Miracle-dz/UniFAR-FTUs huggingface dataset:
A 2-year survey of phytoplankton in the Marne Reservoir (France): A case study to validate the use of an in situ spectrofluorometer by comparison with algal taxonomy and chlorophyll a measurements

For almost two years (2006–2007), phytoplankton structure and distribution were monitored using a combination of methods in the Marne Reservoir (France). We used the bbe FluoroprobeTM spectrofluorometer, which provides vertical profiles for different algal classes and chlorophyll analysis, based on the in vivo autofluorescence characteristics of the phytoplankton. In parallel, we measured chlorophyll a (chl a ) concentrations using the classical extraction method coupled with spectrophotometric analyses, and calculated the biovolume of all the taxa identified using inverted light microscopy. A very close correlation (ρ = 0.93, n = 243, p concentrations given by the bbe FluoroprobeTM and the total chl a concentrations (g·L−1 ) provided by the spectrophotometric analysis. The closest correlation was obtained for concentrations below 6.9 μ g·L−1 whereas little or no correlation was found for those above 21.6 μ g·L−1 . The relationship was highly significant when total phytoplankton biovolume was compared with data from the probe (ρ = 0.6, n = 243, p = 0.67, p = 0.33, p appears to be a reliable tool suitable for use by fresh water managers to monitor phytoplankton on any relevant time and space scales.
Integrated primary care: patient perceptions and the role of mental health stigma

Abstract Some patients are more willing to see a behavioral health provider within primary care. The purpose of this study was to evaluate the patients’ perspectives of having access to a psychologist within primary care and to investigate whether mental health stigma affected preferences. In total, 36 patients completed questionnaires after seeing a psychologist in primary care. Patients were satisfied with having a primary care psychologist involved in their care. Most patients were more likely to see the psychologist in primary care and those who preferred this indicated higher levels of mental health stigma. The overarching theme for why patients saw a psychologist in primary care was convenience. Mental health stigma may also have played a role. Results suggest that providing integrated services may reach patients who may not have otherwise sought services in a behavioral health clinic. Findings from this study encourage the continued integration of behavioral health services.
Preliminary Efficacy of a Friendly Constraint-Induced Therapy (Friendly-CIT) Program on Motor and Psychosocial Outcomes in Children with Cerebral Palsy

ABSTRACT Aims: Constraint-induced therapy (CIT) is effective, but concerns have been repeatedly raised regarding the generalizability, feasibility, and potentially intrusive nature of restraining a child's unimpaired upper limb. We examined the feasibility and efficacy of friendly-CIT, which uses home-based model, a caregiver-determined schedule, and gentle restraint, in children with cerebral palsy. Methods: In a one-group pre-post intervention trial, 15 children (median of age = 8 years, 4 months) participated in 8 weeks of friendly-CIT (36 hours). Motor and psychosocial outcomes were investigated. Results: Participants showed significant improvement on the manual dexterity subtest of the Bruininks-Oseretsky Test of Motor Proficiency (p = 0.004); the how often (p = 0.01) and how well (p = 0.02) scales of the Revised Pediatric Motor Activity Log; and the functional skills score on the Chinese version of the Pediatric Evaluation of Disability Inventory (p = 0.002). For psychosocial outcomes, children with CP consistently demonstrated high engagement and increased playfulness (p < 0.0001) during the 8-week intervention. Parents showed no change in stress status and high satisfaction with friendly-CIT. Conclusion: Friendly-CIT is feasible and promising for improving a child's motor and psychosocial outcomes without increasing parental stress.
Epigenetic maintenance of topological domains in the highly rearranged gibbon genome

The relationship between evolutionary genome remodeling and the three-dimensional structure of the genome remain largely unexplored. Here, we use the heavily rearranged gibbon genome to examine how evolutionary chromosomal rearrangements impact genome-wide chromatin interactions, topologically associating domains (TADs), and their epigenetic landscape. We use high-resolution maps of gibbon–human breaks of synteny (BOS), apply Hi-C in gibbon, measure an array of epigenetic features, and perform cross-species comparisons. We find that gibbon rearrangements occur at TAD boundaries, independent of the parameters used to identify TADs. This overlap is supported by a remarkable genetic and epigenetic similarity between BOS and TAD boundaries, namely presence of CpG islands and SINE elements, and enrichment in CTCF and H3K4me3 binding. Cross-species comparisons reveal that regions orthologous to BOS also correspond with boundaries of large (400–600 kb) TADs in human and other mammalian species. The colocalization of rearrangement breakpoints and TAD boundaries may be due to higher chromatin fragility at these locations and/or increased selective pressure against rearrangements that disrupt TAD integrity. We also examine the small portion of BOS that did not overlap with TAD boundaries and gave rise to novel TADs in the gibbon genome. We postulate that these new TADs generally lack deleterious consequences. Last, we show that limited epigenetic homogenization occurs across breakpoints, irrespective of their time of occurrence in the gibbon lineage. Overall, our findings demonstrate remarkable conservation of chromatin interactions and epigenetic landscape in gibbons, in spite of extensive genomic shuffling.
Cerebrovascular Resistance: The Basis of Cerebrovascular Reactivity

The cerebral vascular network regulates blood flow distribution by adjusting vessel diameters, and consequently resistance to flow, in response to metabolic demands (neurovascular coupling) and changes in perfusion pressure (autoregulation). Deliberate changes in carbon dioxide (CO2) partial pressure may be used to challenge this regulation and assess its performance since CO2 also acts to change vessel diameter. Cerebrovascular reactivity (CVR), the ratio of cerebral blood flow (CBF) response to CO2 stimulus is currently used as a performance metric. However, the ability of CVR to reflect the responsiveness of a particular vascular region is confounded by that region’s inclusion in the cerebral vascular network, where all regions respond to the global CO2 stimulus. Consequently, local CBF responses reflect not only changes in the local vascular resistance but also the effect of changes in local perfusion pressure resulting from redistribution of flow within the network. As a result, the CBF responses to CO2 take on various non-linear patterns that are not well-described by straight lines. We propose a method using a simple model to convert these CBF response patterns to the pattern of resistance responses that underlie them. The model, which has been used previously to explain the steal phenomenon, consists of two vascular branches in parallel fed by a major artery with a fixed resistance unchanging with CO2. One branch has a reference resistance with a sigmoidal response to CO2, representative of a voxel with a robust response. The other branch has a CBF equal to the measured CBF response to CO2 of any voxel under examination. Using the model to calculate resistance response patterns of the examined branch showed sigmoidal patterns of resistance response, regardless of the measured CBF response patterns. The sigmoid parameters of the resistance response pattern of examined voxels may be mapped to their anatomical location. We show an example for a healthy subject and for a patient with steno-occlusive disease to illustrate. We suggest that these maps provide physiological insight into the regulation of CBF distribution.
Comparison of DCO-OFDM and M-PAM for LED-Based Communication Systems

Light-emitting diode (LED)-based communications, such as visible light communications (VLC) and infrared (IR) communications, are candidate techniques to provide short-range and high-speed data transmission. In this paper, M-ary pulse amplitude modulation (M-PAM), used as a high bandwidth efficiency scheme, is compared with an optimized DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme. Considering the bandwidth limit and constrained peak transmitted power characteristics of LEDs, a bit loading algorithm with an optimized modulation index is used for the DCO-OFDM. To reduce the inter-symbol interference caused by LEDs, a waveform design algorithm with a minimum mean squared error (MMSE) equalizer is applied to the M-PAM system. From numerical results, M-PAM with the optimized signal processing can provide a substantially higher data rate than the optimally designed DCO-OFDM for the same performance.
A Review of User Interface Design for Interactive Machine Learning

Interactive Machine Learning (IML) seeks to complement human perception and intelligence by tightly integrating these strengths with the computational power and speed of computers. The interactive process is designed to involve input from the user but does not require the background knowledge or experience that might be necessary to work with more traditional machine learning techniques. Under the IML process, non-experts can apply their domain knowledge and insight over otherwise unwieldy datasets to find patterns of interest or develop complex data-driven applications. This process is co-adaptive in nature and relies on careful management of the interaction between human and machine. User interface design is fundamental to the success of this approach, yet there is a lack of consolidated principles on how such an interface should be implemented. This article presents a detailed review and characterisation of Interactive Machine Learning from an interactive systems perspective. We propose and describe a structural and behavioural model of a generalised IML system and identify solution principles for building effective interfaces for IML. Where possible, these emergent solution principles are contextualised by reference to the broader human-computer interaction literature. Finally, we identify strands of user interface research key to unlocking more efficient and productive non-expert interactive machine learning applications.
Revisiting Context-Based Authentication in IoT

The emergence of IoT poses new challenges towards solutions for authenticating numerous very heterogeneous IoT devices to their respective trust domains. Using passwords or pre-defined keys have drawbacks that limit their use in IoT scenarios. Recent works propose to use contextual information about ambient physical properties of devices' surroundings as a shared secret to mutually authenticate devices that are co-located, e.g., the same room. In this paper, we analyze these context-based authentication solutions with regard to their security and requirements on context quality. We quantify their achievable security based on empirical real-world data from context measurements in typical IoT environments.
Three-dimensional visualization and navigation tool for diagnostic and surgical planning applications

This study aims at providing the radiologist and the surgeon with a diagnostic and planning tool. To this end multimodal (T1, T2 and PD-weighted) sets of MR images representing a human head and a human knee and without neoplastic formations were acquired. All the software was developed in C++ language using Open Graphics Library (OpenGL) and OpenGL Volumizer. It was tested on a Silicon Graphics O2 workstation. The medical user can rotate along the x-y-z axes the volume under investigation and zoom in and out the data, can make cuts of the set of images in all directions and display volume intersections with the three conventional anatomical planes. By enfolding the volume in a cube and by moving its apexes, the user can dig the volume. The surfaces of the anatomical districts can be visualized. The tool renders a composite volumetric image by using the false-coloring technique and it can combine morphological information of the surface and data about the nature of the volume by using the different distribution of the intensity levels of the pixels. It is also possible to set transparency to obtain an image representing simultaneously the 3D volume and its internal structure. The tool can display surface information and volume information at the same time and provides endo-navigation facility that helps the user to move into an anatomical district in order to find the correct position of potential lesions and the way to remove them.
Poster: Predicting the Fault Revelation Utility of Mutants

Mutation testing is one of the strongest code-based test criteria. However, it is expensive as it involves a large number of mutants. To deal with this issue we propose a machine learning approach that learns to select fault revealing mutants. Fault revealing mutants are valuable to testers as their killing results in (collateral) fault revelation. We thus, formulate mutant reduction as the problem of selecting the mutants that are most likely to lead to test cases that uncover unknown program faults. We tackle this problem using a set of static program features and machine learning. Experimental results involving 1,629 real faults show that our approach reveals 14% to 18% more faults than a random mutant selection baseline.
Neural Machine Translation Enhancements through Lexical Semantic Network

In most languages, many words have multiple senses, thus machine translation systems have to choose between several candidates representing different senses of an input word. Although neural machine translation has recently become a dominant paradigm and achieved great progress, it still has to confront with the challenge of word sense disambiguation. Neural machine translation models are trained to identify the correct sense of a word as part of an end-to-end translation task, and their performances on word sense disambiguation are not satisfactory. This paper presents a case study of machine translation for Korean language. We have manually built a Korean lexical semantic network - UWordMap - as a large-scale lexical semantic knowledge-based in which each sense of every polysemous word is associated with a sense-code constituting a network node. Then, based on UWordMap, we determine the correct sense and tag the appropriated sense-code for polysemous words of the training corpus before training neural machine translation models. Experiments on translation from Korean to English and Vietnamese show that UWordMap can significantly improve quality of Korean neural machine translation systems in terms of BLEU and TER cores.
Packet Pump: Overcoming Network Bottleneck in On-Chip Interconnects for GPGPUs*

In order to fully exploit GPGPU's parallel processing power, on-chip interconnects need to provide bandwidth efficient data communication. GPGPUs exhibit a many-to-few-to-many traffic pattern which makes the memory controller connected routers the network bottleneck. Inefficient design of conventional routers causes long queues of packets blocked at memory controllers and thus greatly constrained the network bandwidth. In this work, we employ heterogeneous design techniques and propose a novel decoupled architecture for routers connected with memory controllers. To further improve performance, we propose techniques called Injection Virtual Circuit and Memory-aware Adaptive Routing. We show that our scheme can effectively eliminate NoC bottleneck and improve performance by 78% on average.
Semantic Video Segmentation: A Review on Recent Approaches

This paper gives an overview on semantic segmentation consists of an explanation of this field, it's status and relation with other vision fundamental tasks, different datasets and common evaluation parameters that have been used by researchers. This survey also includes an overall review on a variety of recent approaches (RDF, MRF, CRF, etc.) and their advantages and challenges and shows the superiority of CNN-based semantic segmentation systems on CamVid and NYUDv2 datasets. In addition, some areas that is ideal for future work have mentioned.
Dadu-P: A Scalable Accelerator for Robot Motion Planning in a Dynamic Environment

As a critical operation in robotics, motion planning consumes lots of time and energy, especially in a dynamic environment. Through approaches based on general-purpose processors, it is hard to get a valid planning in real time. We present an accelerator to speed up collision detection, which costs over 90% of the computation time in motion planning. Via the octree-based roadmap representation, the accelerator can be reconfigured online and support large roadmaps. We in addition propose an effective algorithm to update the roadmap in a dynamic environment, together with a batched incremental processing approach to reduce the complexity of collision detection. Experimental results show that our accelerator achieves 26.5X speedup than an existing CPU-based approach. With the incremental approach, the performance further improves by 10X while the solution quality is degraded by 10% only.
SQLInspect: A Static Analyzer to Inspect Database Usage in Java Applications

We present SQLInspect, a tool intended to assist developers who deal with SQL code embedded in Java applications. It is integrated into Eclipse as a plug-in that is able to extract SQL queries from Java code through static string analysis. It parses the extracted queries and performs various analyses on them. As a result, one can readily explore the source code which accesses a given part of the database, or which is responsible for the construction of a given SQL query. SQL-related metrics and common coding mistakes are also used to spot inefficiently or defectively performing SQL statements and to identify poorly designed classes, like those that construct many queries via complex control-flow paths. SQLInspect is a novel tool that relies on recent query extraction approaches. It currently supports Java applications working with JDBC and SQL code written for MySQL or Apache Impala. Check out the live demo of SQLInspect at http://perso.unamur. be/~cnagy/sqlinspect.
CamPUF: Physically Unclonable Function based on CMOS Image Sensor Fixed Pattern Noise

Physically unclonable functions (PUFs) have proved to be an effective measure for secure device authentication and key generation. We propose a novel PUF design, named CamPUF, based on commercial off-the-shelf CMOS image sensors, which are ubiquitously available in almost all mobile devices. The inherent process mismatch between pixel sensors and readout circuits in an image sensor manifests as unique fixed pattern noise (FPN) in the image. We exploit FPN caused by dark signal non-uniformity (DSNU) as the basis for implementing the PUF. DSNU can be extracted only from dark images that are not shared with others, and only the legitimate user can obtain it with full control of the image sensor. Compared to other FPN components that can be extracted from shared images, DSNU facilitates more secure and usable device authentication. We present an eficient and reliable key generation procedure for use in wireless low-power devices. We implement CamPUF on Google Nexus 5X and Nexus 5 and evaluate the uniqueness and robustness of the keys, as well as its security against counterfeiting. We demonstrate that it discriminates legitimate and illegitimate authentication attempts without confusion.
Proteomic Architecture of Human Coronary and Aortic Atherosclerosis

Backgound: The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets. Methods: Here we describe the human arterial proteome and proteomic features strongly associated with early atherosclerosis based on mass spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling, and bioinformatic analyses defined the composition, network rewiring, and likely regulatory features of the protein networks associated with early atherosclerosis and how they vary across 2 anatomic distributions. Results: The data document significant differences in mitochondrial protein abundance between coronary and aortic samples (coronary>>aortic), and between atherosclerotic and normal tissues (atherosclerotic<<normal), and major alterations in tumor necrosis factor, insulin receptor, peroxisome proliferator-activated receptor-&agr;, and peroxisome proliferator-activated receptor-&ggr; protein networks, as well, in the setting of early disease. In addition, a subset of tissue protein biomarkers indicative of early atherosclerosis was shown to predict anatomically defined coronary atherosclerosis when measured in plasma samples in a separate clinical cohort (area under the curve=0.92 [0.83–0.96]), thereby validating the use of human tissue proteomics to discover relevant plasma biomarkers for clinical applications. In addition to the specific proteins and pathways identified here, the publicly available data resource and the analysis pipeline used illustrate a strategy for interrogating and interpreting the proteomic architecture of tissues that may be relevant for other chronic diseases characterized by multicellular tissue phenotypes. Conclusions: The human arterial proteome can be viewed as a complex network whose architectural features vary considerably as a function of anatomic location and the presence or absence of atherosclerosis. The data suggest important reductions in mitochondrial protein abundance in early atherosclerosis and also identify a subset of plasma proteins that are highly predictive of angiographically defined coronary disease.
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification

In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been widely adopted for the task of image set classification. Recently, it has been proven that infinite-dimensional CovDs are more discriminative than their low-dimensional counterparts. However, the form of infinite-dimensional CovDs is implicit and the computational load is high. We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nyström method based on a Riemannian kernel. We start by modeling the images via CovDs, which lie on the Riemannian manifold spanned by SPD (Symmetric Positive Definite) matrices. We then extend the Nyström method to the SPD manifold and obtain the approximations of CovDs in RKHS (Reproducing Kernel Hilbert Space). Finally, we approximate infinite-dimensional CovDs via these approximations. Empirically, we apply our framework to the task of image set classification. The experimental results obtained on three benchmark datasets show that our proposed approximate infinite-dimensional CovDs outperform the original CovDs.