is interrogated by computer.
Detecting Structural Changes in Whole Brain Based on Nonlinear Deformations—Application to Schizophrenia Research

This paper describes a new method for detecting structural brain differences based on the analysis of deformation fields. Deformations are obtained by an intensity-based nonlinear registration routine that transforms one brain onto another one. We present a general multivariate statistical approach to analyze deformation fields in different subjects. This method was applied to the brains of 85 schizophrenic patients and 75 healthy volunteers to examine whether low frequency deformations are sufficiently sensitive to detect regional deviations in the brains of both groups. We observed significant changes caused by volume reduction in brains of schizophrenics bilaterally in the thalamus and in the superior temporal gyrus. On the left side, the superior frontal gyrus and precentral gyrus are found to be changed, while on the right side, the middle frontal gyrus was altered. In addition, there were significant changes in the occipital lobe (left lingual gyrus) and in the left cerebellum. Volume enlargement in brains of schizophrenics was observed in the right putamen and in the adjacent white matter of the thalamic region. Our data suggest a disturbance in the nodes of a prefrontal-thalamic-cerebellar circuitry. This provides further support for the model of "cognitive dysmetria," which postulates a disruption in these nodes. We have demonstrated the application of deformation-based morphometry by detecting structural changes in the whole brain. This technique is fully automatic, thus allowing for the inclusion of large samples, with no user bias or a priori-defined regions of interest.
Developing a cumulative anatomic scoring system for military perineal and pelvic blast injuries

Background Improvised explosive device (IED) yields in Afghanistan have increased resulting in more proximal injuries. The injury severity score (ISS) is an anatomic aggregate score of the three most severely injured anatomical areas but does not accurately predict severity in IED related pelvi-perineal trauma patients. A scoring system based on abbreviated injury score (AIS) was developed to reflect the severity of these injuries in order to better understand risk factors, develop a tool for future audit and improve performance. Method Using standard AIS descriptors, injury scales were constructed for the pelvis (1, minor to 6, maximal). The perineum was divided into anterior and posterior zones as relevant to injury patterns and blast direction with each soft tissue structure being allocated a score from its own severity scale. A cumulative score, from 1 to 36 for soft tissue, or a maximum of 42 if a pelvic fracture was involved, was created for all structures injured in the anterior and posterior zones. Results Using this new scoring system, 77% of patients survived with a pelvi-perineal trauma score (PPTS) below 5. There was a significant increase in mortality, number of pelvic fractures and amputations with increase in score when comparing the first group (score 1–5) to the second group (score 6–10). For scores between 6 and 16 survival was 42% and 22% for scores between 17 and 21. In our cohort of 62 survivors, 1 patient with an IED related pelvi-perineal injury had a ‘theoretically un-survivable’ maximal ISS of 75 and survived, whereas there were no survivors with a PPTS greater than 22 but this group had no-one with an ISS of 75 suggesting ISS is not an accurate reflection of the true severity of pelvi-perineal blast injury. Conclusions This scoring system is the initial part of a more complex logistic regression model that will contribute towards a unique trauma scoring system to aid surgical teams in predicting fluid requirements and operative timelines. In austere environments, it may also help to prevent futile resuscitations. Better correlation between measurement of severity and outcome would aid performance improvement monitoring. In the longer term it will also allow benchmarking of current survival rates and comparisons in the future.
Addressing Manufacturing Challenges with Cost-Efficient Fault Tolerant Routing

The high-performance computing domain is enriching with the inclusion of Networks-on-chip (NoCs) as a key component of many-core (CMPs or MPSoCs) architectures. NoCs face the communication scalability challenge while meeting tight power, area and latency constraints. Designers must address new challenges that were not present before. Defective components, the enhancement of application-level parallelism or power-aware techniques may break topology regularity, thus, efficient routing becomes a challenge.In this paper, uLBDR (Universal Logic-Based Distributed Routing) is proposed as an efficient logic-based mechanism that adapts to any irregular topology derived from 2D meshes, being an alternative to the use of routing tables (either at routers or at end-nodes). uLBDR requires a small set of configuration bits, thus being more practical than large routing tables implemented in memories. Several implementations of uLBDR are presented highlighting the trade-off between routing cost and coverage. The alternatives span from the previously proposed LBDR approach (with 30\% of coverage) to the uLBDR mechanism achieving full coverage. This comes with a small performance cost, thus exhibiting the trade-off between fault tolerance and performance.
Show me your brain! Stories of interdisciplinary knowledge creation in practice. Experiences and observations from Aalto Design Factory, Finland.

Ulla-Maija Uusitalo Show me your brain! Stories of interdisciplinary knowledge creation in practice. Experiences and observations from Aalto Design Factory, Finland. Lappeenranta 2015 295 pages Acta Universitatis Lappeenrantaensis 649 Diss. Lappeenranta University of Technology ISBN 978-952-265-819-7, ISBN 978-952-265-820-3 (PDF), ISSN-L 1456-4491, ISSN 14564491 This dissertation centres on the themes of knowledge creation, interdisciplinarity and knowledge work. My research approaches interdisciplinary knowledge creation (IKC) as practical situated activity. I argue that by approaching IKC from the practice-based perspective makes it possible to “deconstruct” how knowledge creation actually happens, and demystify its strong intellectual, mentalistic and expertise-based connotations. I have rendered the work of the observed knowledge workers into something ordinary, accessible and routinized. Consequently this has made it possible to grasp the pragmatic challenges as well the concrete drivers of such activity. Thus the effective way of organizing such activities becomes a question of organizing and leading effective everyday practices. To achieve that end, I have conducted ethnographic research of one explicitly interdisciplinary space within higher education, Aalto Design Factory in Helsinki, Finland, where I observed how students from different disciplines collaborated in new product development projects. I argue that IKC is a multi-dimensional construct that intertwines a particular way of doing; a way of experiencing; a way of embodied being; and a way of reflecting on the very doing itself. This places emphasis not only the practices themselves, but also on the way the individual experiences the practices, as this directly affects how the individual practices. My findings suggest that in order to effectively organize and execute knowledge creation activities organizations need to better accept and manage the emergent diversity and complexity inherent in such activities. In order to accomplish this, I highlight the importance of understanding and using a variety of (material) objects, the centrality of mundane everyday practices, the acceptance of contradictions and negotiations well as the role of management that is involved and engaged. To succeed in interdisciplinary knowledge creation is to lead not only by example, but also by being very much present in the very everyday practices that make it happen.
Automatic predicate abstraction of C programs

Model checking has been widely successful in validating and debugging designs in the hardware and protocol domains. However, state-space explosion limits the applicability of model checking tools, so model checkers typically operate on abstractions of systems. Recently, there has been significant interest in applying model checking to software. For infinite-state systems like software, abstraction is even more critical. Techniques for abstracting software are a prerequisite to making software model checking a reality. We present the first algorithm to automatically construct a predicate abstraction of programs written in am industrial programming language such as C, and its implementation in a tool -- C2BP. The C2BP tool is part of the SLAM toolkit, which uses a combination of predicate abstraction, model checking, symbolic reasoning, and iterative refinement to statically check temporal safety properties of programs. Predicate abstraction of software has many applications, including detecting program errors, synthesizing program invariants, and improving the precision of program analyses through predicate sensitivity. We discuss our experience applying the C2BP predicate abstraction tool to a variety of problems, ranging from checking that list-manipulating code preserves heap invariants to finding errors in Windows NT device drivers.
Attachment, Relational-Needs, and Psychotherapeutic Presence

Humans require the contactful presence of another person who is attuned and responsive to relational-needs. Insecure attachment patterns are the result of repeated disruptions in significant relationships. This article describes eight relational-needs that, when repeatedly unsatisfied, lead to insecure attachment patterns based on the fears of loss of relationship, vulnerability, violation, and invasion.The healing of insecure attachment patterns occurs through a contactful psychotherapeutic presence that occurs when the attitude, behavior and communication of the psychotherapist consistently respects and enhances the client's integrity while responding to relational-needs. The article is the Keynote Address given at the 5th International Integrative Psychotherapy Association Conference in Vichy, France, April 21, 2011.