Miracle-dz/UniFAR-FTUs huggingface dataset:
Institutions, Resources, and Entry Strategies in Emerging Economies

We investigate the impact of market-supporting institutions on business strategies by analyzing the entry strategies of foreign investors entering emerging economies. We apply and advance the institution-based view of strategy by integrating it with resource-based considerations. In particular, we show how resource-seeking strategies are pursued using different entry modes in different institutional contexts. Alternative modes of entry— greenfield, acquisition, and joint venture (JV)—allow firms to overcome different kinds of market inefficiencies related to both characteristics of the resources and to the institutional context. In a weaker institutional framework, JVs are used to access many resources, but in a stronger institutional framework, JVs become less important while acquisitions can play a more important role in accessing resources that are intangible and organizationally embedded. Combining survey and archival data from four emerging economies, India, Vietnam, South Africa, and Egypt, we provide empirical support for our hypotheses.
Error rate analysis of threshold-based hybrid selection/maximal-ratio diversity over correlated nakagami-m fading channels

An exact performance analysis of triple-branch threshold-based hybrid selection/maximal-ratio combining (T-HS/MRC) receivers over correlated Nakagami-m fading channels is presented. Our analysis is valid for integer-order fading parameters and an arbitrary covariance matrix. Following the moment-generating function-based approach, the error rate performance of T-HS/MRC receivers for various modulation formats is analytically obtained. Various performance evaluation results are also presented and compared to equivalent simulation ones.
Babylon Parallel Text Builder: Gathering Parallel Texts for Low-Density Languages

This paper describes Babylon, a system that attempts to overcome the shortage of parallel texts in low-density languages by supplementing existing parallel texts with texts gathered automatically from the Web. In addition to the identification of entire Web pages, we also propose a new feature specifically designed to find parallel text chunks within a single document. Experiments carried out on the Quechua-Spanish language pair show that the system is successful in automatically identifying a significant amount of parallel texts on the Web. Evaluations of a machine translation system trained on this corpus indicate that the Web-gathered parallel texts can supplement manually compiled parallel texts and perform significantly better than the manually compiled texts when tested on other Web-gathered data.
Pelvic Myxoid Leiomyoma Mass between Vagina and Rectum

Leiomyomas are the most common pelvic tumors in women. About 20–30% of women older than 35 are affected. Rare conditions of leiomyomas have extrauterine locations. Myxoid degeneration is a rare type of leiomyoma degeneration. We report a case of solid-cystic myxoid leiomyoma in a 53-year-old woman complained of constipation, urinary hesitation, and malodorous vaginal discharge with palpable 17 × 12 cm mass between vagina and rectum. Regarding the inferior location of the mass, a perineal approach was used to enucleate it. This rare location has not been mentioned before. The woman was finally diagnosed by pathologists which was myxoid leiomyoma.
Multicore Cache Simulations Using Heterogeneous Computing on General Purpose and Graphics Processors

Traditional trace-driven memory system simulation is a very time consuming process while the advent of multicores simply exacerbates the problem. We propose a framework for accelerating trace-driven multicore cache simulations by utilizing the capabilities of the modern many core GPUs. A straightforward way towards this direction is to rely on the inherent parallelism in cache simulations: communicating cache sets can be simulated independently and concurrently to other sets. Based on this, we map collections of communicating cache sets (each belonging to a different target cache) on the same GPU block so that the simulated coherence traffic is local traffic in the GPU. However, this is not enough due to the great imbalance in the activity in the different cache sets: some sets receive a flurry of activity while others do not. Our solution is to load balance the simulated sets (based on activity) on the computing element (host-CPU or GPU) that can manage them in the most efficient way. We propose a heterogeneous computing approach in which the host-CPU simulates the few but most active sets, while the GPU is responsible for the many more but less active sets. Our experimental findings using the SPLASH-2 suite demonstrate that our cache simulator based on the CPU-GPU cooperation achieves on average 5.88x speedup over alternative implementations running on CPU, speedups which scale well with the size of the simulated system.
A Persuading Approach for Cooperating Nodes in Mobile Ad Hoc Networks

A Mobile Ad Hoc Network adapts a distributed communication approach relying on cooperative interaction among nodes at each layer of the protocol stack. Confidence on member's cooperation makes the ad hoc network very sensitive to node behavior. Specifically, for functions such as routing, nodes which are limited in their resources may be unwilling to cooperate in forwarding data and control packets for other nodes. Such selfish behavior leads to degradation in network's performance badly. Hence, it is important to devise solutions in order to encourage the resource constrained nodes to cooperate in other member's process. In this paper, a novel method is proposed to stimulate nodes for cooperating regarding complexity and performance issues.
Understanding individual and collective mobility patterns from smart card records: A case study in Shenzhen

Understanding the dynamics of the inhabitants' daily mobility patterns is essential for the planning and management of urban facilities and services. In this paper, novel aspects of human mobility patterns are investigated by means of smart card data. Using extensive smart card records resolved in both time and space, we study the mean collective spatial and temporal mobility patterns at large scales and reveal the regularity of these patterns. We also investigate patterns of travel behavior at the individual level and show that the concentricity and regularity of mobility patterns. The analytical methodologies to spatially and temporally quantify, visualize, and examine urban mobility patterns developed in this paper could provide decision support for transport planning and management.
Designing Random Sample Synopses with Outliers

Random sampling is one of the most widely used means to build synopses of large datasets because random samples can be used for a wide range of analytical tasks. Unfortunately, the quality of the estimates derived from a sample is negatively affected by the presence of "outliers" in the data. In this paper, we show how to circumvent this shortcoming by constructing outlier-aware sample synopses. Our approach extends the well-known outlier indexing scheme to multiple aggregation columns.
A generic infrastructure for benchmarking motion planners

Randomized planners, search-based planners, potential-field approaches and trajectory optimization based motion planners are just some of the types of approaches that have been developed for motion planning. Given a motion planning problem, choosing the appropriate algorithm to use is a daunting task even for experts since there has been relatively little effort in comparing the plans generated by the different approaches, for different problems. In this paper, we present a set of benchmarks and the associated infrastructure for comparing different types of motion planning approaches and algorithms. The benchmarks are specifically designed for robotics and include typical indoor human environments. We present example motion planning problems for single arm tasks. Our infrastructure is designed to be easily extensible to allow for the addition of new planning approaches, new robots, new environments and new metrics. We present results comparing the performance of several motion planning algorithms to validate the use of these benchmarks.
Galaxy clustering and dark energy

We study the evolution of galaxy clustering in various cosmological models with quintessence. We investigate how the analytical predictions vary with the change of the dark energy equation of state wX. Comparing these predictions against available data, we discuss to what extent the problems of galaxy biasing can be modelled. This will be key in constraining the dark energy equation of state with future galaxy surveys. We use a compilation of various surveys to study the number density and amplitude of galaxy clustering from observations of the local universe at z∼ 0 to that of the Lyman-break galaxies and Ly α emitters at z∼ 4.9. We find that there is a degeneracy between the dark energy equation of state and the way galaxies populate dark matter haloes; objects are more biased in models with more negative values of dark energy equation of state wX. We conclude that, while future all-sky cosmic microwave background observations will determine cosmological parameters with unprecedented precision, and cross-correlation of weak lensing experiments and galaxy surveys will provide a cleaner and more accurate picture of bias associated with collapsed objects, the rate of growth of large-scale structure in such surveys can potentially constrain the equation of state of dark energy and the potential of the scalar field associated with quintessence. In particular, we show that the abundance and spatial distribution of galaxy clusters at intermediate redshifts strongly depend on the dark energy equation of state. When accurate measurement of galaxy clustering at high redshift becomes possible, it will provide constraints on dark energy that are independent and complementary to Type Ia supernova studies.
Fuzzy learning variable admittance control for human-robot cooperation

This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert knowledge for intuitive cooperation. A Fuzzy Model Reference Learning Controller is used to adapt the Fuzzy Inference System according to the minimum jerk trajectory model. To evaluate the performance of the proposed controller a point-to-point cooperation task is conducted with multiple subjects using a KUKA LWR robot.
Explore the Possibility of Utilizing Blog Semantic Analysis for Domain Expert and Opinion Mining

Democratic in nature, the Blogosphere allows individuals to accumulate a tremendous amount of personal knowledge, share experiences, and express opinions. Based on blog contents and social-referral data, blog semantic analysis can identify individual expertise and an appropriate social network, thus obtaining word-of-mouth reviews of certain products or services. Effectively utilizing this opinion cloud significantly contributes to efforts to devise decision support applications such as domain expert billboard and mobile POI recommendation.
Distributed Frequent Itemset Mining using Trie Data Structure

Finding association rules is one of the most investigated fields of data mining. Computation and communication are two important factors in distributed association rule mining. In this problem Association rules are generated by first mining of frequent itemsets in distributed data. In this paper we proposed a new distributed trie-based algorithm (DTFIM) to find frequent itemsets. This algorithm is proposed for a multi-computer environment. In second phase we added an idea from FDM algorithm for candidate generation step. Experimental evaluations on different sort of distributed data show the effect of using this algorithm and adopted techniques.
M4M: A model for enabling social network based sharing in the Internet of Things

The true potential of the Internet of Things (IoT) will be realized only when devices are able to harness the collective capabilities of a wide range of peer-devices. In this paper, we propose a novel model where friends in a social network can share device capabilities with their peers in an access controlled manner. We develop a theoretical model of such a peer-to-peer network in which devices can search for remote capabilities, and elaborate on the trade-offs of different algorithms in terms of capability searching and execution. We study various types of social network models to understand the degree of sharing in such networks. Our results show that Barabási-Albert graphs that approximate most real world social networks have a high degree of sharing, validating the utility of our social network based model. We also propose an algorithm that takes any given network and augments it to increase the level of sharing by intelligently suggesting friendship recommendations among vertices. Finally, we describe the prototype implementation of an android mobile application that uses facebook APIs to allow smartphones share GPS and camera capabilities with other devices owned by friends.
DARD : A Practical Distributed Adaptive Routing Architecture for Datacenter Networks

Datacenter networks typically have multiple paths connecting each host pair to achieve higher bisection bandwidth. However, existing transport and routing protocols have little support for multipath in the scale of a datacenter. We advocate to design practical datacenter architectures for easy operation, i.e., an architecture should be reliable, capable of improving bisection bandwidth, scalable and debugging-friendly. By strictly following these four guidelines, We propose DARD, a Distributed Adaptive Routing architecture for Datacenter networks. DARD allows each end host to reallocate traffic from overloaded paths to underloaded paths without central coordination. We use congestion game theory to show that DARD converges to a Nash equilibrium in finite steps and its gap to the optimal flow allocation is bounded in the order of 1 logL with L being the number of links. We use a testbed implementation and simulations to show that DARD can achieve a close-to-optimal flow allocation with small control overhead in practice.
Hardware-in-the-loop simulation as a stage of plasma control system model-based design in tokamaks: A test case

The paper is devoted to a hardware-in-the-loop simulation stage in the whole cycle of tokamak plasma control system design starting from practical needs and challenges and ending up in applications of developed control systems deployed in real world plants to achieve desirable objectives. Control problems of tokamak plasmas are described. The hardware-in-the-loop test case simulation is implemented into the Test Bed which consists of target PCs for modeling MIMO plant and controller on real-time operating system xPC Target from MathWorks and a workstation (Host PC) with MATLAB/Simulink development environment. This environment allows to automatically generate and compile plant and controller codes with Real-Time Workshop and to upload compiled programs to the target PCs. The raw Ethernet is used to connect the target PCs to each other. Two plasma shape and current MIMO H∞ control systems were designed and simulated on the Test Bed specifically for tokamaks T-15MD and ITER on linear plasma models obtained from nonlinear plasma physics DINA code on plasma current flat-top phase. The real time hardware-in-the-loop simulation results are presented for both tokamaks when minor disruptions occur.
Tools to design new devices for rehabilitation or robotics

Several groups are working on the design of a new generation of robots and haptics for the activities of daily life or rehabilitation of elderly people or patients with some pathology. Sometime their forces in power grasping are different from healthy people. This paper is focused on the study of grasp forces of these patients with different pathologies and presents a new methodology to calculate grasp forces applied by virtual human hand when grasping any object with power. The Denavit-Hartenberg method is implemented to analyze the 25-degree-of-freedom (DOF) hand model of the virtual humans. Human performance measures (joint torques and joint displacements) are criteria for the design of joysticks in the material handling machines. Finally we compare these analytical results with experiments where n =3D 16 persons. Based on a comfortable position for the hand a new model is proposed to design joysticks for patients or the elderly.
Equivalence of Two Nonequilibrium Ensembles Based on Maximum Entropy Principle

The relation between two versions of so called non-equilibrium statistical operator method (NESOM), NESOM-1 due to Zubarev (1961) and NESOM-2 due to Zubarev and Kalashnikov (1970), is considered. It is proved that, once the balance equations of NESOM-2 are satisfied, those of NESOM-1 will be satisfied with the same set of the macro-parameters. The proof uses the convexity-type inequalities, and does not involve any assumptions additional to the rationales behind NESOM. However, converse statement cannot be proved within this technique. An extension of the proof to overall equivalence of the two nonequilibrium ensembles is discussed. Dedicated to the memory of Dmitrii Nikolaevich Zubarev
Fast displacement estimators for freehand ultrasonic strain imaging

Ultrasonic strain imaging aims to display mechanical stiffness parameters of soft tissue. This not yet commonly used medical imaging modality can help improve tumor detection and diagnosis based on the fact that many pathological changes are related to increases in tissue stiffness. Since real-time imaging is often indispensible for diagnostic purposes in clinical practice, fast estimation methods are needed. We investigated the performances of one novel and several established estimators with real-time capability and found that two are especially well-suited for freehand strain imaging. Results are from both Field II simulations and experiments using a commercial ultrasound scanner and a tissue-mimicking phantom that was designed to represent a typical tumor situation.
Sharing Touch Interfaces: Proximity-Sensitive Touch Targets for Tablet-Mediated Collaboration

During conversational practices, such as a tablet-mediated sales conversation between a salesperson and a customer, tablets are often used by two users who prefer specific bodily formations in order to easily face each other and the surface of the touchscreen. In a series of studies, we investigated bodily formations that are preferred during tablet-mediated sales conversations, and explored the effect of these formations on performance in acquiring touch targets (e.g., buttons) on a tablet device. We found that bodily formations cause decreased viewing angles to the shared screen, which results in a decreased performance in target acquisition. In order to address this issue, a multi-modal design consideration is presented, which combines mid-air finger movement and touch into a unified input modality, allowing the design of proximity sensitive touch targets. We conclude that the proposed embodied interaction design not only has potential to improve targeting performance, but also adapts the ``agency' of touch targets for multi-user settings.
Vehicle Lane Change Maneuver In Automated Highway Systems

In this report, lane change maneuver for an automated highway system is investigated as a tracking problem with respect to the virtual desired trajectory (VDT). The two main issues discussed in the report are: 1) design of virtual desired trajectory and 2) design of control algorithms. Four types of desired trajectories are considered. The use of onboard sensors for closed loop control are used. Three closed loop controllers, based on 1) linear quadratic (LC) optimal control, 2) frequency shaped linear quadratic (FSLQ), and 3) sliding mode control are evaluated. Simulation results show that these controllers provide acceptable performance in the presence of uncertainties and disturbances.
Determinants for Stop-transfer and Post-import Pathways for Protein Targeting to the Chloroplast Inner Envelope Membrane*

The inner envelope membrane (IEM) of the chloroplast plays key roles in controlling metabolite transport between the organelle and cytoplasm and is a major site of lipid and membrane synthesis within the organelle. IEM biogenesis requires the import and integration of nucleus-encoded membrane proteins. Previous reports have led to the conclusion that membrane proteins are inserted into the IEM during protein import from the cytoplasm via a stop-transfer mechanism or are completely imported into the stroma and then inserted into the IEM in a post-import mechanism. In this study, we examined the determinants for each pathway by comparing the targeting of APG1 (albino or pale green mutant 1), an example of a stop-transfer substrate, and atTic40, an example of a post-import substrate. We show that the APG1 transmembrane domain is sufficient to direct stop-transfer insertion. The APG1 transmembrane domain also functions as a topology determinant. We also show that the ability of the post-import signals within atTic40 to target proteins to the IEM is dependent upon their context within the full protein sequence. In the incorrect context, the atTic40 signals can behave as stop-transfer signals or fail to target fusion proteins to the IEM. These data suggest that the post-import pathway signals are complex and have evolved to avoid stop-transfer insertion.
Optimizing the quality of bootstrap-based prediction intervals

The bootstrap method is one of the most widely used methods in literature for construction of confidence and prediction intervals. This paper proposes a new method for improving the quality of bootstrap-based prediction intervals. The core of the proposed method is a prediction interval-based cost function, which is used for training neural networks. A simulated annealing method is applied for minimization of the cost function and neural network parameter adjustment. The developed neural networks are then used for estimation of the target variance. Through experiments and simulations it is shown that the proposed method can be used to construct better quality bootstrap-based prediction intervals. The optimized prediction intervals have narrower widths with a greater coverage probability compared to traditional bootstrap-based prediction intervals.
A Web Services Composition Model and Its Verification Algorithm Based on Interface Automata

Web service is a key distributed computing technology to achieve "software as a service" (SaaS) in cloud computing. How to integrate various web services according to business processes correctly and efficiently, realize the seamless integration of services, and form enterprise level service processes with abundant functions has become an important problem. In this paper, we presented a new web services composition model and its verification algorithm based on interface automata. By extending interface automata, this model supports semantic descriptions of web services. We proposed the transformation rules and transformation algorithm between BPEL and the semantic service interface automata model. Furthermore, an interface automata composition algorithm was designed to achieve the concurrent composition of interface automata. In order to judge whether the service process generated satisfies the business function requirements, a verification algorithm was designed to validate the execution sequence of the concurrent composite interface automaton, and the simulation experiment showed that verification algorithm could be used to validate the consistency of the service process and the business process correctly and effectively.