Miracle-dz/UniFAR-FTUs huggingface dataset:
A General Modulation Strategy for a Five-Level Three-Phase Current Source Inverter with Regulated Intermediate DC Link Currents

Multilevel converters use series/parallelled semiconductor switching devices to synthesise switched waveforms at power levels that are well above individual device ratings. To date, most multilevel inverter research has focused on voltage source structures, primarily because these topologies address the more common high power converter limiting factor of device voltage ratings. However, multilevel current source converters can have advantages in lower voltage, very high current applications, or in situations where, despite their higher losses, DC inductors have reliability benefits compared to electrolytic capacitors. This paper presents an integrated three phase 5-level current source inverter topology that requires only two intermediate link inductors, and shows how it can be controlled by mapping modulation control signals from a multilevel VSI controller. This allows the wealth of existing knowledge relating to modulation of multilevel VSI's to be immediately applied to the multilevel CSI. The mapping process matches space vectors created by the VSI modulation process to equivalent CSI space vectors, and then selects between redundant space vector alternatives to maintain the current balance of the intermediate link inductors. The converter operation has been verified in a detailed circuit simulation taking account of all second order effects.
Relay misbehavior detection for robust diversity combining in cooperative communications

Most previous studies on cooperative communications assume that the relays operate normally and are trustworthy. However, this assumption may not always be true in practice. Accordingly, this study proposes a robust cooperative communication scheme in the physical layer for combating relay misbehavior. Two models for cooperative communications are considered, namely with direct path (WDP) and without direct path (WODP). For each model, a signal-correlation detection scheme is proposed in which the destination identifies the misbehaving relays within the network and then excludes their messages when performing diversity combining to infer the symbols of interest sent by the source. The proposed signal-correlation-detection rules are designed in such a way as to minimize the probability of error in identifying the misbehaving relays. The simulation results show that the proposed schemes yield an excellent detection performance in cooperative communication networks with relay misbehavior.
PEAK SIGNAL-TO-NOISE RATIO BASED ON THRESHOLD METHOD FOR IMAGE SEGMENTATION

Binarization or thresholding is one problem that must be solved in pattern recognition and it has a very important influence on the sequent steps in imaging applications. Thresholding is used to separate objects from the background, and diminish the amount of data alter the computational speed. Recently, interest in multilevel thresholding has been altered. However, when the levels are altered, the computation time alters so single threshold methods are accelerated than multilevel methods. Moreover, for every new application, new methods are is acquired. In this work, a new algorithm which used the gain signal-to-noise ratio method as an indicator to segment the image is aimed. The algorithm which is used the DIBCO 2011 in printed and a handwritten image was tested. This method has a better performance than new methods, such as Kittler and Illingworth's Minimum Error Thresholding, potential difference and Otsu.
Estimating the Effect of Parliamentary Elections on Primary Budget Deficits in OECD Countries

Using an unbalanced panel of 24 OECD countries for the period 1986-2005 the paper empirically tests the political budget cycle hypothesis. The econometric approach is based on the equation proposed by BOHN (1998) for testing the sustainability of fiscal policy and system GMM estimators. The empirical results strongly support the hypothesis of smaller primary surpluses (only) in election years. The result found by BRENDER and DRAZEN (2005) that an election effect exists only in new democracies is rejected. However, in contrast to the political budget cycle hypothesis, it is argued that the result may rather be explained by governments' attempt to avoid intra-governmental conflicts on limited budgetary funds during election years, since this may be interpreted as an adverse signal by the voters. Besides, the results indicate only a temporary effect of the European Monetary Union.
Global Prevalence and Major Risk Factors of Diabetic Retinopathy

OBJECTIVE To examine the global prevalence and major risk factors for diabetic retinopathy (DR) and vision-threatening diabetic retinopathy (VTDR) among people with diabetes. RESEARCH DESIGN AND METHODS A pooled analysis using individual participant data from population-based studies around the world was performed. A systematic literature review was conducted to identify all population-based studies in general populations or individuals with diabetes who had ascertained DR from retinal photographs. Studies provided data for DR end points, including any DR, proliferative DR, diabetic macular edema, and VTDR, and also major systemic risk factors. Pooled prevalence estimates were directly age-standardized to the 2010 World Diabetes Population aged 20–79 years. RESULTS A total of 35 studies (1980–2008) provided data from 22,896 individuals with diabetes. The overall prevalence was 34.6% (95% CI 34.5–34.8) for any DR, 6.96% (6.87–7.04) for proliferative DR, 6.81% (6.74–6.89) for diabetic macular edema, and 10.2% (10.1–10.3) for VTDR. All DR prevalence end points increased with diabetes duration, hemoglobin A1c, and blood pressure levels and were higher in people with type 1 compared with type 2 diabetes. CONCLUSIONS There are approximately 93 million people with DR, 17 million with proliferative DR, 21 million with diabetic macular edema, and 28 million with VTDR worldwide. Longer diabetes duration and poorer glycemic and blood pressure control are strongly associated with DR. These data highlight the substantial worldwide public health burden of DR and the importance of modifiable risk factors in its occurrence. This study is limited by data pooled from studies at different time points, with different methodologies and population characteristics.
Data Allocation in Heterogeneous Disk Arrays

We consider the allocation of Virtual Arrays (VAs)with different RAID levels in a Heterogeneous Disk Array (HDA). This is expected to provide significant cost savings by consolidating multiple disk arrays into one. Disk load balancing across disk arrays is a by-product of HDA. We provide an example of potential performance benefits expected from HDA. The number of Virtual Disks (VDs) required to materialize a VA is determined by upper bounds on VD bandwidth and space per physical disk drive. Only RAID1 and RAID5 are considered in this paper, but we develop the analysis for other RAID levels. A VA allocation is successful if disks sharing VAs with a failed disk are not overloaded in degraded mode. Single-pass data allocation methods for HDA are evaluated with a synthetic stream of allocation requests. Experimental results show that allocation methods minimizing the maximum disk bandwidth and capacity utilization or their variance across all disks yield the maximum number of allocated VAs. When the disk bandwidth is the bottleneck resource we utilize clustered RAID5 arrays to increase the number of allocated VAs.
Design, Fabrication and Characterization of an In Silico Cell Physiology lab for Bio Sensing Applications

In this paper, we report the design, fabrication and characterization of an In Silico cell physiology biochip for measuring Ca2+ ion concentrations and currents around single cells. This device has been designed around specific science objectives of measuring real time multidimensional calcium flux patterns around sixteen Ceratopteris richardii fern spores in microgravity flight experiments and ground studies. The sixteen microfluidic cell holding pores are 150 by 150 µm each and have 4 Ag/AgCl electrodes leading into them. An SU-8 structural layer is used for insulation and packaging purposes. The In Silico cell physiology lab is wire bonded on to a custom PCB for easy interface with a state of the art data acquisition system. The electrodes are coated with a Ca2+ ion selective membrane based on ETH-5234 ionophore and operated against an Ag/AgCl reference electrode. Initial characterization results have shown Nernst slopes of 30mv/decade that were stable over a number of measurement cycles. While this work is focused on technology to enable basic research on the Ceratopteris richardii spores, we anticipate that this type of cell physiology lab-on-a-chip will be broadly applied in biomedical and pharmacological research by making minor modifications to the electrode material and the measurement technique. Future applications include detection of glucose, hormones such as plant auxin, as well as multiple analyte detection on the same chip.
Extracting causation knowledge from natural language texts

SEKE is a semantic expectation‐based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 327–358, 2005.
Empirical Exercise Behavior of Warrant Holders and its Consequences for Warrant Values

In this paper, we analyze the exercise behavior of warrant holders and its impact on warrant values. For this purpose, we propose a parametric model to describing the exercise volume of warrants and calibrate it to exercise data of 40 warrants from the German market. We find that few too-early exercises but also a significant number of too-late exercises occur. This observed exercise behavior results in warrant values that are more than 3% below those under the optimal exercise strategy for at-the-money warrants and the differences are even much higher for in- and out-of-the-money warrants.
Rethinking Presidential Responsiveness: The Public Presidency and Rhetorical Congruency, 1953–2001

Several studies have examined the relationship between presidential action and public opinion; however, few explore a direct and continuous connection between presidential rhetoric and public opinion. To measure presidential rhetorical congruence with opinion, I construct a data set of matched opinion and policy statements from Presidents Eisenhower to Clinton. Confirming expectations (while contradicting others), I find no differences in congruent position taking between presidents who served earlier (Eisenhower to Ford) from those who served later (Carter to Clinton). Importantly, the election effect discovered in the president's first term is repeated in the second term in advance of midterm or presidential elections. Methods of public communication present mixed results; statements made on television are less likely to be congruent with public opinion in the first term (but more likely in the second term) while statements made in public speeches are positive for second-term presidents, both points sug.
Making the most out of life: Exploring the contribution of attention restorative theory in developing a non-pharmacological intervention for fatigue—ERRATUM

Objectives:This study investigates an approach based onKaplan’s Attention Restorative Theory (ART) to develop a non-pharmacological intervention to help individuals manage the distressing effects of illness related fatigue. The study aims to: identify activities perceived as being enjoyable by individuals who have moderate to severe fatigue related to advanced illness; determine the core attributes of potentially beneficially interventions; analyse reported ‘enjoyable’ experiences within the ART framework bymapping emergent themes to attributes of attention restoration; and develop the prototype for a self-management intervention tool. Methods: A purposive sample of 25 individuals who experienced moderate to severe fatigue was selected from the local hospice and community. Focused semi-structured interviews probed the questions: What do you enjoying doing? What is it about the activity that you particularly enjoy? Framework analysis was used to manage responses. Results: Seventy-five ‘enjoyable experiences’ were identified, including artistic pursuits, voluntary work, socialising and learning. These activities were organised into four conceptual themes: Belonging, Expansive, Nurturing and Purposeful. When mapped against attributes of restorative activities specified in ART, there was some congruence and variation. It was clear that the participants expressed a great need to be safe and in a nurturing environment. Some participants placed a high value in and received great joy from contributing to the community; this was not noted in previous ART literature. Significance of results: This study has extended Kaplan’s insightful work on restorative behaviours by revealing the value that purposeful, engaging and safe activities hold for people who live with fatigue. ART has inspired the research team to develop a self-management intervention tool to guide health care practitioners in promoting a non-pharmacological approach to manage fatigue through exploring, discovering and promoting experiences which engage, excite, nurture and challenge the person. Further research is needed to integrate this approach into clinical practice.
A MEASUREMENT STUDY OF A SENSOR / ACTUATOR NETWORK FOR METEOROLOGICAL OBSERVATIONS

In Distributed Collaborative Adaptive Sensing (DCAS) networks like the CASA testbeds [1] the data network itself is a crucial part of the overall system. Without timely transport of weather data from the radars to the control node and control data from this central node back to the radars an optimal operation of the DCAS network cannot be guaranteed. For example, if data from one radar arrives with a significant delay, the decision of how to steer the radars in the following heartbeat will not be optimal since the optimization component of the Meteorological Command and Control (MC&C) considers outdated information to determine the optimal scan set. Analyzing the data network performance of DCAS systems is important for two reasons. First, in many cases (like in the CASA IP1 testbed [2]) at least the first hop from the radars will be a wireless link due to the remote locations of the radars where wired data networks will often not be available. Due to the nature of these wireless links their capacity is not constant which is different from wired links. This variety in bandwidth capacity could lead to performance impairment on the application level. Second, weather can impair the quality of wireless links. This issue is especially crucial in the case of weather radar networks. In situations of weather in the radar networks its flawless performance is important. Therefore, it has to be investigated how weather impairs the quality of wireless links. This paper presents data and analysis of network measurements in in the IP1 testbed in Oklahoma during the 2008 Spring Experiment and the Western Massachusetts Off-the-Grid testbed [3]. Network measurement studies have been performed for a variety of network architectures and applications. Recently, such studies have also been performed for sensor networks([4],[5]). To the best of our knowledge, no such network measurement has been performed for an operational sensor/actuator network where closed-loop control of the sensors is essential. From a network point of view, the testbed we performed our measurement in is different from most sensor networks as follows. In this testbed high-bandwidth radar data are sent from the sensor nodes to the sink while very low-bandwidth control data are periodically transmitted from the sink to the nodes. The motivation for this measurement study is to perform a deeper analysis of the network performance of DCAS networks. On the application level, the operation of the IP1 testbed for two …
Location recommendation algorithm based on temporal and geographical similarity in location-based social networks

Because the existing location recommendation algorithms in Location-based Social Networks have the characteristic of high time complexity and low recommendation accuracy, a new location recommendation algorithm based on temporal and geographical similarity is proposed by improving the traditional location recommendation algorithm in this paper. New recommendation algorithm has innovation mainly in the following three aspects: At first, new algorithm changes the traditional processing method of time dimension, it divides 24 hours into some periods of time in accordance with the time law of people's work and life, so the user similarity calculated by such periods of time will be more accurate; Secondly, the DBSCAN algorithm is improved by introducing grid thought, which makes the clustering object is no longer a single check-in point, but a grid contained a lot of check-in points, this improves the speed of recommendation algorithm greatly; Finally, a new rating function of the potential points of interest which are never visited by the user is proposed. The experimental results show that the proposed approach can improve the speed and precision of recommendation system obviously.
Un protocole de consensus efficace fondé sur les conditions

The condition-basedapproach for consensus solvability (that we have introduce in a previous paper,ACM STOC’01) consists in identifying sets of input vectors for which it is possible to design a protocol solving the consensus problem for n processes despite the occurrence of up to f process crashes. For each value of f these conditions actually define a hierarchy. This paper con tinues our investigation of this approach. It has three main contributions. It first show that it is possible to define conditions from very simple weight functions. Interestingly any weight function defines an acceptable con dition, i.e., a condition that allows to solve the consensus problem. The second contribution is an efficient p rotocol whose wait-free part has a number of shared memory accesses upper bounded by O(n log2(f+1)) (actually, according to the vector and the number of actual crashes, this upper bound is rarely attained). A pr ocess that decides by itself, has to write the value it decides in shared memory in order to help decide processes that cannot decide by themselves. The third contribution of the paper is the study of conditions that all ow processes to save this write. When this occurs, the only write to shared memory by a process is the initial wri te of the value it proposes. Key-words: Asynchronous Distributed System, Condition, Consensus Pr oblem, Early Decision, FaultTolerance, Input Vector, Shared Memory.
Volumetric Semantic Segmentation using Pyramid Context Features Supplemental Material

Our “pyramid filtering” insight enables exact and extremely efficient per-voxel classification with minimal memory overhead. We will now demonstrate our improvement over existing techniques empirically and theoretically. For an empirical demonstration of efficiency, consider the following two alternatives to pyramid filtering: 1) the “sliding window” approach: iterate through every voxel in the input volume, and for each voxel construct and classify a feature vector. 2) we could use the fact that our feature and classifier are both linear, and could therefore be reduced to a single linear operation which amounts to filtering the volume with an extremely large filter — one with a support as large as the input volume. Such filtering is intractably expensive in the spatial domain, but is much more efficient in the Fourier domain: we can compute the FFT of a volume and a filter, do a component-wise multiplication in Fourier space, and then invert the FFT. In the case in which we have multiple feature channels, this can be sped up by summing the filter-responses in Fourier space and inverting the FFT only once. We will refer to this as the “FFT” approach. The “FFT” approach can be sped up by assuming that the Fourier-domain filters have been precomputed, which reduces computational demands but dramatically increases memory overhead. We will refer to this precomputed Fourier technique as “FFT+caching”. The sliding window approach is extremely common in object detection [5], and the FFT approach has also been explored previously [6]. And of course, there are many specialized ways to efficiently approximate these sliding-window type filtering operations [7, 8], but we will only consider general and exact techniques. In Figure 1 we compare our pyramid filtering technique against the three previously-described baselines. We see that only our technique performs well in terms of speed and memory overhead when n is large — which is crucial, as n ≈ 256 in our experiments. The sliding window technique has minimal memory overhead, but is intractably slow — Figure 1. Profiles of different methods for densely evaluating a linear classifier on a feature vector for every voxel in a volume (where we have 50 channels for the volume, as is common in our experiments). Here we show the speed (time taken to classify the entire volume) and memory overhead (memory required to classify the volume, not including the feature channels of the volume themselves) for different classification techniques and different size volumes. Only our pyramid filtering technique is both fast and memory-efficient when n is large (n ≈ 256 in our experiments). In fact, pyramid filtering is the only method which actually runs to completion when n is large – all other techniques run out of memory or never finish, which is why ours plots appear incomplete. Experiments were performed on a 2011 Macbook Pro.
Optimal DiffServ AC Design using Non-Linear Programming

Most DiffServ admission control (AC) algorithms rely on tuning parameters to help in the decision making. Tuning these parameters is a difficult task, especially when one considers the problem of assuring QoS guarantees to individual flows. This paper proposes a method for helping the design of DiffServ AC algorithms based on non-linear programming optimization. It enables to find the values for the AC parameters that permits to satisfy the QoS guarantees for individual VoIP flows, while minimizing a cost function that represents the performance goals of the service provider. This approach is used to compare the performance of some commonly used DiffServ AC techniques and also to design a novel AC algorithm based on queue estimates.