Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning Text for Advisory Dialogues

Explanation is an interactive process requiring a dialogue between advice-giver and advice-seeker. In this paper, we argue that in order to participate in a dialogue with its users, a generation system must be capable of reasoning about its own utterances and therefore must maintain a rich representation of the responses it produces. We present a text planner that constructs a detailed text plan, containing the intentional, attentional, and rhetorical structures of the text it generates.
Structural Reverse Engineering of Arithmetic Circuits

This paper focuses on detecting arithmetic components in large gate-level circuits using cut enumeration and structural analysis. It is based on the assumption that adder trees, the key part of arithmetic components, are represented using halfand full-adders, whose boundaries can be traced down to individual nodes present in the gatelevel circuit structure. The proposed method detects the elementary adders, then adder trees composed of these adders, and finally, arithmetic components. The detection leads to annotating nodes of the original gate-level circuit with exact boundaries of each component. The proposed method is useful in applications, such as design analysis, post-synthesis optimization, and formal verification.
Architecture Earthquake Disaster Simulation Based on Three-Dimension GIS Algorithm

GIS geographic information system algorithm are able to be used in the disaster initialization period, middle and rescue stage for the advanced two-dimensional simulation technology with its geographical model as well as providing us with accurate and quick geographical indicators, with the estimation of building distribution and mountain damaged condition, can bring us extremely high value in use. But the traditional GIS algorithm also has tremendous convenience, namely we can't form accurate analysis of the buildings and streets form three dimensional regions, we cannot tell the difference between topographic change quantities as well, resulting in certain circumstances that the algorithm is no practical. This article puts forward 3d and 2d embedded GIS algorithm according to the satellite remote sensing technology, which can be used to cope with buildings and streets for disaster situation simulation and analysis, through this algorithm we are able to obtain the original data before a disaster, disaster failure scene and the distribution of post-disaster construction. With verification, this algorithm has high practical value and can shorten the fitting time of data.
On default priors and approximate location models

A prior for statistical inference can be one of three basic types: a mathematical prior originally proposed in Bayes (Philos. Trans. R. Soc. Lond. 53 (1763) 370-418; 54 (1764) 269-325), a subjective prior presenting an opinion, or a truly objective prior based on an identified frequency ref- erence. In this note we consider a method for deriving a mathematical prior based on approximate location models. This produces a mathematical poste- rior, and any practical interpretation of such a posterior is in terms of exact or approximate confidence under the postulated model. We describe how a pro- posed prior can be simply checked for consistency with confidence methods, using expansions about the maximum likelihood estimator.
Pairwise Boosted Audio Fingerprint

A novel binary audio fingerprint obtained by filtering and then quantizing the spectral centroids is proposed. A feature selection algorithm, coined pairwise boosting (PB), is used to determine the filters and quantizers by casting the fingerprinting problem of identifying a query audio clip into a binary classification problem. The PB algorithm selects the filters and quantizers which lead to accurate classification of matching and nonmatching audio pairs: a matching pair is an audio pair that should be classified as being identical, and a nonmatching pair is a pair that should be classified as being different. By iteratively reducing the classification error of both matching and nonmatching pairs, the PB algorithm improves both the robustness and discriminating ability. In our experiments, the proposed fingerprint outperformed previously reported binary fingerprints in terms of robustness and discriminating ability. In the experiment, we compared the performances of a number of distance measures.
Computationally bounded retrieval

The increase in size of large image databases makes the problem of efficient retrieval extremely challenging. This is especially true in the case of high dimensional data where even operations like hashing become expensive because of costly projection operators. Unlike most hashing methods that sacrifice accuracy for speed, we propose a novel method that improves the speed of high dimensional image retrieval by several orders of magnitude without any significant drop in performance. To do this, we propose to learn computationally bounded sparse projections for the encoding step. To further increase the accuracy of the method, we add an orthogonality constraint on projections to reduce bit correlation. We then introduce an iterative scheme that jointly optimizes this objective, which helps us obtain fast and efficient projections. We demonstrate this technique on large retrieval databases, specifically ImageNET, GIST1M and SUN-attribute for the task of nearest neighbor retrieval, and show that our method achieves a speed-up of up to a factor of 100 over state-of-the-art methods, while having on-par and in some cases even better accuracy.
On the Efficient Minimization of Classification Calibrated Surrogates

Bartlett et al (2006) recently proved that a ground condition for convex surrogates, classification calibration, ties up the minimization of the surrogates and classification risks, and left as an important problem the algorithmic questions about the minimization of these surrogates. In this paper, we propose an algorithm which provably minimizes any classification calibrated surrogate strictly convex and differentiable — a set whose losses span the exponential, logistic and squared losses —, with boosting-type guaranteed convergence rates under a weak learning assumption. A particular subclass of these surrogates, that we call balanced convex surrogates, has a key rationale that ties it to maximum likelihood estimation, zero-sum games and the set of losses that satisfy some of the most common requirements for losses in supervised learning. We report experiments on more than 50 readily available domains of 11 flavors of the algorithm, that shed light on new surrogates, and the potential of data dependent strategies to tune surrogates.
Effects of light intensity on the phototoxicity of fluoranthene to a benthic macroinvertebrate.

Conceptual models suggest that the toxicity of photoactivated polycyclic aromatic hydrocarbons (PAHs) should be a direct function both of chemical (PAH) dose and intensity of the ultraviolet (UV) light to which the organism is exposed. However, there have been only limited studies with aquatic organisms to quantify the relationship between PAH dose and UV intensity in producing phototoxicity. In this study, oligochaetes (Lumbriculus variegatus) were exposed, via the water, to multiple concentrations of fluoranthene, a PAH known to be phototoxic, and then placed under UV light at three different intensities. The resultant phototoxicity clearly was a function both of PAH dose and light intensity. Time-dependent mortality of the oligochaetes could be accurately predicted through evaluation of the product of fluoranthene dose (in the tissue of the animal) and light intensity to which the organisms were exposed. These results indicate that criteria for phototoxic chemicals should incorporate consideration not only of xenobiotic exposure but also of light intensity in specific aquatic environments.
Analyzing analytics

Many organizations today are faced with the challenge of processing and distilling information from huge and growing collections of data. Such organizations are increasingly deploying sophisticated mathematical algorithms to model the behavior of their business processes to discover correlations in the data, to predict trends and ultimately drive decisions to optimize their operations. These techniques, are known collectively as analytics, and draw upon multiple disciplines, including statistics, quantitative analysis, data mining, and machine learning. In this survey paper, we identify some of the key techniques employed in analytics both to serve as an introduction for the non-specialist and to explore the opportunity for greater optimizations for parallelization and acceleration using commodity and specialized multi-core processors. We are interested in isolating and documenting repeated patterns in analytical algorithms, data structures and data types, and in understanding howthese could be most effectively mapped onto parallel infrastructure. To this end, we focus on analytical models that can be executed using different algorithms. For most major model types, we study implementations of key algorithms to determine common computational and runtime patterns. We then use this information to characterize and recommend suitable parallelization strategies for these algorithms, specifically when used in data management workloads.
Two Principles of Parse Preference

The DIALOGIC system for syntactic analysis and semantic translation has been under development for over ten years, and during that time it has been used in a number of domains in both database interface and message-processing applications. In addition, it has been tested on a number of sentences of linguistic interest. Built into the system are facilities for ranking parses according to syntactic and selectional considerations, and over the years, as various kinds of ambiguity have become apparent, heuristics have been devised for choosing the preferred parses. Our aim in this paper is first to present a compendium of many of these heuristics and second to propose two principles that seem to underlie the heuristics. The first will be useful to researchers engaged in building grammars of similarly broad coverage. The second is of psychological interest and may be a guide for estimating parse preferences for newly discovered ambiguities for which we lack the experience to decide among on a more empirical basis.
Multi-RQP: generating test databases for the functional testing of OLTP applications

OLTP applications usually implement use cases which execute a sequence of actions whereas each action usually reads or updates only a small set of tuples in the database. In order to automatically test the correctness of the different execution paths of the use cases implemented by an OLTP application, a set of test cases and test databases needs to be created. In this paper, we suggest that a tester specifies a test database individually for each test case using SQL as a declarative test database specification language. Moreover, we also discuss the design of a database generator which creates a test database based on such a specification. Consequently, our approach allows to generate a tailor-made test database for each test case and to bundle them together for the test case execution phase.
Cognitive behavioral therapy for posttraumatic stress disorder in women: a randomized controlled trial.

CONTEXT The prevalence of posttraumatic stress disorder (PTSD) is elevated among women who have served in the military, but no prior study has evaluated treatment for PTSD in this population. Prior research suggests that cognitive behavioral therapy is a particularly effective treatment for PTSD. OBJECTIVE To compare prolonged exposure, a type of cognitive behavioral therapy, with present-centered therapy, a supportive intervention, for the treatment of PTSD. DESIGN, SETTING, AND PARTICIPANTS A randomized controlled trial of female veterans (n=277) and active-duty personnel (n=7) with PTSD recruited from 9 VA medical centers, 2 VA readjustment counseling centers, and 1 military hospital from August 2002 through October 2005. INTERVENTION Participants were randomly assigned to receive prolonged exposure (n = 141) or present-centered therapy (n = 143), delivered according to standard protocols in 10 weekly 90-minute sessions. MAIN OUTCOME MEASURES Posttraumatic stress disorder symptom severity was the primary outcome. Comorbid symptoms, functioning, and quality of life were secondary outcomes. Blinded assessors collected data before and after treatment and at 3- and 6-month follow-up. RESULTS Women who received prolonged exposure experienced greater reduction of PTSD symptoms relative to women who received present-centered therapy (effect size, 0.27; P = .03). The prolonged exposure group was more likely than the present-centered therapy group to no longer meet PTSD diagnostic criteria (41.0% vs 27.8%; odds ratio, 1.80; 95% confidence interval, 1.10-2.96; P = .01) and achieve total remission (15.2% vs 6.9%; odds ratio, 2.43; 95% confidence interval, 1.10-5.37; P = .01). Effects were consistent over time in longitudinal analyses, although in cross-sectional analyses most differences occurred immediately after treatment. CONCLUSIONS Prolonged exposure is an effective treatment for PTSD in female veterans and active-duty military personnel. It is feasible to implement prolonged exposure across a range of clinical settings. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00032617.
Performance of TiCN and TiAlN tools in machining hardened steel under dry, wet and minimum fluid application

In this work, an experimental study has been carried out on the comparative performance of TiCN and TiAlN coated tools in machining of AISI 4340 hardened steel under dry, wet and minimum fluid application conditions and the analysis of results are presented. Cutting force, cutting temperature, surface finish and flank wear are compared in all the cases to understand the performance. Then, the relative influences of the machining and the minimum fluid application parameters on the machining performance of tools have been analysed and presented in this paper.
Relational Implementation of Multi-dimensional Indexes for Time Series

Similarity search over time-series data is a useful, but expensive, application. Sequence data can be transformed via an orthonormal transformation, which is then dimensionally reduced, to be indexed. Rtrees and R*-trees have been used for this purpose. These do not scale well, however, for very large datasets. We propose a new indexing data-structure within relational tables, indexed with B+-tree indexes. We employ SQL in the retrieval process, and achieve sequential, instead of random I/O, access. We perform comprehensive experiments to test and compare our proposed technique against existing solutions, and demonstrate it to be scalable and more efficient for very large datasets.
Cancer Therapy : Clinical Phase I Study of the Combination of Sorafenib and Temsirolimus in Patients with Metastatic Melanoma

Purpose: This phase I clinical trial was conducted to determine the safety, efficacy, andmolecular effects of sorafenib with temsirolimus in patients with advanced melanoma. Patients andMethods:Patientswith stage IVor unresectable or recurrent stage IIImelanoma andEastern Cooperative Oncology Group performance status of 0 to 1 were eligible. Sorafenib was given orally once or twice daily and temsirolimus was given i.v. weekly, both starting on day 1, with a 4-week cycle. Responses were assessed every 2 cycles per Response Evaluation Criteria in Solid Tumors criteria. Consenting patients with accessible tumors underwent optional tumorbiopsies before treatment and after the second infusionof temsirolimus. Tumorbiopsieswere analyzed for activatingmutations inBRAF andNRAS, and for expression of P-extracellular signal-regulated kinase (P-ERK) and P-S6 proteins. Results: A total of 25 patients were accrued to the study. The maximum tolerated doses were sorafenib 400 mg every morning and 200 mg every evening and temsirolimus 25 mg i.v. weekly. Dose-limiting toxicities included thrombocytopenia, hand-foot syndrome, serum transaminase elevation, and hypertriglyceridemia. There were no complete or partial responses with the combination; 10 patients achieved stabilization of disease as their best response. The median progression-free survival was 2.1 months. Matching pretreatment and day 15 tumor biopsies showedmarked inhibition of P-S6with treatment in 3 of 4 evaluable patients, but minimal inhibition of P-ERK. Conclusions: Combination therapy with sorafenib and temsirolimus resulted in significant toxicity at higher dose levels, failed to achieve any clinical responses in genetically unselected patient population, and did not inhibit P-ERK. Clin Cancer Res; 18(4); 1120–28. 2012 AACR.
OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework

Progress in experimental tools and design is allowing the acquisition of increasingly large datasets. Storage, manipulation and efficient analyses of such large amounts of data is now a primary issue. We present OpenElectrophy, an electrophysiological data- and analysis-sharing framework developed to fill this niche. It stores all experiment data and meta-data in a single central MySQL database, and provides a graphic user interface to visualize and explore the data, and a library of functions for user analysis scripting in Python. It implements multiple spike-sorting methods, and oscillation detection based on the ridge extraction methods due to Roux et al. (2007). OpenElectrophy is open source and is freely available for download at http://neuralensemble.org/trac/OpenElectrophy.
Lower trees with fixed degrees: A recipe for efficient secure hierarchical aggregation in WSNs

We propose two strategies to reduce energy consumption for secure data aggregation in wireless sensor networks. While the purpose of data aggregation is to reduce energy consumption, secure data aggregation introduces an overhead making the total energy consumption comparable to naive secure non-aggregation. Another aspect is that the secure data aggregation has higher node congestion than non-aggregation schemes for networks of sizes up to about 100 nodes. Considering a secure data aggregation protocol, our strategies are to reduce the height of the aggregation tree and limit its node degree. The former results in fewer verification messages and a lower total communication load, and the latter reduces node congestion. The most heavily burdened node sends a third of the messages required in the original protocol, which for small networks is lower than with non-aggregation, and the total communication load is lowered compared to non-aggregation.
A run-time, feedback-based energy estimation model For embedded devices

We present an adaptive, feedback-based, energy estimation model for battery-powered embedded devices such as sensor network gate-ways and hand-held computers. Our technique maps hardware and software counters to energy consumption values using a set of first order, linear regression equations. Our system is novel in that it combines online and offline techniques to enable runtime power prediction. Our system employs an offline instantiated model that it continuously updates using feedback from a readily available battery monitor within the device. We empirically evaluate our model and detail its robustness, accuracy, and computational cost. We also analyze the stability of the model in the presence of feedback errors. We demonstrate that our approach can achieve an error rate of 1% (extant techniques: 2.6% to 4%) for computationally bound tasks and 6.6% (extant techniques: 11%) for communication bound tasks.
Protection Against Lethal Marburg Virus Infection Mediated by Lipid Encapsulated Small Interfering RNA

Background. Marburg virus (MARV) infection causes severe morbidity and mortality in humans and nonhuman primates. Currently, there are no licensed therapeutics available for treating MARV infection. Here, we present the in vitro development and in vivo evaluation of lipid-encapsulated small interfering RNA (siRNA) as a potential therapeutic for the treatment of MARV infection. Methods. The activity of anti-MARV siRNAs was assessed using dual luciferase reporter assays followed by in vitro testing against live virus. Lead candidates were tested in lethal guinea pig models of 3 different MARV strains (Angola, Ci67, Ravn). Results. Treatment resulted in 60%–100% survival of guinea pigs infected with MARV. Although treatment with siRNA targeting other MARV messenger RNA (mRNA) had a beneficial effect, targeting the MARV NP mRNA resulted in the highest survival rates. NP-718m siRNA in lipid nanoparticles provided 100% protection against MARV strains Angola and Ci67, and 60% against Ravn. A cocktail containing NP-718m and NP-143m provided 100% protection against MARV Ravn. Conclusions. These data show protective efficacy against the most pathogenic Angola strain of MARV. Further development of the lipid nanoparticle technology has the potential to yield effective treatments for MARV infection.
Adiabatic quantum computation is equivalent to standard quantum computation

The model of adiabatic quantum computation has recently attracted attention in the physics and computer science communities, but its exact computational power has been unknown. We settle this question and describe an efficient adiabatic simulation of any given quantum algorithm. This implies that the adiabatic computation model and the standard quantum circuit model are polynomially equivalent. We also describe an extension of this result with implications to physical implementations of adiabatic computation. We believe that our result highlights the potential importance of the adiabatic computation model in the design of quantum algorithms and in their experimental realization.
Epistemic Landscapes, Optimal Search, and the Division of Cognitive Labor

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor” (this journal, 2009), that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments.
Preparation and Characterization of UV-Cured Hybrid Polyvinyl Alcohol Nanofiber Membranes by Electrospinning

/N=N nanofiber membranes were synthesized. These membranes were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric-differential thermal analysis, and Brunauer–Emmett–Teller analysis. According to results, fine polymeric nanofibers were obtained in the size range of 100-200 nm for pure PVA nanofibers and 150-250 nm for PVA/SiO