Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Type-I Hybrid-ARQ Scheme in a TDMA System over a Non-Stationary Channel

In this paper, we investigate a queueing performance of an adaptive Type-I hybrid-Automatic Repeat reQuest(ARQ) scheme in a TDMA System over a non-stationary channel. In the system, we assume that an arriving message of variable size over a medium access control(MAC) layer consists of a number of unit information of fixed size. It is fragmented in MAC layer according to the payload size of ARQ block of fixed size and transmitted. The payload size of ARQ block varies in accordance with each channel state in order to increase the packet transmission success probability. The system of interest is modelled as a bulk arrivals and modulated bulk service in an infinite buffer and solved by a generating function approach. From the queueing analysis, we obtain the message delay, which is its queueing delay plus the time to transmit the entire message. From the numerical examples, the queueing performance of a non-adaptive hybrid type-I and adaptive one is compared.
On the Summarization of Dynamically Introduced Information: Online Discussions and Blogs

In this paper we describe computational approaches to summarizing dynamically introduced information: online discussions and blogs, and their evaluations. Research in the past has been mainly focused on text-based summarization where the input data is predominantly newswire data. When branching into these newly emerged data types, we face number of difficulties that are discussed here.
A versatile HDR video production system

Although High Dynamic Range (HDR) imaging has been the subject of significant research over the past fifteen years, the goal of acquiring cinema-quality HDR images of fast-moving scenes using available components has not yet been achieved. In this work, we present an optical architecture for HDR imaging that allows simultaneous capture of high, medium, and low-exposure images on three sensors at high fidelity with efficient use of the available light. We also present an HDR merging algorithm to complement this architecture, which avoids undesired artifacts when there is a large exposure difference between the images. We implemented a prototype high-definition HDR-video system and we present still frames from the acquired HDR video, tonemapped with various techniques.
MONTE CARLO EVALUATION OF AMERICAN OPTIONS USING CONSUMPTION PROCESSES

We develop a new approach for pricing both continuous-time and discrete-time American options which is based on the fact that any American option is equivalent to a European one with a consumption process involved. This approach admits the construction of an upper bound (a lower bound) on the true price using some lower bound (an upper bound) by Monte Carlo simulation. A number of effective estimators of upper and lower bounds with the reduced variance are proposed. The method is supported by numerical experiments which look promising.
Extending the Extensional Lambda Calculus with Surjective Pairing is Conservative

We answer Klop and de Vrijer's question whether adding surjective-pairing axioms to the extensional lambda calculus yields a conservative extension. The answer is positive. As a byproduct we obtain the first ``syntactic'' proof that the extensional lambda calculus with surjective pairing is consistent.
Automatic Generation of Object Usage Specifications from Large Method Traces

Formal specifications are used to identify programming errors, verify the correctness of programs, and as documentation. Unfortunately, producing them is error-prone and time-consuming, so they are rarely used in practice. Inferring specifications from a running application is a promising solution. However, to be practical, such an approach requires special techniques to treat large amounts of runtime data. We present a scalable dynamic analysis that infers specifications of correct method call sequences on multiple related objects. It preprocesses method traces to identify small sets of related objects and method calls which can be analyzed separately. We implemented our approach and applied the analysis to eleven real-world applications and more than 240 million runtime events. The experiments show the scalability of our approach. Moreover, the generated specifications describe correct and typical behavior, and match existing API usage documentation.
Natural variation in plasticity of glucose homeostasis and food intake

SUMMARY Balancing the acquisition, allocation and storage of energy during periods of food deprivation is critical for survival. We show that natural variation in the foraging (for) gene, which encodes a cGMP-dependent protein kinase (PKG) in the fruit fly Drosophila melanogaster, affects behavioral and physiological responses to short-term food deprivation. Rover and sitter, natural allelic variants of for, differ in their stored carbohydrate reserves as well as their response to short-term deprivation. Fewer carbohydrates are stored in the fat body of rovers compared with sitters, and more labeled glucose is allocated to lipid stores compared with carbohydrate stores during a short feeding bout. Short-term food deprivation decreases hemolymph glucose levels in rovers but not in sitters. After food deprivation, rovers increase their food intake more slowly than sitters, and rover hemolymph levels take longer to respond to re-feeding. Finally, rovers have lower adipokinetic hormone (akh) mRNA levels than sitters. Our data suggest that for mediates larval responses to short-term food deprivation by altering food intake and blood glucose levels.
Inflammation drives wound hyperpigmentation in zebrafish by recruiting pigment cells to sites of tissue damage

SUMMARY In humans, skin is the largest organ and serves as a barrier between our body and the outside world. Skin protects our internal organs from external pathogens and other contaminants, and melanocytes within the skin protect the body from damage by ultraviolet light. These same pigment cells also determine our skin colour and complexion. Skin wounding triggers a repair response that includes a robust recruitment of inflammatory cells, which function to kill invading microbes and clear away cell and matrix debris. Once at the wound site, these innate immune cells release a barrage of cytokines that direct the activities of other cells during the repair process. Tissue damage and repair also frequently lead to alterations in skin pigmentation, in particular to wound hyperpigmentation. In this study, we describe a model of wound hyperpigmentation in the translucent zebrafish larva, where we can live-image the recruitment of melanocytes and their precursors, melanoblasts, to the wound site. We show that these pigment cells are drawn in after the initial recruitment of innate immune cells and that the inflammatory response is essential for wound hyperpigmentation. This new model will allow us to uncover the molecular link between immune and pigment cells during tissue repair and to screen for potential therapeutics to dampen wound hyperpigmentation.
Generating revocable fingerprint template using minutiae pair representation

With the growing concern for secure and private information protection, several issues pertaining to the traditional biometric authentication system such as the reissuance of biometric template and template security have been raised. To resolve these limitations, many new concepts of revocable biometrics have been introduced. However, the design of a template protection scheme that fulfils all the security and privacy criteria, namely revocability, non-invertibility, and anonymity, is still a challenging task. In this paper, we present a secure fingerprint template protection scheme by using minutiae pair representation that extends the concept of revocable biometrics. The proposed method integrates template with its discriminative helper data to achieve acceptable equal error rate (EER) of near 0%. Apart from performance, the security and privacy issues such as revocability and non-invertibility are also discussed.
Significant Themes in 19 th-Century Literature

External factors such as author gender, author nationality, and date of publication affect both the choice of literary themes in novels and the expression of those themes, but the extent of this association is difficult to quantify. In this work, we apply statistical methods to identify and extract hundreds of "topics" from a corpus of 3,346 works of 19th-century British, Irish, and American fiction. We use these topics as a measurable, data-driven proxy for literary themes. External factors may predict fluctuations in the use of themes and the individual word choices within themes. We use topics to measure the evidence for these associations and whether that evidence is statistically significant.1
The impact of local heterogeneity on alternative control strategies for foot-and-mouth disease

The 2001 epidemic of foot–and–mouth disease (FMD) in the UK resulted in the death of nearly 10 million livestock at a cost that was estimated to be up to £8 billion. Owing to the controversy surrounding the epidemic, the question of whether or not alternative policies would have resulted in significantly better control of the epidemic remains of great interest. A hexagonal lattice simulation of FMD in Cumbria is used to address the central question of whether or not better use could have been made of expert knowledge of FMD transmission to target pre–emptive culling, by assuming that the premises at greatest risk of becoming infected can be targeted for culling. The 2000 UK census and the epidemiological database collected during the epidemic are used to describe key characteristics of disease transmission, and the model is fit to the epidemic time–series. Under the assumptions of the model, the parameters that best fit the epidemic in Cumbria indicate that a policy based on expert knowledge would have exacerbated the epidemic compared with the policy as implemented. However, targeting more distant, high–risk farms could be more valuable under different epidemic conditions, notably, if risk factors of sufficient magnitude could be identified to aid in prioritizing vaccination or culling of farms at high risk of becoming infected.
Alert Correlation Using a Novel Clustering Approach

Since the birth of Intrusion Detection System (IDS) technology, the most significant implementation problem is the enormous number of alerts generated by the IDS sensors. Moreover due to this obtrusive predicament, two other problems have emerged which are the difficulty in processing the alerts accurately and also the decrease in performance rate in terms of time and memory capacity while processing these alerts. Thus, based on the specified problems, the purpose of our overall research is to construct a holistic solution that is able to reduce the number of alerts to be processed and at the same time to produce a high quality attack scenarios that are meaningful to the administrators in a timely manner. However for the purpose of this paper we will present our proposed clustering method, architectured solely with the intention of reducing the amount of alerts generated by IDS. The clustering method was tested against a live data from a cyber attack monitoring unit that uses SNORT engine to capture the alerts. The result obtained from the experiment is very promising, the clustering algorithm was able to reduce about 86.9% of the alerts used in the experiment. From the result we are able to highlight the contribution to practitioners in an actual working environment.
Convergence Time of Quantized Metropolis Consensus Over Time-Varying Networks

We consider the quantized consensus problem on undirected time-varying connected graphs with n nodes, and devise a protocol with fast convergence time to the set of consensus points. Specifically, we show that when the edges of each network in a sequence of connected time-varying networks are activated based on Poisson processes with Metropolis rates, the expected convergence time to the set of consensus points is at most O(n2 log2 n), where each node performs a constant number of updates per unit time.
Recessive germline SDHA and SDHB mutations causing leukodystrophy and isolated mitochondrial complex II deficiency

Background Isolated complex II deficiency is a rare form of mitochondrial disease, accounting for approximately 2% of all respiratory chain deficiency diagnoses. The succinate dehydrogenase (SDH) genes (SDHA, SDHB, SDHC and SDHD) are autosomally-encoded and transcribe the conjugated heterotetramers of complex II via the action of two known assembly factors (SDHAF1 and SDHAF2). Only a handful of reports describe inherited SDH gene defects as a cause of paediatric mitochondrial disease, involving either SDHA (Leigh syndrome, cardiomyopathy) or SDHAF1 (infantile leukoencephalopathy). However, all four SDH genes, together with SDHAF2, have known tumour suppressor functions, with numerous germline and somatic mutations reported in association with hereditary cancer syndromes, including paraganglioma and pheochromocytoma. Methods and results Here, we report the clinical and molecular investigations of two patients with histochemical and biochemical evidence of a severe, isolated complex II deficiency due to novel SDH gene mutations; the first patient presented with cardiomyopathy and leukodystrophy due to compound heterozygous p.Thr508Ile and p.Ser509Leu SDHA mutations, while the second patient presented with hypotonia and leukodystrophy with elevated brain succinate demonstrated by MR spectroscopy due to a novel, homozygous p.Asp48Val SDHB mutation. Western blotting and BN-PAGE studies confirmed decreased steady-state levels of the relevant SDH subunits and impairment of complex II assembly. Evidence from yeast complementation studies provided additional support for pathogenicity of the SDHB mutation. Conclusions Our report represents the first example of SDHB mutation as a cause of inherited mitochondrial respiratory chain disease and extends the SDHA mutation spectrum in patients with isolated complex II deficiency.
A pr 2 00 0 Topology from the Simulated Sloan Digital Sky Survey

We measure the topology (genus curve) of the galaxy distribution in a very large cosmological simulation designed to resemble closely results from the upcoming Sloan Digital Sky Survey (SDSS). The mock survey is based on a large N-body simulation that uses 54,872,000 particles in a periodic cube 600h−1Mpc on a side. The adopted inflationary cold dark matter (CDM) model has parameters ΩCDM = 0.4, ΩΛ = 0.6, h = 0.6, b = 1.3. We “observe” this simulation to produce a simulated redshift catalog of ∼ 106 galaxies over π steradians, mimicking the ancitipated spectroscopic selection procedures of the SDSS in some detail. Sky maps, redshift slices, and 3-D contour maps of the mock survey reveal a rich and complex structure, including networks of voids and superclusters that resemble the patterns seen in the CfA redshift survey and the Las Campanas Redshift Survey (LCRS). The 3-D genus curve can be measured from the simulated catalog with superb precision; this curve not only has the general shape predicted for Gaussian, random phase initial conditions, but the error bars are small enough to demonstrate with high significance the subtle departures from this shape caused by non-linear gravitational evolution on a 10h−1Mpc smoothing scale (where σgal = 0.4). These distortions have the form predicted by Matsubara’s (1994) perturbative analysis, but they are smaller in amplitude. We also measure the 3-D genus curve of the radial peculiar velocity field measured by applying distance-indicator relations (with realistic errors) to the mock catalog. The genus curve is consistent with the Gaussian random phase prediction, though it is of relatively low precision because of the large smoothing length required to overcome noise in the measured velocity field. Finally, we measure the 2-D topology in redshift slices, similar to those which will become available early in the course of the SDSS and to those already observed in the LCRS. The genus curves of these slices are consistent with the observed genus curves of the LCRS, suggesting that our inflationary CDM model with ΩCDM ∼ 0.4 is a good choice. Our mock redshift catalog is publicly available for the use of other researchers. Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge MA 02138, wcolley@cfa.harvard. edu Princeton University Department of Astrophysical Sciences, Princeton, NJ 08544, jrg@astro.princeton. edu Department of Astronomy, The Ohio State University, 174 W 18th Ave., Columbus, OH 43210-1106, dhw,aberlind@astronomy.ohio-state.edu Department of Astronomy, Seoul National University, Seoul 151, Korea, cbp@astro.snu. ac.kr
Sparse PDF maps for non-linear multi-resolution image operations

We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.
Memory in Mind and Culture: Historians and Sites of Memory

Historians are interested in sites of memory, understood as places where groups of people engage in public activity through which they express “a collective shared knowledge … of the past, on which a group's sense of unity and individuality is based” (Assmann, 1995). The group that goes to such sites inherits earlier meanings attached to the event, as well as adding new meanings. Their activity is crucial to the presentation and preservation of commemorative sites. When such groups disperse or disappear, sites of memory lose their initial force, and may fade away entirely. Thus, historians are more interested in remembrance as a cultural practice than in memory as an individual's capacity to recall or reconfigure the past. The term, sites of memory, abumbrated in a seven-volume study edited by Pierre Nora (n.d.) has been extended to many different texts, from legends, to stories, to concepts. In this brief essay, I define the term more narrowly to mean physical sites where commemorative acts take place. In the twentieth century, most such sites marked the loss of life in war. It is these sites that have attracted the attention of entire battalions of historians in the past twenty-five years. What makes such sites of memory attractive for historical research is their character as topoi with a life history. They have an initial, creative phase, when they are constructed or adapted to particular commemorative purposes. Then follows a period of institutionalization and routinization of their use.
Structure maps based pedestrian detection

Pedestrian detection is widely needed in many applications, such as people counting, surveillance and scene understanding. In this paper, we propose a robust stereo vision based approach using novel structure maps of static scene, which can be generated from the disparity image and represents the geometry profiles of 3D objects in pixels. Both of the background subtraction and pedestrian detection are taken on the maps only, this decreases the computational cost greatly compared with general reprojection based methods. Due to the maps retain the shape of pedestrian in color image, our detection can precisely locate the head of people and robust to any view angles. The experimental results show that the approach works well in challenging conditions, and can be used in a real time system for various applications.
First ideas on the use of affective cues in an empathic computer-based companion

This paper describes a system to enhance the interaction between humans and virtual characters with emotional mimicry and role-taking. Such system increases the believability of virtual agents. Mimicking necessitates a model of emotional intelligence to understand and display user’s emotions. A more complex processing is however necessary for a reactive behavior, where the virtual agent reacts e.g. in an encouraging way which allows to actively change user’s current state. A virtual character with highly expressive capabilities was created to create a platform to figure out the differences in mimicking and role-taking. As we will concentrate on nonverbal behavior input from users, our agent will not be able to understand what, but how users are speaking.
Persistence to Graduation for Students With Disabilities: Implications for Performance-Based Outcomes

The study sought to determine whether students with disabilities are disadvantaged because of state and institutional performance-based policies providing incentives for 4-year graduation. In a longitudinal study of 32,187 students at a Midwestern Research University, the retention and graduation rates, and mean years to graduation, of students with disabilities were compared with students without disabilities. This study demonstrated that the presence of a disability does not negatively influence eventual graduation, but that it does influence the amount of time to degree completion. However, as the transition to college for students with disabilities can be more difficult, it is important that the institution has interventions in place to assist students with disabilities to assimilate into college. The discussion focuses on policy and practice implications linked to performance-based outcomes related to students with disabilities.
Learning dictionaries for local sparse coding in image classification

Low dimensional embedding of data samples lying on a manifold can be performed using locally linear modeling. By incorporating suitable locality constraints, sparse coding can be adapted to modeling local regions of a manifold. This has been coupled with the spatial pyramid matching algorithm to achieve state-of-the-art performance in object recognition. In this paper, we propose an algorithm to learn dictionaries for computing local sparse codes of descriptors extracted from image patches. The algorithm iterates between a local sparse coding step and an update step that searches for a better dictionary. Evaluation of the local sparse code for a data sample is simplified by first estimating its neighbors using the proposed distance metric and then computing the minimum ℓ1 solution using only the neighbors. The proposed dictionary update ensures that the neighborhood of a training sample is not changed from one iteration to the next. Simulation results demonstrate that the sparse codes computed using the proposed dictionary achieve improved classification accuracies when compared to using a K-means dictionary with standard image datasets.