Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Calibration of the SpotON Ad-Hoc Location Sensing System

The location of equipment, people, and other physical things is essential data to many emerging applications. Unfortunately, location data is often not easy to obtain. We have created SpotON to investigate ad-hoc location sensing , a ﬂexible alternative to infrastructure-centric location systems. SpotON tags use received radio signal strength information as an inter-tag distance estimator. In this paper, we describe ad-hoc location sensing and the SpotON approach. We present speciﬁc results regarding calibration of the SpotON radios and suggest directions for further research.
Modeling genus zero closed manifolds with spherical Powell{Sabin B-splines

We present an algorithm for computing a B-spline representation for Powell{Sabin splines on the sphere. The B-splines form a partition of unity and we dene control points that constitute control triangles that give us a good insight in the shape of the spline. We further consider a number of CAGD applications such as approximation and compression of a given genus zero triangular mesh, and editing global shape and local detail of a spline using spline subdivision.
LP-NUCA: Networks-in-Cache for High-Performance Low-Power Embedded Processors

High-end embedded processors demand complex on-chip cache hierarchies satisfying several contradicting design requirements such as high-performance operation and low energy consumption. This paper introduces light-power (LP) nonuniform cache architecture (NUCA), a tiled-cache addressing both goals. LP-NUCA places a group of small and low-latency tiles between the L1 and the last level cache (LLC) that adapt better to the application working sets and keep most recently evicted blocks close to L1. LP-NUCA is built around three specialized “networks-in-cache,” each aimed at a separate cache operation. To prove the design feasibility, we have fully implemented LP-NUCA in a 90-nm technology. From the VLSI implementation, we observe that the proposed networks-in-cache incur minimal area, latency, and power overhead. To further reduce the energy consumption, LP-NUCA employs two network-wide techniques (miss wave stopping and sectoring) that together reduce the dynamic cache energy by 35% without degrading performance. Our evaluations also show that LP-NUCA improves performance with respect to cache hierarchies similar to those found in high-end embedded processors. Similar results have been obtained after scaling to a 32-nm technology.
Structural Exploration and Conformational Transitions in MDM2 upon DHFR Interaction from Homo sapiens: A Computational Outlook for Malignancy via Epigenetic Disruption

Structural basis for exploration into MDM2 and MDM2-DHFR interaction plays a vital role in analyzing the obstruction in folate metabolism, nonsynthesis of purines, and further epigenetic regulation in Homo sapiens. Therefore, it leads to suppression of normal cellular behavior and malignancy. This has been earlier documented via yeast two-hybrid assays. So, with a novel outlook, this study explores the molecular level demonstration of the best satisfactory MDM2 model selection after performing manifold modeling techniques. Z-scores and other stereochemical features were estimated for comparison. Further, protein-protein docking was executed with MDM2 and the experimentally validated X-ray crystallographic DHFR. Residual disclosure from the best suited simulated protein complex disclosed 18 side chain and 3 ionic interactions to strongly accommodate MDM2 protein into the pocket-like zone in DHFR due to the positive environment by charged residues. Lysine residues from MDM2 played a predominant role. Moreover, evaluation from varied energy calculations, folding rate, and net area for solvent accessibility implied the active participation of MDM2 with DHFR. Fascinatingly, conformational transitions from coils to helices and β-sheets after interaction with DHFR affirm the conformational strength and firmer interaction of human MDM2-DHFR. Therefore, this probe instigates near-future clinical research and interactive computational investigations with mutations.
Catalog of genetic variants within mature microRNA seed regions in chicken

MicroRNA (miRNA) is a class of noncoding RNA important in posttranscriptional regulation of target genes. The regulation mechanism requires complementarity between target mRNA and the miRNA region responsible for their recognition and binding, also called the seed region. It has been estimated that each miRNA targets approximately 200 genes and genetic variability of miRNA genes has been associated with phenotypic variation and disease susceptibility in humans, livestock species, and model organisms. Polymorphisms in miRNA genes especially within the seed region could therefore represent biomarkers for phenotypic traits important in livestock animals. Using the updated Version 5.0 of our previously developed bioinformatics tool miRNA SNiPer we assembled polymorphic miRNA genes in chicken. Out of 740 miRNA genes 263 were polymorphic, among them 77 had SNPs located within the mature region, and 29 of them within the miRNA seed region. Because several polymorphisms in databases result from sequencing errors, we performed experimental validation of polymorphisms located within 4 selected miRNA genes in chicken (gga-mir-1614, −1644, −1648, and −1657). We confirmed the presence of nine polymorphisms and identified 3 additional novel polymorphisms within primary miRNA regions in chicken representing 3 layer-type breeds, one layer-type hybrid, and one meat-type intercrossed population. The developed catalog of mir-SNPs in chicken can serve researchers as a starting point for association studies dealing with poultry production traits and designing functional experiments.
LEXUS, a web-based tool for manipulating lexical resources lexicon

LEXUS provides a flexible framework for the maintaining lexical structure and content. It is the first implementation of the Lexical Markup Framework model currently being developed at ISO TC37/SC4. Amongst its capabilities are the possibility to create lexicon structures, manipulate content and use of typed relations. Integration of well established Data Category Registries is supported to further promote interoperability by allowing access to well established linguistic concepts. Advanced linguistic functionality is offered to assist users in cross lexica operations such as search and comparison and merging of lexica. To enable use within various user groups the look and feel of each lexicon may be customized. In the near future more functionality will be added including integration with other tools accessing lexical content.
Analysis of Whole Transcriptome Sequencing Data: Workflow and Software

RNA is a polymeric molecule implicated in various biological processes, such as the coding, decoding, regulation, and expression of genes. Numerous studies have examined RNA features using whole transcriptome sequencing (RNA-seq) approaches. RNA-seq is a powerful technique for characterizing and quantifying the transcriptome and accelerates the development of bioinformatics software. In this review, we introduce routine RNA-seq workflow together with related software, focusing particularly on transcriptome reconstruction and expression quantification.
A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks

A robust optimization model is considered for nonzero-sum discounted stochastic games with incomplete information in order to formally formulate and analyze the intrusion detection problem in wireless sensor networks (WSNs). Security requirements of WSNs are taken into account to characterize the game parameters and model the player objectives. To generalize the problem, the game data are assumed not to be fully known to the players, who take a robust optimization approach to address this data uncertainty. For assessing the validity and effectiveness of the framework, illustrative instances of the developed game model are generated. Equilibrium analysis reveals how the conflicting objectives of the intruder and intrusion detection system compel them to adopt different conservative stances toward data uncertainty. It is also shown, by numerical results, that the robust approach in the presence of uncertainty reduces the sensitivity of the solution with respect to data perturbations, and thus improves design stability.
Image features optimizing for content-based image retrieval

Developing low-dimensional semantics-sensitive features is crucial for content-based image retrieval (CBIR). In this paper, we present a method called M2CLDA (merging 2-class linear discriminant analysis) to capture low-dimensional optimal discriminative features in the projection space. M2CLDA calculates discriminant vectors with respect to each class in the one-vs-all classification scenario and then merges all the discriminant vectors to form a projection matrix. The dimensionality of the M2CLDA space fits in with the number of classes involved. Moreover, when a new class is added, the new M2CLDA space can be approximated by only calculating a new discriminant vector for the new class. The features in the M2CLDA space have better semantic discrimination than those in traditional LDA space. Our experiments show that the proposed approach improves the performance of image retrieval and image classification dramatically.
Treatment of Intercondylar Humeral Fractures With 3D-Printed Osteosynthesis Plates

Abstract The aim of the study was to evaluate the efficacy custom 3D-printed osteosynthesis plates in the treatment of intercondylar humeral fractures. Thirteen patients with distal intercondylar humeral fractures were randomized to undergo surgery using either conventional plates (n = 7) or 3D-printed plates (n = 6) at our institution from March to October 2014. Both groups were compared in terms of operative time and elbow function at 6 month follow-up. All patients were followed-up for a mean of 10.6 months (range: 6–13 months). The 3D-printing group had a significantly shorter mean operative time (70.6 ± 12.1 min) than the conventional plates group (92.3 ± 17.4 min). At the last follow-up period, there was no significant difference between groups in the rate of patients with good or excellent elbow function, although the 3D-printing group saw a slightly higher rate of good or excellent evaluations (83.1%) compared to the conventional group (71.4%). Custom 3D printed osteosynthesis plates are safe and effective for the treatment of intercondylar humeral fractures and significantly reduce operative time.
Discovery of a Low-Mass Bipolar Molecular Outflow from L1014-IRS with the Submillimeter Array

Using the Submillimeter Array, we report the discovery of a compact low-mass bipolar molecular outflow from L1014-IRS and confirm its association with the L1014 dense core at 200 pc. Consequently, L1014-IRS is the lowest luminosity (L ~ 0.09 L☉), and perhaps the lowest mass, source known to be driving a bipolar molecular outflow, which is one of the smallest known in size (~500 AU), mass (<10-4 M☉), and energetics (e.g., with a force <10-7 M☉ km s-1 yr-1).
Microtubule-Associated Protein 1A (MAP1A) and MAP1B: Light Chains Determine Distinct Functional Properties

The microtubule-associated proteins 1A (MAP1A) and 1B (MAP1B) are distantly related protein complexes consisting of heavy and light chains and are thought to play a role in regulating the neuronal cytoskeleton, MAP1B during neuritogenesis and MAP1A in mature neurons. To elucidate functional differences between MAP1B and MAP1A and to determine the role of the light chain in the MAP1A protein complex, we chose to investigate the functional properties of the light chain of MAP1A (LC2) and compare them with the light chain of MAP1B (LC1). We found that LC2 binds to microtubules in vivo andin vitro and induces rapid polymerization of tubulin. A microtubule-binding domain in its NH2 terminus was found to be necessary and sufficient for these activities. The analysis of LC1 revealed that it too bound to microtubules and induced tubulin polymerization via a crucial but structurally unrelated NH2-terminal domain. The two light chains differed, however, in their effects on microtubule bundling and stabilityin vivo. Furthermore, we identified actin filament binding domains located at the COOH terminus of LC2 and LC1 and obtained evidence that binding to actin filaments is attributable to direct interaction with actin. Our findings establish LC2 as a crucial determinant of MAP1A function, reveal LC2 as a potential linker of neuronal microtubules and microfilaments, and suggest that the postnatal substitution of MAP1B by MAP1A leads to expression of a protein with an overlapping but distinct set of functions.
Identifying location in indonesian documents for geographic information retrieval

Our research focuses on Geographic Information Retrieval for Indonesian documents. We constructed a Geographical Gazeeter for geographic locations based on information that we collected from the Geographic resources. We used the table to identify the locations of events that are mentioned in the documents. We added a location index associated with each document. We also identified the possible locations based on words that indicate the relative position to locations mentioned in the queries. The result of our experiments showed that the location index and the processing of words indicating relative positions improved the retrieval performance of natural language queries. Applying the query expansion techniques by adding keywords and locations from the documents to the queries resulted in significant performance improvement.
Computer arithmetic structures for quantum cellular automata

In this paper, we discuss arithmetic structures based on quantum cellular automata (QCA). By taking advantage of the unique capabilities of QCA we are able to design interesting computational architectures. We describe important design considerations and show how addition and multiplication circuits can be implemented using QCADesigner, a QCA design tool which has been developed in our laboratory. QCA technology allows, among other things, the implementation of majority boolean gates and interconnecting "wires" that support cross-overs on the same fabrication level. One of the important challenges with QCA design is working within a different cost function from standard transistor circuits. These differences arise from the device level latching inherent in QCA. This latching makes the total delay of a circuit directly proportional to the maximum number of clocking zones between input and output and the number of gates.
Fostering critical teacher agency: the impact of a science capital pedagogical approach

Abstract Teacher agency is considered key in shaping teachers’ professional identities and decision-making capabilities. We suggest that the concept of agency also constitutes a useful tool for evaluating the successful implementation of new teaching approaches. In this paper we discuss findings from a teacher professional development programme aimed at enabling science capital building approaches in the classroom. By applying the lens of agency we identified developments in teachers’ sense of purpose, mastery, reflexivity and autonomy. We also identified factors which appeared to either promote or constrain the acquisition of agency. The science capital pedagogy is underpinned by a social justice agenda. In supporting teachers to ‘tweak’ their practice in ways that provide more opportunities for more students to identify with science, we suggest that the agency fostered by the intervention may best be defined as critical teacher agency.
Embodied perception during walking using Deep Recurrent Neural Networks

Movements such as walking require knowledge of the environment in order to be robust. This knowledge can be gleaned via embodied perception. While information about the upcoming terrain such as compliance, friction, or slope may be difficult to directly estimate, using the walking motion itself allows for these properties to be implicitly observed over time from the stream of movement data. However, the relationship between a parameter such as ground compliance and the movement data may be complex and difficult to discover. In this thesis, we demonstrate the use of a Deep LSTM Network to estimate slope and ground compliance of terrain by observing a stream of sensory information that includes the character state and foot pressure information.
Optimal route selection for highly dynamic wireless sensor and actor networks environment

Wireless Sensor and Actor Networks (WSANs) are increasingly being used as powerful and accurate tools for supervision and control of physical environments subject to emergency situations. In a fire condition, for instance, the sensor and actor network status can change dramatically. This paper presents a novel routing protocol that is aware of these changes and adjusts the routes accordingly, aiming to keep the Quality of Service required by the application. Route selection for different types of packets is performed considering QoS parameters such as latency, delivery rate and packet loss rate. Our protocol is also energy-aware and fault tolerant. Performance evaluation results show that our protocol outperforms other existing QoS-aware protocols, providing selective traffic with lower end-to-end latency, packet loss, and control packet overhead, besides network energy balancing and fault tolerance.
Perceptual Image Hashing Based on Shape Contexts and Local Feature Points

Local feature points have been widely investigated in solving problems in computer vision, such as robust matching and object detection. However, its investigation in the area of image hashing is still limited. In this paper, we propose a novel shape-contexts-based image hashing approach using robust local feature points. The contributions are twofold: 1) The robust SIFT-Harris detector is proposed to select the most stable SIFT keypoints under various content-preserving distortions. 2) Compact and robust image hashes are generated by embedding the detected local features into shape-contexts-based descriptors. Experimental results show that the proposed image hashing is robust to a wide range of distortions and attacks, due to the benefits of robust salient keypoints detection and the shape-contexts-based feature descriptors. When compared with the current state-of-the-art schemes, the proposed scheme yields better identification performances under geometric attacks such as rotation attacks and brightness changes, and provides comparable performances under classical distortions such as additive noise, blurring, and compression. Also, we demonstrate that the proposed approach could be applied for image tampering detection.
Confidence Band for Additive Regression Model

Additive model is widely recognized as an effective tool for di- mension reduction. Existing methods for estimation of additive regression function, including backfitting, marginal integration, projection and spline methods, do not provide any level of uniform confidence. In this paper a sim- ple construction of confidence band is proposed for the additive regression function based on polynomial spline estimation and wild bootstrap. Monte Carlo results show three desirable properties of the proposed band: excellent coverage of the true function, width rapidly shrinking to zero with increasing sample size, and minimal computing time. These properties make he pro- cedure is highly recommended for nonparametric regression with confidence when additive modelling is appropriate.
A Stochastic Time-to-Digital Converter for Digital Phase-Locked Loops

for their valuable guidance over the course of the last two years. I would also like to thank members of my thesis committee for their useful feedback. Thanks to Pavan Hanumolu and Gil-Cho Ahn for always being supportive and providing helpful insight into design issues. Thanks to everyone else in lab for being open to answering questions and putting up with me during soccer. Thanks to AFRL for making my assistantship possible by funding this research project. Thanks to Tufan for always being ready to go lifting or to Platinum (Thanks Dave) or Senor Sam's with me, acting as my airport shuttle, doing house or driving around Corvallis. Many thanks to Cumhur for helping a lot with organizing the defense and always saying yes to sushi. Thanks to both for late night Shari's runs, daily doses of Dutch Bros. /Starbucks and cigars on the roof or at the MU quad. Thanks mom and dad, I hope I can raise my kids the way you raised me. Finally, thanks Bebegim for always being patient with me, always supporting me and always being so sweet.
An adaptive multi-module cache with hardware prefetching mechanism for multimedia applications

This research is to design a high performance cache structure with a hardware prefetching mechanism that is applicable to embedded multimedia processors. Our cache organization and operational mechanism are especially designed to maximize temporal locality and spatial locality, selectively and adaptively. The overhead of prefetching operation is shown to be a negligible factor. Also the accuracy of the prefetch operation is over 97% of the total number of prefetches generated. Simulation shows that the average memory access time of the proposed cache is equal to that of a conventional direct-mapped cache with eight times as much space. In addition, the simulations show that our cache achieves better performance than a 2-way or 4-way set associative cache with four or twice as much space respectively. And also, compared with a victim cache with 32-byte block size, the average miss ratio can be reduced by over twice for multimedia applications. We have also shown that power consumption in the AMMC is around 10% /spl sim/ 60% lower than these various cache systems.
SEI and SHI representations for human movement recognition

In this paper, we propose spatio-temporal silhouette representations, called silhouette energy image (SEI) and silhouette history image (SHI) to characterize motion and shape properties for recognition of human movements such as human actions, activities in daily life. The SEI and SHI are constructed by using the silhouette image sequence of an action. The span or difference of the end time and start time is used to make the SHI. For addressing the human shape variability, we used the variation of the anthropometry of the person. We extract the features based on geometric shape moments. We tested our approach successfully in the indoor and outdoor environment. Our experimental results show that the proposed method of human action recognition is robust, flexible and efficient.
Understanding the Impact of Pseudo Randomness on Internet Worm Propagation

Recent research has shown that worms continue to remain among the significant infectors of the Internet [1]. Our understanding of worms’ propagation and malicious potential must keep in pace with their rapid evolution. In this paper, we point out a fundamental flaw in the past worm analysis — ignoring the importance of pseudo randomness employed in the worm code. The impact of pseudo randomness is twofold. First, it breaks the randomness assumptions underlying the classical epidemic worm model and casts doubt on the correctness of the model. Second, it has a potential of making all existing randomscanning worms substantially more effective and stealthier. To precisely study such impact, we derive a detailed, closed-form model for realistic worms that perform pseudo-random scanning and utilize pseudo randomness to reduce the volume of scanning traffic. Our worm model offers a number of interesting insights: Pseudo randomness is able to make worms stealthier without any sacrifice in their propagation speed. It reduces the volume of the worms’ scanning traffic to within a factor of 1.5 from the optimal, and reduces the maximum number of infected hosts that perform scanning simultaneously by 57%. Based on the model, we conclude that rate-limit techniques will remain effective when pseudo randomness is taken into account. However, defense techniques targeting at scanning volume will be less effective.