Miracle-dz/UniFAR-FTUs huggingface dataset:
Parieto-Frontal Connectivity during Visually Guided Grasping

Grasping an object requires processing visuospatial information about the extrinsic features (spatial location) and intrinsic features (size, shape, orientation) of the object. Accordingly, manual prehension has been subdivided into a reach component, guiding the hand toward the object on the basis of its extrinsic features, and a grasp component, preshaping the fingers around the center of mass of the object on the basis of its intrinsic features. In neural terms, this distinction has been linked to a dedicated dorsomedial “reaching” circuit and a dorsolateral “grasping” circuit that process extrinsic and intrinsic features, linking occipital areas via parietal regions with the dorsal and ventral premotor cortex, respectively. We have tested an alternative possibility, namely that the relative contribution of the two circuits is related to the degree of on-line control required by the prehension movement. We used dynamic causal modeling of functional magnetic resonance imaging time series to assess how parieto-frontal connectivity is modulated by planning and executing prehension movements toward objects of different size and width. This experimental manipulation evoked different movements, with different planning and execution phases for the different objects. Crucially, grasping large objects increased inter-regional couplings within the dorsomedial circuit, whereas grasping small objects increased the effective connectivity of a mainly dorsolateral circuit, with a degree of overlap between these circuits. These results argue against the presence of dedicated cerebral circuits for reaching and grasping, suggesting that the contributions of the dorsolateral and the dorsomedial circuits are a function of the degree of on-line control required by the movement.
Modeling of ECC materials using numerical formulations based on plasticity

Engineered Cementitious Composite (ECC), is a high performance fiber-reinforced cementitious composite. In contrast to conventional Fiber-Reinforced Concrete (FRC), ECC is characterized by its enhanced ability to undergo strain-hardening in tension. Since the introduction of ECC less than two decades ago a large amount of research has been conducted on ECC and it has been used for some large field applications. To extend the use of the material, special attention has to be given to a number of areas: the material characteristics found in laboratories has to be recreated on construction sites, the test methods should be standardized and more advanced material models are needed to utilize the special characteristic of the ECC-material. In the present thesis a material model for ECC is derived, and implemented in a Finite Element Method (FEM) program. The model differs from existing models by combining a matrix and a fiber description to describe the behavior of the ECC-material. Using this formulation the model is able to provide information about crack orientation, opening and spacing, which is important knowledge in the multiple cracking state. The model is based on the smeared fixed, multiple cracking approach and the matrix crack is described using a plasticity damage mechanics model. The model is able to capture the dilation effect that will appear during mode II crack opening. In the thesis the model has been tested on both the material scale and the construction scale. To derive the material model a multi scale, approach was employed. On each length scale important phenomena were investigated using numerical and analytical calculations. The mechanisms found on the small scales were then incorporated in the models on the larger scales. On the micro scale, it was examined whether the ECC matrix was adequately described using Linear Elastic Fracture Mechanics (LEFM) or whether a cohesive approach was more appropriate. The ECC matrix is a cementitious material with small aggregate and such materials are often described using LEFM. The investigation showed that even for such brittle materials the cohesive zone was long enough to eliminate the use of LEFM. Using the cohesive approach more realistic first crack strengths could be predicted and the sensitivity toward the size of initial defects diminished. On the meso scale numerical simulations have been used to shed light on whether or
Evaluation of Proactive Congestion Control on RED Gateway

In this paper we investigate the network performance of TCP hosts and gateways employing New Reno TCP using a parallel simulator. The gateway adopts the random early detection (RED) scheme and proactive congestion control mechanism to enforce fair sharing among competing TCP connections. The test network is modeled using PARSEC. The simulation reveals that the RED gateway drops only a small number of packets, and utilization of the bottleneck link on the RED gateway is less than 75%. The number of retransmission timeouts recorded at the TCP senders is also small. Moreover, increasing the bandwidth of the gateway input links does not substantially improve the network performance, while increasing the number of input links is very effective.
Trade Liberalisation, Market Structure and the Incentive to Merge

In this article, we consider whether a movement towards freer international trade generates incentives for firms to merge and if so what forms of merger are most profitable. In a linear Cournot framework, we show that a reduction in trade costs may, but will not necessarily, encourage mergers. Both market structure and the level to which trade costs fall are shown to play a decisive role. Domestic mergers will be encouraged only if the product market is not highly concentrated and trade costs fall below a threshold level. International mergers can be encouraged in any market structure, and are generally more profitable than domestic mergers.
Influence of domain-specific notation to program understanding

Application libraries are the most commonly used implementation approach to solve problems in general-purpose languages. Their competitors are domain-specific languages, which can provide notation close to the problem domain. We carried out an empirical study on comparing domain-specific languages and application libraries regarding program understanding. In this paper, one case study is presented. Over 3000 lines of code were studied and more than 86 pages long questionnaires were answered by end-users, answering questions on learning, perceiving and evolving programs written in domain-specific language as well as general-purpose language using application library. In this paper, we present comparison results on end-users' correctness and consumed time. For domain-specific language and application library same problem domain has been used—a well-known open source graph description language, DOT.
Avoiding moving obstacles: the forbidden velocity map

Robotic obstacle avoidance in cluttered and dense environments is an important issue in robotic navigation. Over the past few years a number of techniques has been proposed to deal with safe navigation among obstacles in unknown scenarios. Unfortunately many of these methods do not consider obstacle velocities, which can rise some serious questions concerning their safety. This paper will deal with a novel approach to moving obstacle avoidance in holonomic robots. It proposes the Forbidden Velocity Map, a generalization of the Dynamic Window concept that considers obstacle and robot shape, velocity and dynamics, resulting in a safe, reactive real-time navigation algorithm that is able to deal with navigation in unpredictable and cluttered scenarios.
From exaflop to exaflow

Exascale computing is facing a gap between the ever increasing demand for application performance and the underlying chip technology that does no longer deliver the expected exponential increases in CPU performance. The industry is now progressively moving towards dedicated accelerators to deliver high performance and better energy efficiency. However, the question of programmability still remains. To address this challenge we propose a dedicated high-level accelerator programming and execution model where performance and efficiency are primary targets. Our model splits the computation into a conventional CPU-oriented part and a highly efficient fully programmable data flow part. We present a number of systematic transformations and optimisations targeting Maxeler dataflow systems that typically yield one to two orders of magnitude improvements in terms of both performance and energy efficiency. These significant gains are enabled by addressing fundamental algorithmic properties and on-demand numerical requirements. This approach is demonstrated by a case study from computational finance.
Genome-Wide Mapping of Chromatin State of Mouse Forelimbs.

BACKGROUND Cell types are defined at the molecular level during embryogenesis by a process called pattern formation and created by the selective utilization of combinations of sequence specific transcription factors. Developmental programs define the sets of genes that are available to each particular cell type, and real-time biochemical signaling interactions define the extent to which these sets are used at any given time and place. Gene expression is regulated through the integrated action of many cis-regulatory elements, including core promoters, enhancers, silencers, and insulators. The chromatin state in developing body parts provides a code to cellular populations that direct their cell fates. Chromatin profiling has been a method of choice for mapping regulatory sequences in cells that go through developmental transitions. RESULTS We used antibodies against histone H3 lysine 4 trimethylations (H3K4me3) a modification associated with promoters and open/active chromatin, histone H3 lysine 27 trimethylations (H3K27me3) associated with Polycomb-repressed regions and RNA polymerase II (Pol2) associated with transcriptional initiation to identify the chromatin state signature of the mouse forelimb during mid-gestation, at embryonic day 12 (E12). The families of genes marked included those related to transcriptional regulation and embryogenesis. One third of the marked genes were transcriptionally active while only a small fraction were bivalent marked. Sequence specific transcription factors that were activated were involved in cell specification including bone and muscle formation. CONCLUSION Our results demonstrate that embryonic limb cells do not exhibit the plasticity of the ES cells but are rather programmed for a finer tuning for cell lineage specification.
Preserving Cohesive Structures for Tool-Based Modularity Reengineering

The quality of software systems heavily depends on their structure, which affects maintainability and readability. However, the ability of humans to cope with the complexity of large software systems is limited. To support reengineering large software systems, software clustering techniques that maximize module cohesion and minimize inter-modular coupling have been developed. The main drawback of these approaches is that they might pull apart elements that were thoughtfully placed together. This paper describes how strongly connected component analysis, dominance analysis, and intra-modular similarity clustering can be applied to identify and to preserve cohesive structures in order to improve the result of reengineering. The use of the proposed method allows a significant reduction of the number of component movements. As a result, the probability of false component movements is reduced. The proposed approach is illustrated by statistics and examples from 18 open source Java projects.
Understanding variation in primary medical care: a nine-country qualitative study of clinicians’ accounts of the non-clinical factors that shape antibiotic prescribing decisions for lower respiratory tract infection

Objectives There is a wide variation between European countries in antibiotic prescribing for patients in primary care with lower respiratory tract infection (LRTI) that is not explained by case mix and clinical factors alone. Variation in antibiotic prescribing that is not warranted by differences in illness and clinical presentation may increase selection of resistant organisms, contributing to the problem of antibiotic resistance. This study aimed to investigate clinicians’ accounts of non-clinical factors that influence their antibiotic prescribing decision for patients with LRTI, to understand variation and identify opportunities for addressing possible unhelpful variation. Design Multicountry qualitative semistructured interview study, with data subjected to a five-stage analytic framework approach (familiarisation, developing a thematic framework from interview questions and emerging themes, indexing, charting and interpretation), and with interviewers commenting on preliminary analytic themes. Setting Primary care. Participants Eighty primary care clinicians randomly selected from primary care research networks based in nine European cities. Results Clinicians’ accounts identified non-clinical factors imposed by the healthcare system operating within specific regional primary care research networks, including patient access to antibiotics before consulting a doctor (Barcelona and Milan), systems to reduce patient expectations for antibiotics (Southampton and Antwerp) and lack of consistent treatment guidelines (Balatonfüred and Łódź). Secondly, accounts revealed factors related to specific characteristics of clinicians regardless of network (professional ethos, self-belief in decision-making and commitment to shared decision-making). Conclusions Addressing healthcare system factors (eg, limiting patients’ self-management with antibiotics before consulting in primary care, increased public awareness and provision of more consistent guidelines) may assist in reducing unhelpful variation in antibiotic prescribing. Promoting clinicians’ receptivity to change, confidence in decision-making and readiness to invest in explaining prescribing decisions may also be beneficial. As factors were emphasised differently between networks, local flexibility in interventions is likely to maximise effectiveness.
Tools and Frameworks for Big Learning in Scala: Leveraging the Language for High Productivity and Performance

Implementing machine learning algorithms for large data, such as the Web graph and social networks, is challenging. Even though much research has focused on making sequential algorithms more scalable, their running times continue to be prohibitively long. Meanwhile, parallelization remains a formidable challenge for this class of problems, despite frameworks like MapReduce which hide much of the associated complexity. We present three ongoing efforts within our team, previously presented at venues in other fields, which aim to make it easier for machine learning researchers and practitioners alike to quickly implement and experiment with their algorithms in a parallel or distributed setting. Furthermore, we hope to highlight some of the language features unique to the Scala programming language in the treatment of our frameworks, in an effort to show how these features can be used to produce efficient and correct parallel systems more easily than ever before.
Computerised cognitive assessment of concussed Australian Rules footballers

Background—“Paper and pencil” neuropsychological tests play an important role in the management of sports related concussions. They provide objective information on the athlete's cognitive function and thus facilitate decisions on safe return to sport. It has been proposed that computerised cognitive tests have many advantages over such conventional tests, but their role in this domain is yet to be established. Objectives—To measure cognitive impairment after concussion in a case series of concussed Australian Rules footballers, using both computerised and paper and pencil neuropsychological tests. To investigate the role of computerised cognitive tests in the assessment and follow up of sports related concussions. Methods—Baseline measures on the Digit Symbol Substitution Test (DSST), Trail Making Test-Part B (TMT), and a simple reaction time (SRT) test from a computerised cognitive test battery (CogState) were obtained in 240 players. Tests were repeated in players who had sustained a concussive injury. A group of non-injured players were used as matched controls. Results—Six concussions were observed over a period of nine weeks. At the follow up, DSST and TMT scores did not significantly differ from baseline scores in both control and concussed groups. However, analysis of the SRT data showed an increase in response variability and latency after concussion in the injured athletes. This was in contrast with a decrease in response variability and no change in latency on follow up of the control players (p<0.02). Conclusion—Increased variability in response time may be an important cognitive deficit after concussion. This has implications for consistency of an athlete's performance after injury, as well as for tests used in clinical assessment and follow up of head injuries.
Localized Hybrid Data Delivery Scheme using K-hop Clustering Algorithm in Ad Hoc Networks

In mobile ad hoc networks, as nodes move freely, network partitions occur frequently, which significantly degrades the performance of data access. Data replication is a promising approach to improve data availability in mobile ad hoc networks. In this paper, we propose a localized hybrid data delivery scheme that combines the push-based and the pull-based approaches. This scheme is constructed by implementing a localized clustering algorithm that constructs groups, in which each node is at most K hops away from a group leader. This construction permits to bind the query access delay by K hops. The localized clustering algorithm dynamically creates groups to adapt to topology changes. It can also predict when the group will partition. So, it can replicate data items on nodes of the future separate group before the partitioning occurs. The proposed localized scheme helps to increase data availability while improving query delay and update cost
Investigation of intrinsic spine muscle properties to improve musculoskeletal spine modelling

ii Acknowledgements. iv Table of contents. v List of tables. viii List of figures. ix Chapter 1: Introduction. 1 1.1 Passive muscle modelling. 1 1.2 Muscle anatomy. 5 1.2.1 Multifidus. 6 1.2.2 Erector spinae. 7 1.2.3 Latissimus dorsi. 8 1.2.4 Quadratus lumborum. 8 1.2.5 Psoas major. 8 1.2.6 Abdominal muscles. 9 1.3 Dissertation overview. 10 1.3.1 Part A: Experiment 1. 10 1.3.2 Part A: Experiment 2. 11 1.3.3 Part A: Experiment 3. 11 1.3.4 Part B: Musculoskeletal Model. 11 1.4 Statement of ethics. 12 Chapter 2: Part AExperiment I. 13 Disruption of erector spinae aponeurosis and facet compression in a rat model does not alter the passive mechanics of spine muscles after four weeks of recovery 2.1 Chapter overview. 13 2.2 Introduction. 13 2.3 Methods. 16 2.3.1 Surgical groups. 16 2.3.2 Tissue samples. 18 2.3.3 Mechanical testing. 19 2.3.4 Data analysis. 20 2.3.5 Behavioural testing. 21 2.3.6 Statistical analysis. 22 2.4 Results. 22 2.4.1 Elastic moduli. 22 2.4.2 Slack length. 24 2.4.3 Passive stress-length relationship. 26 2.4.4 Behavioural testing. 27 2.4.5 Histology. 27 2.5 Discussion. 28 vi 2.5.1 Effect of Incision surgery. 29 2.5. 2 Effect of Compression surgery . 30 2.5. 3 Future directions. 32 2.6 Bridge summary. 34 Chapter 3: Part AExperiment II. 39 Sarcomere length organization as a design for cooperative function amongst all lumbar spine muscles 3.1 Chapter overview. 39 3.2 Introduction. 40 3.3 Materials and methods. 43 3.3.1 Cadaveric donors . 43 3.3.2 Sarcomere length measurement. 44 3.3.3 Modelled operating ranges. 45 3.3.4 Statistical analysis. 48 3.4 Results. 48 3.4.1 Neutral spine cadaveric sarcomere lengths. 48 3.4.2 Modelled sarcomere lengths. 50 3.4.2.1 Flexion and extension. 50 3.4.2.2 Lateral bending. 54 3.4.2.3 Axial rotation. 55 3.5 Discussion. 55 3.6 Bridge summary. 61 Chapter 4: Part AExperiment III. ,,. 63 Decreasing the required lumbar extensor moment induces earlier onset of flexion relaxation 4.1 Chapter overview. 63 4.2 Introduction. 64 4.3 Methods. 67 4.3.1 Participant characteristics. 67 4.3.2 Experimental set-up. 67 4.3.3 Protocol. 70 4.3.4 Data processing. 71 4.3.5 Statistical analysis. 72 4.4 Results. 73 4.4.1 Lumbar erector spinae. 73 4.4.2 Abdominal muscles. 75 4.4.3 Additional muscles. 78 4.5 Discussion. 80 4.6 Bridge summary. 86 Chapter 5: Part BMusculoskeletal model. 88 5.1 Chapter overview. 88
Indigenous Innovation In China: Implications For Sustainable Growth

This paper aims to examine indigenous innovation and draw implications for sustainable economic growth in China. It investigates China’s capacity and achievements in indigenous innovation at both the macro and micro levels. China’s indigenous innovation is also compared to that in other major economies in the world. It is found that China’s innovation development is well ahead of other economies at the similar stage of development but there is a gap between China and the world’s leading innovative economies. Both aggregate and disaggregate evidence shows that China is catching up rapidly with the world’s innovation leaders. If current growth momentum is maintained, China is well positioned to become one of the most innovative economies in the world in the coming decade. There are however some serious issues to be resolved before China’s innovation potential could be realized.
Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality

A new algorithm for classifying ECG recording quality based on the detection of commonly observed ECG contaminants which often render the ECG unusable for diagnostic purposes was evaluated. Contaminants (baseline drift, flat line, QRS-artefact, spurious spikes, amplitude stepwise changes, noise) were detected on individual leads from joint time-frequency analysis and QRS amplitude. Classification was based on cascaded single-condition decision rules (SCDR) that tested levels of contaminants against classification thresholds. A supervised learning classifier (SLC) was implemented for comparison. The SCDR and SLC algorithms were trained on an annotated database (Set A, PhysioNet Challenge 2011) of ‘acceptable’ versus ‘unacceptable’ quality recordings using the ‘leave M out’ approach with repeated random partitioning and cross-validation. Two training approaches were considered: (i) balanced, in which training records had equal numbers of ‘acceptable’ and ‘unacceptable’ recordings, (ii) unbalanced, in which the ratio of ‘acceptable’ to ‘unacceptable’ recordings from Set A was preserved. For each training approach, thresholds were calculated, and classification accuracy of the algorithm compared to other rule based algorithms and the SLC using a database for which classifications were unknown (Set B PhysioNet Challenge 2011). The SCDR algorithm achieved the highest accuracy (91.40%) compared to the SLC (90.40%) in spite of its simple logic. It also offers the advantage that it facilitates reporting of meaningful causes of poor signal quality to users.