Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning High-Dimensional Concave Utility Functions for Discrete Choice Models

The discrete choice model explains and predicts the behavior of a consumer who chooses one item to purchase from among a set of alternatives. The consumer is assumed to assign a utility to each item based on its features, and to choose an item with probability proportional to its utility. We consider the high-dimensional setting where the number of item features is large. It is important in this setting to identify and remove features that are irrelevant to the utility function. Not only is feature selection important to skirt the curse of dimensionality for statistical estimation, it is of interest to understand the factors on which people base their spending decisions. We show that, when the utility function has the diminishing returns property and is thus concave, an additive approximation is safe, in that in the population setting it does not erroneously remove relevant features. We propose a two stage feature screening method based on this result, evaluate our method on both simulation data and a novel job/housing survey dataset, and show that it is practical and effective.
Effects of Twice-Daily Icodextrin Administration on Blood Pressure and Left Ventricular Mass in Patients on Continuous Ambulatory Peritoneal Dialysis

Background Hypervolemia is a risk factor for left ventricular hypertrophy and hypertension in peritoneal dialysis patients. Icodextrin improves volume control by increasing ultrafiltration in peritoneal dialysis patients. Aim To examine the effects of twice-daily icodextrin administration on blood pressure and left ventricular hypertrophy in peritoneal dialysis patients with hypervolemia and ultrafiltration failure. Method and Results Administration of icodextrin twice daily resulted in a significant reduction in the left ventricular mass index (LVMI) of patients by the end of the third month (p < 0.05). The reduction in LVMI was also significant for the once-daily icodextrin group (p < 0.05). Mean blood pressure of patients receiving icodextrin twice daily was significantly reduced (p < 0.05). By the end of the third month no significant changes were observed in mean blood pressure of the patients using once-daily icodextrin (p > 0.05). No statistically significant changes were observed in weekly total creatinine clearances or Kt/V of patients in either group at the end of 3 months (NS). Conclusion Twice-daily icodextrin administration was clinically beneficial as shown by reduced blood pressure and prevention of the progress of left ventricular hypertrophy without causing any decrease in dialysis adequacy or any side effects. The icodextrin metabolite results did not suggest any further increase in their values when comparing once- to twice-daily administration of icodextrin. Although prescription of icodextrin once daily may yield good clinical results in the long term, this study showed that it may be more efficient to use twice-daily icodextrin for at least a specific period for the purpose of obtaining quicker results in patients with ultrafiltration failure, serious hypervolemia, or hard-to-control blood pressure conditions.
Dynamic Intention-Aware Recommendation System

Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recommender systems. In this paper, we propose a dynamic intention-aware recommender system to better facilitate users to find desirable products and services. Compare to prior work, our proposal possesses the following advantages: (1) it takes user intentions and demands into account through intention mining techniques. By unearthing user intentions from the historical user-item interactions, and various user digital traces harvested from social media and Internet of Things, it is capable of delivering more satisfactory recommendations by leveraging rich online and offline user data; (2) it embraces the benefits of embedding heterogeneous source information and shared representations of multiple domains to provide accurate and effective recommendations comprehensively; (3) it recommends products or services proactively and timely by capturing the dynamic influences, which can significantly reduce user involvements and efforts.
Robust reversible watermarking scheme based on wavelet-like transform

Watermarking reversibility is one of the basic requirements for medical imaging, military imaging, and remote sensing applications. In these fields a slight change in the original image can lead to a significant difference in the final decision making process. However, the reversibility alone is not enough for practical applications because the hidden data must be extracted even after unintentional attacks (e.g., noise addition, JPEG compression) so a robust (i.e., semi-fragile) reversible watermarking methods became required. In this paper, we present a new robust reversible watermarking method that utilizes the Slantlet transform (SLT) to transform image blocks and modifying the SLT coefficients to embed the watermark bits. If the watermarked image is not attacked, the method is completely reversible (i.e., the watermark and the original image will be recovered correctly). After JPEG compression, the hidden data can be extracted without error. Experimental results prove that the presented scheme achieves high visual quality, complete reversibility, and better robustness in comparison with the previous methods.
Correlation between Cerebral Blood Volume Measurements by Perfusion-Weighted Magnetic Resonance Imaging and Two-Year Progression-Free Survival in Gliomas

Our goal was to determine whether relative cerebral blood volume (rCBV) can serve as an adjunct to histopathologic grading in the assessment of gliomas, with the hypothesis that rCBV can predict two-year survival. We evaluated 29 newly diagnosed gliomas (13 WHO grade II, seven grade III, nine grade TV; 17 astrocytomas, 12 oligodendroglial tumors). Dynamic susceptibility-weighted contrast-enhanced perfusion MR images and CBV maps were obtained. rCBVmax measurements (maximum tumor CBV/contralateral normal tissue CBV) and progression-free survival (PFS) were recorded. Receiver operating characteristic curves and Kaplan-Meier survival curves were calculated for rCBVmax and histologic grade. rCBVmax measurements differed between gliomas without (2.38 +/− 1.22) and with progression (5.57 +/− 2.84) over two years. The optimal rCBVmax cut-off value to predict progression was 2.95. rCBVmax < 2.95 was a significant predictor of two-year PFS, almost as accurate as WHO grade IT In the pure astrocytoma subgroup, the optimal rCBVmax cut-off value to predict progression was 2.85. In this group rCBVmax < 2.85 was a significant predictor of two-year PFS, an even better predictor of two-year PFS than WHO grade IT rCBVmax can be used to predict two-year PFS in patients with gliomas, independent of pathologic findings, especially in tumors without oligodendroglial components.
Racial and income disparities in childhood asthma in the United States.

OBJECTIVE To examine racial and income disparities in asthma prevalence in US children, and disparities in morbidity and ambulatory health care use among children with asthma. METHODS Using 1993-1996 National Health Interview Survey data, we measured asthma prevalence and morbidity in children aged 3 to 17 years (N = 14 211) stratifying by race and poverty status. Measures of morbidity included asthma-related activity limitation and number of bed days. We used the ratio of asthma-related doctor contacts to number of bed days in the past 2 weeks to measure health care use adjusted for severity of illness. RESULTS An annual average of 7.4% of children aged 3 to 17 years had asthma. There were no significant differences in asthma prevalence between race and poverty groups. In contrast, asthma-related morbidity was higher among black and poor children. Black poor children were most likely to have activity limitations due to asthma: 49% were limited compared with about 20% of black nonpoor, white poor, and white nonpoor children. Among children with activity limitations, black children and white poor children were more likely to have severe limitations, and white nonpoor children were least likely. Finally, white nonpoor children had the highest level of ambulatory care use for asthma after accounting for disease severity, and black poor children had the lowest level. CONCLUSIONS We found no significant racial or income disparities in asthma prevalence among children in the United States. However, black children and poor children are at higher risk for activity limitation, more severe activity limitation, and relative underuse of ambulatory health care. Black children living in poverty are at highest risk. Targeted interventions to reduce the burden of asthma morbidity in this population are likely to reduce disparities in asthma morbidity as well as reduce overall childhood asthma morbidity.
Fast Packet Classification Using Bit Compression with Fast Boolean Expansion

To support applications such as Internet security, virtual private networks, and Quality of Service (QoS), Internet routers need to quickly classify incoming packets into flows. Packet classification uses information contained in the packet header and a predefined rule table in the routers. In general, packet classification on multiple fields is a difficult problem. Hence, researchers have proposed a variety of classification algorithms. This paper presents a novel packet classification algorithm, the bit compression algorithm. As with the best-known classification algorithm, bitmap intersection, bit compression is based on the multiple dimensional range lookup approach. Since bit vectors of the bitmap intersection contain many ”0” bits, the bit vectors could be compressed. We compress the bit vectors by preserving only useful information and removing the redundant bits of the bit vectors. An additional index table would be created to keep track of the rule number associated with the remaining bits. Additionally, the wildcard rules enable an extensive improvement in the storage requirement. A novel Fast Boolean Expansion enables our scheme to obtain better classification speed even under a large number of wildcard rules. Compared to the bitmap intersection algorithm, the bit compression algorithm reduces the storage complexity in the average case from O(dN^2) (for bitmap intersection) to θ(dN•logN), where d denotes the number of dimensions and N represents the number of rules. The proposed scheme cuts the cost of packet classification engine and increases classification performance by accessing less memory, which is the performance bottleneck in the packet classification engine implementation using a network processor.
Proximity coherence for chip multiprocessors

Many-core architectures provide an efficient way of harnessing the increasing numbers of transistors available in modern fabrication processes. While they are similar to multi-node systems, they exhibit different communication latency and storage characteristics, providing new design opportunities that were previously not feasible. Traditional cache coherence protocols, although often used in many-core designs, have been developed in the context of multi-node systems. As such, they seldom take advantage of the new possibilities that many-core architectures offer. We propose Proximity Coherence, a scheme in which L1 load misses are optimistically forwarded to nearby caches via new dedicated links rather than always being indirected via a directory structure. Such an optimization is made possible by the comparable cost of local cache accesses with the use of on-chip network resources. Coherency is maintained using lightweight graph structures embedded in the L1 caches. We compare our Proximity Coherence protocol to an existing directory-based MESI protocol using full-system simulations of a 32 core system. Our extension lowers the latency of L1 cache load misses by up to 32% while reducing the bytes transferred on the global on-chip interconnect by up to 19% for a range of parallel benchmarks. Employing Proximity Coherence provides execution time improvements of up to 13%, reduces cache hierarchy energy consumption by up to 30% and delivers a more efficient solution to the challenge of coherence in chip multiprocessors.
Intralesional Cryotherapy for the Treatment of Keloid Scars: Evaluating Effectiveness

Background: Intralesional (IL) cryotherapy is a novel treatment technique for keloid scars, in which the scar is frozen from inside. Over the past decade, several studies have been published with varying outcomes. A critical analysis of the current literature is, therefore, warranted to determine whether IL cryotherapy is an alternative to established keloid scar treatments. Methods: A comprehensive review was performed, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. PubMed and EMBASE were searched from inception. Studies and level of recommendation were graded according to the American Society of Plastic Surgeons criteria. Results: Eight studies meeting the inclusion criteria were selected. The average scar volume decrease ranged from 51% to 63%, but no complete scar eradication was achieved on average. Scar recurrence ranged from 0% to 24%. Hypopigmentation posttreatment was seen mostly in Fitzpatrick 4–6 skin type patients. Finally, complaints of pain and pruritus decreased significantly in most studies. Conclusions: IL cryotherapy for the treatment of keloid scars shows favorable results in terms of volume reduction and alleviated complaints of pain and pruritus. However, no complete scar eradication is established, and recurrences are seen. Also, persistent hypopigmentation proved a problem in Fitzpatrick 4–6 skin type patients. Summarized, the evidence proved limited and inconsistent resulting in an American Society of Plastic Surgeons grade C recommendation for this type of treatment of keloid scars.
Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages

Producing machine translation (MT) for the many minority languages in the world is a serious challenge. Minority languages typically have few resources for building MT systems. For many minor languages there is little machine readable text, few knowledgeable linguists, and little money available for MT development. For these reasons, our research programs on minority language MT have focused on leveraging to the maximum extent two resources that are available for minority languages: linguistic structure and bilingual informants. All natural languages contain linguistic structure. And although the details of that linguistic structure vary from language to language, language universals such as context-free syntactic structure and the paradigmatic structure of inflectional morphology, allow us to learn the specific details of a minority language. Similarly, most minority languages possess speakers who are bilingual with the major language of the area. This paper discusses our efforts to utilize linguistic structure and the translation information that bilingual informants can provide in three sub-areas of our rapid development MT program: morphology induction, syntactic transfer rule learning, and refinement of imperfect learned rules.
Detecting Novel Compounds: The Role of Distributional Evidence

Research on the discovery of terms from corpora has focused on word sequences whose recurrent occurrence in a corpus is indicative of their terminological status, and has not addressed the issue of discovering terms when data is sparse. This becomes apparent in the case of noun compounding, which is extremely productive: more than half of the candidate compounds extracted from a corpus are attested only once. We show how evidence about established (i.e., frequent) compounds can be used to estimate features that can discriminate rare valid compounds from rare nonce terms in addition to a variety of linguistic features than can be easily gleaned from corpora without relying on parsed text.
Symbol formation.

Symbol formation is a term used to unify the view on the inter- dependencies in the research of the Hamburg University before 1933: the Philosophical Institute (William Stern, Ernst Cassirer), the Psychological Institute (Stern) with its laboratory (Heinz Werner) in cooperation with the later joining Umwelt Institut (Jakob von Uexküll). The term, definitely used by Cassirer and Werner, is associated with the personalistic approach: “Keine Gestalt ohne Gestalter” (Stern), but also covers related terms like “melody of motion” (Uexküll), and “relational content” (Cassirer), discussing the term “empirical scheme” (Kant). All this scientific interest addressed personal forces to structure thresholds in equivalent stimuli. This view on intermodal formation allowed research in common aspects in the environments of animals, of children and adults to meet there the symbol formation of artists (Weimar Bauhaus) and poets like R. M. Rilke, a friend of Uexküll.
A modified Coherence based method for dual microphone speech enhancement

In some telephony headsets the microphone is not close enough to the speaker's mouth due to size limitation. Thus the microphone captures background noise in addition to useful speech. There is a need to enhance the received signal in order to improve the voice quality in these systems. Among various enhancement methods, dual microphone methods are important for their low cost implementation and use of spatial properties. In this paper, we present a double microphone noise reduction method for nearby microphones. Our method is funded on a modified coherence based filter which reduces correlated noises using cross spectral subtraction. In our proposed method the amount of noise reduction is controlled in order to achieve an acceptable compromise between residual noise and speech distortion. The results evaluated using objective and subjective measures, confirm the superiority of the proposed approach.
Choosing a Local or Remote Cloud

Energy consumption in ICT as well as quality of service (QoS) are important considerations in the choice of computational resources. In Cloud Computing, users who work within an organisation will increasingly have access to their own cloud service (the "local cloud") and will also be able to access external cloud services. Choices between these options will be made based on security, cost, data and software protec- tion and resilience, but also in terms of technical choices regarding QoS and energy consumption. This paper addresses only the technical choice between a local or remote cloud service, and discusses how this choice can be formulated as an optimisation problem. After providing some experimental measurements regarding energy and performance of servers, we formulate the optimisation problem, describe its solution and present some numerical examples.
Effects of Arm Training with the Robotic Device ARMin I in Chronic Stroke: Three Single Cases

Background:Several clinical studies on chronic stroke conducted with end-effector-based robots showed improvement of the motor function in the affected arm. Compared to end-effector-based robots, exoskeleton robots provide improved guidance of the human limb and are better suited to train task-oriented movements with a large range of motions. Objective: To test whether intensive arm training with the arm exoskeleton ARMin I is feasible with chronic-stroke patients and whether it improves motor function in the paretic arm. Methods: Three single cases with chronic hemiparesis resulting from unilateral stroke (at least 14 months after stroke). A-B design with 2 weeks of multiple baseline measurements (A), 8 weeks of training (B) with repetitive measurements and a follow-up measurement 8 weeks after training. The training included shoulder and elbow movements with the robotic rehabilitation device ARMin I. Two subjects had three 1-hour sessions per week and 1 subject received five 1-hour sessions per week. The main outcome measurement was the upper-limb part of the Fugl-Meyer Assessment (FMA). Results:The ARMin training was well tolerated by the patients, and the FMA showed moderate, but significant improvements for all 3 subjects (p < 0.05). Most improvements were maintained 8 weeks after discharge. Conclusions: This study indicates that intensive training with an arm exoskeleton is feasible with chronic-stroke patients. Moderate improvements were found in all 3 subjects, thus further clinical investigations are justified.
Structure of the Yeast Cytochrome bc1 Complex with a Hydroxyquinone Anion Qo Site Inhibitor Bound*

Bifurcated electron transfer during ubiquinol oxidation is the key reaction of cytochrome bc1 complex catalysis. Binding of the competitive inhibitor 5-n-heptyl-6-hydroxy-4,7-dioxobenzothiazole to the Qo site of the cytochrome bc1 complex from Saccharomyces cerevisiae was analyzed by x-ray crystallography. This alkylhydroxydioxobenzothiazole is bound in its ionized form as evident from the crystal structure and confirmed by spectroscopic analysis, consistent with a measured pKa = 6.1 of the hydroxy group in detergent micelles. Stabilizing forces for the hydroxyquinone anion inhibitor include a polarized hydrogen bond to the iron-sulfur cluster ligand His181 and on-edge interactions via weak hydrogen bonds with cytochrome b residue Tyr279. The hydroxy group of the latter contributes to stabilization of the Rieske protein in the b-position by donating a hydrogen bond. The reported pH dependence of inhibition with lower efficacy at alkaline pH is attributed to the protonation state of His181 with a pKa of 7.5. Glu272, a proposed primary ligand and proton acceptor of ubiquinol, is not bound to the carbonyl group of the hydroxydioxobenzothiazole ring but is rotated out of the binding pocket toward the heme bL propionate A, to which it is hydrogen-bonded via a single water molecule. The observed hydrogen bonding pattern provides experimental evidence for the previously proposed proton exit pathway involving the heme propionate and a chain of water molecules. Binding of the alkyl-6-hydroxy-4,7-dioxobenzothiazole is discussed as resembling an intermediate step of ubiquinol oxidation, supporting a single occupancy model at the Qo site.
Static approximation of dynamically generated Web pages

Server-side programming is one of the key technologies that support today's WWW environment. It makes it possible to generate Web pages dynamically according to a user's request and to customize pages for each user. However, the flexibility obtained by server-side programming makes it much harder to guarantee validity and security of dynamically generated pages.To check statically the properties of Web pages generated dynamically by a server-side program, we develop a static program analysis that approximates the string output of a program with a context-free grammar. The approximation obtained by the analyzer can be used to check various properties of a server-side program and the pages it generates.To demonstrate the effectiveness of the analysis, we have implemented a string analyzer for the server-side scripting language PHP. The analyzer is successfully applied to publicly available PHP programs to detect cross-site scripting vulnerabilities and to validate pages they generate dynamically.