Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [18F]-Florbetaben PET Quantitation in Alzheimer's Model Mice

Preclinical PET studies of β-amyloid (Aβ) accumulation are of growing importance, but comparisons between research sites require standardized and optimized methods for quantitation. Therefore, we aimed to evaluate systematically the (1) impact of an automated algorithm for spatial brain normalization, and (2) intensity scaling methods of different reference regions for Aβ-PET in a large dataset of transgenic mice. PS2APP mice in a 6 week longitudinal setting (N = 37) and another set of PS2APP mice at a histologically assessed narrow range of Aβ burden (N = 40) were investigated by [18F]-florbetaben PET. Manual spatial normalization by three readers at different training levels was performed prior to application of an automated brain spatial normalization and inter-reader agreement was assessed by Fleiss Kappa (κ). For this method the impact of templates at different pathology stages was investigated. Four different reference regions on brain uptake normalization were used to calculate frontal cortical standardized uptake value ratios (SUVRCTX∕REF), relative to raw SUVCTX. Results were compared on the basis of longitudinal stability (Cohen's d), and in reference to gold standard histopathological quantitation (Pearson's R). Application of an automated brain spatial normalization resulted in nearly perfect agreement (all κ≥0.99) between different readers, with constant or improved correlation with histology. Templates based on inappropriate pathology stage resulted in up to 2.9% systematic bias for SUVRCTX∕REF. All SUVRCTX∕REF methods performed better than SUVCTX both with regard to longitudinal stability (d≥1.21 vs. d = 0.23) and histological gold standard agreement (R≥0.66 vs. R≥0.31). Voxel-wise analysis suggested a physiologically implausible longitudinal decrease by global mean scaling. The hindbrain white matter reference (Rmean = 0.75) was slightly superior to the brainstem (Rmean = 0.74) and the cerebellum (Rmean = 0.73). Automated brain normalization with reference region templates presents an excellent method to avoid the inter-reader variability in preclinical Aβ-PET scans. Intracerebral reference regions lacking Aβ pathology serve for precise longitudinal in vivo quantification of [18F]-florbetaben PET. Hindbrain white matter reference performed best when considering the composite of quality criteria.
An area efficient on-chip hybrid voltage regulator

Experimental results of an active filter based on-chip hybrid voltage converter are described in this paper. The area of the voltage converter is significantly less than the area of a conventional passive filter based DC-DC voltage converter or a low-dropout (LDO) regulator. Hence, the proposed circuit is appropriate for point-of-load voltage regulation for the noise sensitive portions of an integrated circuit. The performance of the circuit has been verified with Cadence Spectre simulations and fabricated with a commercial 110 nm CMOS technology. The area of the voltage regulator is 0.015 mm2 and delivers up to 80 mA of output current. The transient response with no output capacitor ranges from 72 ns to 192 ns. The advantages and disadvantages of an active filter based, conventional switching, linear, and switched capacitor voltage converters are compared. The proposed circuit provides a means for distributing multiple local power supplies across an integrated circuit while maintaining high current efficiency and fast response time within a small area.
Influential user detection on Twitter: Analyzing effect of focus rate

Social media usage has increased marginally in the last decade and it is still continuing to grow. Companies, data scientists, and researchers are trying to infer meaningful information from this vast amount of data. One of the most important target applications is to find influential people in these networks. This information can serve many purposes such as; user or content recommendation, viral marketing, and user modeling. Social media is divided into subcategories like where one can share photos (i.e. Instagram, Flickr), video or music (i.e. Youtube, Last.fm), restaurant suggestions like Foursquare, or text like Twitter. Twitter is more of an idea and news sharing media than other types of social media and it has a huge amount of public profiles. These features of Twitter make it a more interesting and valuable media to research on. In this paper, we are addressing to identify topical authorities/influential users in Twitter. We provide a novel representation of users' topical interests called focus rate. We incorporate nodal features into network features and introduce a modified version of Pagerank algorithm which efficiently analyzes topical influence of users. Experimental results show that focus rate of users on specific topics increase their influence scores and lead to higher information diffusion. We use also distributed computing environment which enables to work with large data sets. We demonstrate our results on Turkish Twitter messages. For the best of our knowledge, this is the first influence analysis on Twitter that is conducted for Turkish language.
Is Chimpanzee (Pan troglodytes schweinfurthii) Low Population Density Linked with Low Levels of Aggression

quality (Jones et al. 2010), or even general competence at foraging could account for our results. None of these alternative hypotheses can be tested with our data in this preliminary study. However, rank is a constructed attribute , not a behavioral variable. How dominance 'acts', whether generally or specifically, can be elucidated only through focussed studies such as this one. Future studies should make use of comprehensive databases that include such variables (e.g. Strier et al. 2010); a more comprehensive study of termite fishing and rank over lifetimes might resolve the correlation/causation quandary. RW 2007. Core area quality is associated with variance in reproductive success among female chimpanzees at Kibale National Park. Foraging strategies as a function of season and rank among wild female chimpanzees (Pan troglodytes schweinfurthii). Behav Ecol 17: 1020–1028. Murray CM, Mane SV, Pusey AE 2007. Dominance rank influences female space use in wild chimpanzees, Pan troglodytes: towards an ideal despotic distribution. History Database: a unique shared ecological data resource. The meat-scrap hypothesis: small quantities of meat may promote cooperative hunting in wild chimpanzees (Pan troglodytes). Female competition and male territorial behaviour influence female chimpanzees' ranging patterns. Anim Behav 63:347–360.
A Formation Control for Dynamical Agents in Digital Network

This paper investigates a formation control for dynamical agents in digital communication network. Each agent in the network is a continuous-time dynamics and the communication network is defined by a undirect graph. Under a linear control protocol the network of dynamical agents is described by a hybrid decentralized control system. We give the condition under which the agents in the network implement the consensus stability. Different from the agents under continuous-time communication network the agents in digital communication network may have some different asymptotical formations, i.e. aggregation, periodic oscillation and divergence, respectively. Numerical simulations are provided to demonstrate the effectiveness of our theoretical results.
Multi-agent Reinforcement Learning for Service Composition

This paper investigates the multi-agent cooperation problems in Web services domain. For Pareto-optimal Nash equilibrium, reinforcement learning algorithms are used to solve the coordination problem in cooperative environments. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). To avoid local optimal solution, we use an improved update function. The experimental results show that proposed reinforcement learning algorithm outperforms other learning methods.
Ontology-driven coordination for supply chain system

This paper proposes an ontology-driven coordination framework for information interoperation in supply chain system (SCS). To achieve semantic interoperability in heterogeneous enterprise information systems, ontologies have been advised to use. Mapping among multiple ontologies is necessary for an across-enterprises supply chain. In some complex supply chain, it highly demands semantic interoperability to bridge heterogeneous enterprise applications at various domain into an integrative system. In this paper, we elaborate application ontologies and their mapping logic in SCS, and discuss how to use object-oriented technologies to define primitive ontology and describe ontology coordination logic. We also discuss how to design and implement the coordination framework to solving the semantic interoperability for supply chain information system.
Frequency-aware rate adaptation and MAC protocols

There has been burgeoning interest in wireless technologies that can use wider frequency spectrum. Technology advances, such as 802.11n and ultra-wideband (UWB), are pushing toward wider frequency bands. The analog-to-digital TV transition has made 100-250 MHz of digital whitespace bandwidth available for unlicensed access. Also, recent work on WiFi networks has advocated discarding the notion of channelization and allowing all nodes to access the wide 802.11 spectrum in order to improve load balancing. This shift towards wider bands presents an opportunity to exploit frequency diversity. Specifically, frequencies that are far from each other in the spectrum have significantly different SNRs, and good frequencies differ across sender-receiver pairs. This paper presents FARA, a combined frequency-aware rate adaptation and MAC protocol. FARA makes three departures from conventional wireless network design: First, it presents a scheme to robustly compute per-frequency SNRs using normal data transmissions. Second, instead of using one bit rate per link, it enables a sender to adapt the bitrate independently across frequencies based on these per-frequency SNRs. Third, in contrast to traditional frequency-oblivious MAC protocols, it introduces a MAC protocol that allocates to a sender-receiver pair the frequencies that work best for that pair. We have implemented FARA in FPGA on a wideband 802.11-compatible radio platform. Our experiments reveal that FARA provides a 3.1x throughput improvement in comparison to frequency-oblivious systems that occupy the same spectrum.
The Ontological Argument in PVS

The Ontological Argument, an 11th Century proof of the existence of God, is a good candidate for Fun With Formal Methods as nearly everyone finds the topic interesting. We formalize the Argument in PVS and verify its correctness. The formalization raises delicate questions in formal logic and provides an opportunity to show how these are handled, soundly and efficiently, by the predicatively-subtyped higher-order logic of PVS and its mechanized support. The simplicity of the Argument, coupled to its bold conclusion, raise interesting issues on the interpretation and application of formal methods in the real world.
Antitumor Efficacy of Intermittent Treatment Schedules with the Rapamycin Derivative RAD001 Correlates with Prolonged Inactivation of Ribosomal Protein S6 Kinase 1 in Peripheral Blood Mononuclear Cells

The orally bioavailable rapamycin derivative RAD001 (everolimus) targets the mammalian target of rapamycin pathway and possesses potent immunosuppressive and anticancer activities. Here, the antitumor activity of RAD001 was evaluated in the CA20948 syngeneic rat pancreatic tumor model. RAD001 demonstrated dose-dependent antitumor activity with daily and weekly administration schedules; statistically significant antitumor effects were observed with 2.5 and 0.5 mg/kg RAD001 administered daily [treated tumor versus control tumor size (T/C), 23% and 23–30%, respectively], with 3–5 mg/kg RAD001 administered once weekly (T/C, 14–36%), or with 5 mg/kg RAD001 administered twice weekly (T/C, 36%). These schedules were well tolerated and exhibited antitumor potency similar to that of the cytotoxic agent 5-fluorouracil (T/C, 23%). Moreover, the efficacy of intermittent treatment schedules suggests a therapeutic window allowing differentiation of antitumor activity from the immunosuppressive properties of this agent. Detailed biochemical profiling of mammalian target of rapamycin signaling in tumors, skin, and peripheral blood mononuclear cells (PBMCs), after a single administration of 5 mg/kg RAD001, indicated that RAD001 treatment blocked phosphorylation of the translational repressor eukaryotic initiation factor 4E-binding protein 1 and inactivated the translational activator ribosomal protein S6 kinase 1 (S6K1). The efficacy of intermittent treatment schedules was associated with prolonged inactivation of S6K1 in tumors and surrogate tissues (≥72 h). Furthermore, detailed analysis of the dose dependency of weekly treatment schedules demonstrated a correlation between antitumor efficacy and prolonged effects (≥7 days) on PBMC-derived S6K1 activity. Analysis of human PBMCs revealed that S6K1 also underwent a concentration-dependent inactivation after RAD001 treatment ex vivo (>95% inactivation with 20 nm RAD001). In contrast, human PBMC-derived eukaryotic initiation factor 4E-binding protein 1 was present predominantly in the hypophosphorylated form and was unaffected by RAD001 treatment. Taken together, these results demonstrate a correlation between the antitumor efficacy of intermittent RAD001 treatment schedules and prolonged S6K1 inactivation in PBMCs and suggest that long-term monitoring of PBMC-derived S6K1 activity levels could be used for assessing RAD001 treatment schedules in cancer patients.
Binding, Transcytosis and Biodistribution of Anti-PECAM-1 Iron Oxide Nanoparticles for Brain-Targeted Delivery

Objective Characterize the flux of platelet-endothelial cell adhesion molecule (PECAM-1) antibody-coated superparamagnetic iron oxide nanoparticles (IONPs) across the blood-brain barrier (BBB) and its biodistribution in vitro and in vivo. Methods Anti-PECAM-1 IONPs and IgG IONPs were prepared and characterized in house. The binding affinity of these nanoparticles was investigated using human cortical microvascular endothelial cells (hCMEC/D3). Flux assays were performed using a hCMEC/D3 BBB model. To test their immunospecificity index and biodistribution, nanoparticles were given to Sprague Dawley rats by intra-carotid infusion. The capillary depletion method was used to elucidate their distribution between the BBB and brain parenchyma. Results Anti-PECAM-1 IONPs were ∼130 nm. The extent of nanoparticle antibody surface coverage was 63.6±8.4%. Only 6.39±1.22% of labeled antibody dissociated from IONPs in heparin-treated whole blood over 4 h. The binding affinity of PECAM-1 antibody (KD) was 32 nM with a maximal binding (Bmax) of 17×105 antibody molecules/cell. Anti-PECAM-1 IONP flux across a hCMEC/D3 monolayer was significantly higher than IgG IONP's with 31% of anti-PECAM-1 IONPs in the receiving chamber after 6 h. Anti-PECAM-1 IONPs showed higher concentrations in lung and brain, but not liver or spleen, than IgG IONPs after infusion. The capillary depletion method showed that 17±12% of the anti-PECAM-1 IONPs crossed the BBB into the brain ten minutes after infusion. Conclusions PECAM-1 antibody coating significantly increased IONP flux across the hCMEC/D3 monolayer. In vivo results showed that the PECAM-1 antibody enhanced BBB association and brain parenchymal accumulation of IONPs compared to IgG. This research demonstrates the benefit of anti-PECAM-1 IONPs for association and flux across the BBB into the brain in relation to its biodistribution in peripheral organs. The results provide insight into potential application and toxicity concerns of anti-PECAM-1 IONPs in the central nervous system.
Static Task Scheduling Using Genetic Algorithm and Reinforcement Learning

Task scheduling in a multiprocessor system is defined as assigning a set of tasks to a set of processors. The goal is to minimize the execution time while meeting a set of constraints. A wide variety set of deterministic and heuristic methods are proposed to solve the problem. The main problem is that the proposed methods cannot deal with big search spaces and cannot guarantee to find the optimal solution. In this research a novel approach based on reinforcement learning and genetic algorithm is proposed. Being divided using genetic algorithm, the smaller problems can be solved with reinforcement learner scheduler. The result of the method is a set of task processor pairs. Simulation results in standard problem set show that the method outperforms some studied GA based scheduling methods
Adaptive Video Motion Estimation Algorithm via Estimation of Motion Length Distribution and Bayesian Classification

Real videos contain mixture of motions with slow and fast contents. No fixed fast block matching algorithm can efficiently remove temporal redundancy of video sequences with wide motion contents. In this paper, an adaptive fast block matching algorithm, called classification based adaptive search (CBAS) has been proposed. A Bayes classifier is applied to classify the motions into slow and fast categories. Accordingly, appropriate search strategy is applied for each class. The algorithm switches between different search patterns according to the content of motions within video frames. Experimental results show the proposed technique outperforms conventional standalone fast block matching methods in terms of both peak signal to noise ratio (PSNR) and computational complexity
Social networks analysis based on topic modeling

Understanding the discussed content in social networks is an uneasy problem but brings a lot of advantages for different fields, such as marketing, education, social trends, security. To build up the system for supporting products marketing in social networks, we develop models of content-based social networks analysis in order to find out the discussed topics. The system consists of steps, such as extracting messages, discovering and automatically labeling the discussed topics, in which we pay attention to time factor. Experimented with the Enron corpus containing 11,945 e-mails discussed by 147 users and estimated 50 topics, the system has found out many useful topics and opened new research and application directions.
RETIN: a smart interactive digital media retrieval system

This demonstration presents a digital media retrieval system for searching large categories in different media databases. The core of our system is an interactive online classification based on user labeling. The classification is obtained with a statistical learning method: kernels for similarity representation and SVM (Support Vector Machine) using binary user annotations. RETIN applies also an active learning strategy for proposing documents to the user for labeling. The system can deal with images, 3D objects and videos and other media can be added to. A graphical user interface allows easy browsing of different media, simple and user-friendly interaction and fast retrieval.
Visual tracking using active appearance models

Visual tracking is one of the fundamental problems in computer vision. The pose of an object extracted through consecutive frames has a variety of applications ranging from robot navigation to camera based man-machine interfaces. In this paper we examine the use of Active Appearance Models(AAM) for the task of visual tracking. The original Active Appearance Model is limited to have all points of the model visible in all frames. We introduce a notion of visibility uncertainty for the points in the AAM, removing the above limitation and therefore allowing the object to contain self-occlusions. The visibility uncertainty is easily integrated into the existing AAM framework, keeping model initialization time to a minimum.We have experiments illustrating that the extension allow AAMs to track through self occlusions at near real-time.
Congestion Control for WCDMA Radio Access Network Using Threshold Segmentation

—The Congestion of WCDMA network becomes a serious problem because each active user introduces interferences to the other users. In order to avoid the instability of mobile network due to the increasing of interference level and blocking probability, when a number of users appear in the system, threshold even and odd segmentation scheme are proposed. In this scheme threshold level is divided into three segments in which users are entering in the system with different levels. The goal of this work is that the maximum numbers of users are simultaneously share the system without interfere to each other . The numerical results are obtained by Matlab.
Listen First: Dialogic Research Ethics With Caribbean Signing Communities

Successful research of Caribbean signed languages and deaf communities involves negotiating complex communication ethics toward both people and languages. In this article, I ground a call for ethical listening to Caribbean deaf and signing communities in sociolinguistic research that investigated deaf community and sign language boundaries in the Caribbean. I argue that a dialogic ethic that privileges listening is foundational for ethical research with Caribbean deaf and signing communities by discussing two ethical challenges that were central to understanding their narrative ground: the communicative construction of categories of linguistic membership and advocacy of social justice and human rights.
“zero-undercut” semi-additive copper patterning - a breakthrough for ultrafine-line RDL lithographic structures and precision RF thinfilm passives

This paper presents the first demonstration of a “zero-undercut” precision formation of ultrafine-line copper conductor patterns for redistribution layers (RDL) and thin film RF passives. This is accomplished by using a highly-anisotropic and uniform copper plasma-etching process to remove the seed layer with no lateral etching of the copper patterns, unlike what is seen with traditional seed-layer removal by wet-etching. Application of this technical breakthrough for large-area panel processes allows demonstration of precision copper patterns demonstrated, for the first time, on organic laminates with no measurable lateral undercut. Two different plasma chemistries, one pure physical sputter-etching, and the other based upon chemical-physical processes with a hydrogen plasma, were investigated and compared.
Proof Reconstruction for Z3 in Isabelle/HOL

Currently, only a few Satisfiability Modulo Theories (SMT) solvers are able to produce proof objects, although there is a strong incentive: Proof objects can be reconstructed in a different system to the check soundness of an SMT solver. We present proof reconstruction for the SMT solver Z3 in Isabelle/HOL and give experimental results of its application.