Miracle-dz/UniFAR-FTUs huggingface dataset:
Social networks and travel: Some hypotheses

Transport planning has clear expectations of how rising income or sinking generalised costs of travel will influence travel behaviour. There are no such expectations for the observable changes in the spatial structures of the social networks. This note tries to formulate such hypotheses for the first time. Appendices round the note off discussing possibilities to measure and model the spatial structure of social networks.
Path-Diversity-Aware Adaptive Routing in Network-on-Chip Systems

The partially adaptive routing plays an important role in the performance of Network-on-Chip (NoC). It uses information of the network to select a better path to deliver a packet. However, it may have imbalanced path diversity in different directions, which makes their tolerances of traffic load differ a lot from each other. This characteristic would cause problems in traffic balancing but give us extra information of the network. To achieve load balancing, in this paper, we present an adaptive routing scenario with Path-Diversity-Aware (PDA) and Augmented-PDA (A-PDA) selections, which use the information of path diversity. Moreover, we derive a formula to quantify the characteristic of path diversity. Experiments with different scenarios were conducted. The simulation results show that our proposed selections have an advantage over other selection functions in saturation throughput, with up to 36.84%, and have better scalability in large scale NoC. In addition, a low-cost router architecture is proposed to implement PDA and A-PDA and the synthesized results are also shown in this paper.
Interaction theory [new paradigm] for solving the assignment problem

The Assignment Problem is very interesting to be researched because it is simple but wide in the applications. Hungarian Method, developed by Harold Kuhn in 1955, is one of the most popular algorithms to solve the assignment problem since it is easy and practical. However, its easiness is restricting the form of iteration process which is quite complicated. A research about the effective and efficient new algorithm is needed. In this paper, we introduce a new concept for solving the assignment problem. The Interaction Theory has been developed since 1966. We developed the coefficient interaction formula and succeeded to be implemented to solving the hard problems in the exact way and giving more alternative optimal solutions with the same value. The algorithm is introduced in this paper along with several applications; including problem with more than one solution, unbalanced problem, and problem with incomplete data. The Interaction Theory gives a new hope as a new paradigm. The algorithm is simple yet effective and efficient to solve all kinds of assignment problems such as unbalanced assignment problem, and minimize and maximize objective. Moreover, the algorithm sometimes produces more than one optimal solution.
JIT compilation policy for modern machines

Dynamic or Just-in-Time (JIT) compilation is crucial to achieve acceptable performance for applications (written in managed languages, such as Java and C#) distributed as intermediate language binary codes for a virtual machine (VM) architecture. Since it occurs at runtime, JIT compilation needs to carefully tune its compilation policy to make effective decisions regarding 'if' and 'when' to compile different program regions to achieve the best overall program performance. Past research has extensively tuned JIT compilation policies, but mainly for VMs with a single compiler thread and for execution on single-processor machines. This work is driven by the need to explore the most effective JIT compilation strategies in their modern operational environment, where (a) processors have evolved from single to multi/many cores, and (b) VMs provide support for multiple concurrent compiler threads. Our results confirm that changing 'if' and 'when' methods are compiled have significant performance impacts. We construct several novel configurations in the HotSpot JVM to facilitate this study. The new configurations are necessitated by modern Java benchmarks that impede traditional static whole-program discovery, analysis and annotation, and are required for simulating future many-core hardware that is not yet widely available. We study the effects on performance of increasing compiler aggressiveness for VMs with multiple compiler threads running on existing single/multi-core and future many-core machines. Our results indicate that although more aggressive JIT compilation policies show no benefits on single-core machines, these can often improve program performance for multi/many-core machines. However, accurately prioritizing JIT method compilations is crucial to realize such benefits.
GiSS: Combining Gibbs Sampling and SampleSearch for Inference in Mixed Probabilistic and Deterministic Graphical Models

Mixed probabilistic and deterministic graphical models are ubiquitous in real-world applications. Unfortunately, Gibbs sampling, a popular MCMC technique, does not converge to the correct answers in presence of determinism and therefore cannot be used for inference in such models. In this paper, we propose to remedy this problem by combining Gibbs sampling with SampleSearch, an advanced importance sampling technique which leverages complete SAT/CSP solvers to generate high quality samples from hard deterministic spaces. We call the resulting algorithm, GiSS. Unlike Gibbs sampling which yields unweighted samples, GiSS yields weighted samples. Computing these weights exactly can be computationally expensive and therefore we propose several approximations. We show that our approximate weighting schemes yield consistent estimates and demonstrate experimentally that GiSS is competitive in terms of accuracy with state-of-the-art algorithms such as SampleSearch, MC-SAT and Belief propagation.
Neurophysiological Correlates of Featural and Spacing Processing for Face and Non-face Stimuli

The peculiar ability of humans to recognize hundreds of faces at a glance has been attributed to face-specific perceptual mechanisms known as holistic processing. Holistic processing includes the ability to discriminate individual facial features (i.e., featural processing) and their spatial relationships (i.e., spacing processing). Here, we aimed to characterize the spatio-temporal dynamics of featural- and spacing-processing of faces and objects. Nineteen healthy volunteers completed a newly created perceptual discrimination task for faces and objects (i.e., the “University of East London Face Task”) while their brain activity was recorded with a high-density (128 electrodes) electroencephalogram. Our results showed that early event related potentials at around 100 ms post-stimulus onset (i.e., P100) are sensitive to both facial features and spacing between the features. Spacing and features discriminability for objects occurred at circa 200 ms post-stimulus onset (P200). These findings indicate the existence of neurophysiological correlates of spacing vs. features processing in both face and objects, and demonstrate faster brain processing for faces.
Atomistic Study of Lipid Membranes Containing Chloroform: Looking for a Lipid-Mediated Mechanism of Anesthesia

The molecular mechanism of general anesthesia is still a controversial issue. Direct effect by linking of anesthetics to proteins and indirect action on the lipid membrane properties are the two hypotheses in conflict. Atomistic simulations of different lipid membranes subjected to the effect of small volatile organohalogen compounds are used to explore plausible lipid-mediated mechanisms. Simulations of homogeneous membranes reveal that electrostatic potential and lateral pressure transversal profiles are affected differently by chloroform (anesthetic) and carbon tetrachloride (non-anesthetic). Simulations of structured membranes that combine ordered and disordered regions show that chloroform molecules accumulate preferentially in highly disordered lipid domains, suggesting that the combination of both lateral and transversal partitioning of chloroform in the cell membrane could be responsible of its anesthetic action.
Improving the Reliability of Optimised Link State Routing in a Smart Grid Neighbour Area Network based Wireless Mesh Network Using Multiple Metrics

Reliable communication is the backbone of advanced metering infrastructure (AMI). Within the AMI, the neighbourhood area network (NAN) transports a multitude of trafﬁc, each with unique requirements. In order to deliver an acceptable level of reliability and latency, the underlying network, such as the wireless mesh network(WMN), must provide or guarantee the quality-of-service (QoS) level required by the respective application trafﬁc. Existing WMN routing protocols, such as optimised link state routing (OLSR), typically utilise a single metric and do not consider the requirements of individual trafﬁc; hence, packets are delivered on a best-effort basis. This paper presents a QoS-aware WMN routing technique that employs multiple metrics in OLSR optimal path selection for AMI applications. The problems arising from this approach are non deterministic polynomial time (NP)-complete in nature, which were solved through the combined use of the analytical hierarchy process (AHP) algorithm and pruning techniques. For smart meters transmitting Internet Protocol (IP) packets of varying sizes at different intervals, the proposed technique considers the constraints of NAN and the applications’ trafﬁc characteristics. The technique was developed by combining multiple OLSR path selection metrics with the AHP algorithminns-2. Compared with the conventional link metric in OLSR, the results show improvements of about 23% and 45% in latency and Packet Delivery Ratio (PDR), respectively, in a 25-node grid NAN.
Anomaly Detection in Unmanned Vehicles: Thesis

Autonomy implies robustness. The use of unmanned (autonomous) vehicles is appealing for tasks which are dangerous or dull. However, increased reliance on autonomous robots increases reliance on their robustness. Even with validated software, physical faults can cause the controlling software to perceive the environment incorrectly, and thus to make decisions that lead to task failure. Anomaly detection can also be applied to medical monitors; alarms will be raised whenever the values of the measurements of the patient are anomalous with respect to the patient’s current condition. This anomaly detection is particularly useful for medical devices that monitor patients in recovery after a surgery, where the assigned nurses or physicians are not near to watch the monitor. Model-based diagnosis and fault-detection systems have been proposed to recognize failures. However, these rely on the capabilities of the underlying model, which necessarily abstracts away from the physical reality of the robot. We present two novel, model-free, domain independent approaches for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Both approaches use the familiar Mahalanobis Distance for the online anomaly detection. The first approach uses an offline training process. With this approach, we show the importance of a training process, which enables the Mahalanobis Distance to detect anomalies successfully. The second approach uses an online training process, in a way that is light-weight, and is able to take into account a large number of monitored sensors and internal measurements. These properties make the approach a “plug & play” anomaly detection mechanism for different robotic platforms. We demonstrate a specialization of the Mahalanobis Distance for robot use, and also show how it can be used even with very large dimensions, by online selection of correlated measurements for its use. We empirically evaluate these contributions in different domains: commercial Unmanned Aerial Vehicles (UAVs), a vacuum-cleaning robot, a high-fidelity flight simulator, and an electrical power system. We find that the online Mahalanobis distance technique, presented here, is superior to previous methods.
Detection of false feature correspondences in feature based object detection systems

In this paper we present a method for the detection of wrong feature correspondences in a local feature based object detection system. Common visual objects in different images share not only similar local features but also a similar spatial layout of their features. We will utilize this fact in order to distinguish between correct and wrong feature correspondences. The spatial feature layout will be modeled through a Delaunay triangulation. This triangulation is used to find clusters of feature correspondences that follow the same affine transformation. The decision whether a correspondence is correct or wrong can than be made based on this clustering. Our method is independent from the number of common objects in the images and produces reliable results even in difficult scenarios. It can also be used if the number of wrong correspondences is much higher than the number of correct correspondences. Experiments on real and synthetically generated images demonstrate the good performance of our approach.
From exploration to imitation: using learnt internal models to imitate others

We present an architecture that enables asocial and social learning mechanisms to be combined in a unified framework on a robot. The robot learns two kinds of internal models by interacting with the environment with no a priori knowledge of its own motor system: internal object models are learnt about how its motor system and other objects appear in its sensor data; internal control models are learnt by babbling and represent how the robot controls objects. These asocially-learnt models of the robot's motor system are used to understand the actions of a human demonstrator on objects that they can both interact with. Knowledge acquired through self-exploration is therefore used as a bootstrapping mechanism to understand others and benefit from their knowledge.
Human-Technology Relationships in the Digital Age: The Collapse of Metaphor in Biohacking

Citation for published version (APA): Petersén, M. (2018). Human-Technology Relationships in the Digital Age: The Collapse of Metaphor in Biohacking. In J. Aagard, J. Kyrre Berg Friis, J. Sorenson, O. Tafdrup, & C. Hasse (Eds.), Postphenomenological Methodologies: New Ways in Mediating Techno-Human Relationships (pp. 65-81). (Postphenomenology and the Philosophy of Technology). London: Lexington Books.
Effect of L-Carnitine on Skeletal Muscle Lipids and Oxidative Stress in Rats Fed High-Fructose Diet

There is evidence that high-fructose diet induces insulin resistance, alterations in lipid metabolism, and oxidative stress in rat tissues. The purpose of this study was to evaluate the effect of L-carnitine (CAR) on lipid accumulation and peroxidative damage in skeletal muscle of rats fed high-fructose diet. Fructose-fed animals (60 g/100 g diet) displayed decreased glucose/insulin (G/I) ratio and insulin sensitivity index (ISI0,120) indicating the development of insulin resistance. Rats showed alterations in the levels of triglycerides, free fatty acids, cholesterol, and phospholipids in skeletal muscle. The condition was associated with oxidative stress as evidenced by the accumulation of lipid peroxidation products, protein carbonyls, and aldehydes along with depletion of both enzymic and nonenzymic antioxidants. Simultaneous intraperitoneal administration of CAR (300 mg/kg/day) to fructose-fed rats alleviated the effects of fructose. These rats showed near-normal levels of the parameters studied. The effects of CAR in this model suggest that CAR supplementation may have some benefits in patients suffering from insulin resistance.
Cheating Cheating Detectors

In this paper we present a new cheating technique that is successful at defeating cheating detectors and could become popular with students. The idea is to use obfuscating code transformations (such as those found in the SANDMARK tool) to apply a sequence of minor code transformations to a copied programming assignment. This purpose is to produce a copy that will defeat detection. We show that this technique is successful in defeating common plagiarism detectors such as Moss. This paper is offered as a cautionary tale to the Computer Science teaching community. With the advent of powerful code transformation tools it will become necessary to develop correspondingly more powerful cheating detectors, or to revert back to manually testing for plagiarism.
vNFS

Modern systems use networks extensively, accessing both services and storage across local and remote networks. Latency is a key performance challenge, and packing multiple small operations into fewer large ones is an effective way to amortize that cost, especially after years of significant improvement in bandwidth but not latency. To this end, the NFSv4 protocol supports a compounding feature to combine multiple operations. Yet compounding has been underused since its conception because the synchronous POSIX file-system API issues only one (small) request at a time. We propose vNFS, an NFSv4.1-compliant client that exposes a vectorized high-level API and leverages NFS compound procedures to maximize performance. We designed and implemented vNFS as a user-space RPC library that supports an assortment of bulk operations on multiple files and directories. We found it easy to modify several UNIX utilities, an HTTP/2 server, and Filebench to use vNFS. We evaluated vNFS under a wide range of workloads and network latency conditions, showing that vNFS improves performance even for low-latency networks. On high-latency networks, vNFS can improve performance by as much as two orders of magnitude.
On Mehrotra-Type Predictor-Corrector Algorithms

In this paper we discuss the polynomiality of a feasible version of Mehrotra's predictor-corrector algorithm whose variants have been widely used in several interior point method (IPM)-based optimization packages. A numerical example is given that shows that the adaptive choice of centering parameter and correction terms in this algorithm may lead to small steps being taken in order to keep the iterates in a large neighborhood of the central path, which is important for proving polynomial complexity properties of this method. Motivated by this example, we introduce a safeguard in Mehrotra's algorithm that keeps the iterates in the prescribed neighborhood and allows us to obtain a positive lower bound on the step size. This safeguard strategy is also used when the affine scaling direction performs poorly. We prove that the safeguarded algorithm will terminate after at most ${\cal{O}}(n^2{\rm log} (x^0)^Ts^0/\epsilon)$ iterations. By modestly modifying the corrector direction, we reduce the iteration complexity to ${\cal{O}}(n {\rm log} (x^0)^Ts^0/\epsilon). $ To ensure fast asymptotic convergence of the algorithm, we changed Mehrotra's updating scheme of the centering parameter slightly while keeping the safeguard. The new algorithms have the same order of iteration complexity as the safeguarded algorithms but enjoy superlinear convergence as well. Numerical results using the McIPM and LIPSOL software packages are reported.
Mental Rotation Ability of Children with Spina Bifida: What Influence Does Manual Rotation Training Have?

We investigated the mental rotation ability of children with spina bifida, a malformation of the spinal cord due to a neural tube defect, and how it is influenced by a manual rotation training. In comparison to a healthy control group these children showed longer reaction times and a higher number of errors in a computer-based mental rotation test. Furthermore, a manual rotation training was applied. The spina bifida group benefited considerably from the manual rotation training. The training effect was not limited to stimuli learned in the training. While the children with spina bifida showed a lower speed of mental rotation than their healthy peers in the mental rotation pretest, the two groups did not differ in their mental rotation speed in the posttest.
An approach to the de-identification of faces in different poses

Publicly captured surveillance videos and images serve as a rich source of biometric identifiers. Of these, the face is the one most frequently used for the identification of people. In order to protect a person's identity, whenever it is not absolutely required, the face should be de-identified. One of the problems that any naturalness-preserving face de-identification method should address is the variation in the face pose. In this paper we build several active appearance face models on the database of face images and then use them to detect face pose as well as a gallery of faces that is then used for the de-identification. The results can be seen as a first step to the face de-identification in video.
Combinatorial interference of toll-like receptor 2 and 4 synergistically stabilizes atherosclerotic plaque in apolipoprotein E-knockout mice

To test the hypothesis that combinatorial interference of toll‐like receptor 2 (TLR2) and TLR4 is superior to isolated interference of TLR2 or TLR4 in stabilizing atherosclerotic plaques, lentiviruses carrying small interfering RNA of TLR2 or TLR4 were constructed and proved efficacious for knocking down mRNA and protein expression of TLR2 or TLR4 significantly in vitro. One hundred and fifty apolipoprotein E−/− mice fed a high‐fat diet were divided into the control, mock, TLR2i, TLR4i and TLR2 + 4i subgroups and a constrictive collar was placed around carotid artery of these mice to induce plaque formation. TLR2i and TLR4i viral suspension was transfected into carotid plaques, respectively, in TLR2i and TLR4i subgroups, or in combination in TLR2 + 4i subgroup. Four weeks after lentivirus transfection, mRNA and protein expression of TLR2 or TLR4 was attenuated markedly in carotid plaques, leading to reduced local inflammatory cytokine expression and plaque content of lipid and macrophages, increased plaque content of collagen and lowered plaque vulnerability index. Factorial ANOVA analysis revealed that there was a synergistic effect between TLR4i and TLR2i in stabilizing plaques. In conclusion, combinatorial interference of TLR2 and TLR4 reduces local inflammation and stabilizes plaques more effectively than interference of TLR2 or TLR4 alone.