Miracle-dz/UniFAR-FTUs huggingface dataset:
Trip Fare Estimation Study from Taxi Routing Behaviors and Localizing Traces

It is hard for people to estimate the trip fare withthe dynamic traffic conditions and routing changes in a bigcity. In this paper, a comprehensive trip fare estimation study isconducted, which is based on taxi routing behaviors and localizingtraces. First, we build a model to investigate taxi drivers' routing behaviors. Based on this model, we are able to analyze driverincome patterns. Second, we build a model to analyze trafficpatterns. Third, based on the above two models, we employ tuckermodel which is based on tenser decomposition to estimate taxifare of any route. To achieve better accuracy, we also introducesmoothing method into the model. Last, we test our method ona large-scale real-life data set.
Reachback capacity with non-interfering nodes

We consider a multiaccess communication problem, in which multiple sources want to send correlated data to a far receiver, over a multiple access channel that is rendered virtually collision free by a suitable MAC protocol. This problem arises, e.g., in the context of sensor networks, where sensors are deployed on a field, and the goal is to send back to a remote location the signals picked up by all nodes. In this paper, we give an exact characterization of the joint source/channel capacity region, i.e., we give conditions on the sources and the channels under which reliable communication is possible in this context. These conditions generalize in a very meaningful way the condition that (entropy of a source less than channel capacity) for point-to-point channels, and surprisingly, give a separation theorem for this problem as well—we know of no other network information theory problem for which source and channel separation holds.
Clustering Documents in Large Text Corpora

In this report, two clustering algorithms PDDP and EM are tested using neural network corpus. Terms extracted by C-value / NC-value method are used to construct the space vector model. The resulting clusters are evaluated with the measure of mean scatter value for PDDP algorithm and with the measure of log likelihood for EM algorithm.
Model-Based Subspace Projection Beamforming for Deep Interference Nulling

This paper considers the problem of adaptive array processing for interference canceling to drive very deep nulls in difficult signal environments. In many practical scenarios, the achievable null depth is limited by covariance matrix estimation error leading to poor identification of the interference subspace. We address the particularly troublesome cases of low interference-to-noise ratio (INR), relatively rapid interference motion, and correlated noise across the receiving array. A polynomial-based model is incorporated in the proposed algorithm to track changes in the array covariance matrix over time, mitigate interference subspace estimation errors, and improve canceler performance. The application of phased array feeds for radio astronomical telescopes is used to illustrate the problem and proposed solution. Here even weak residual interference after cancellation may obscure a signal of interest, so very deep beampattern nulls are required. Performance for conventional subspace projection (SP) is compared with polynomial-augmented SP using simulated and real experimental data, showing null-depth improvement of 6 to 30 dB.
A platform for software engineering research

Research in the fields of software quality, maintainability and evolution requires the analysis of large quantities of data, which often originate from open source software projects. Collecting and preprocessing data, calculating metrics, and synthesizing composite results from a large corpus of project artifacts is a tedious and error prone task lacking direct scientific value. The Alitheia Core tool is an extensible platform for software quality analysis that is designed specifically to facilitate software engineering research on large and diverse data sources, by integrating data collection and preprocessing phases with an array of analysis services, and presenting the researcher with an easy to use extension mechanism. Alitheia Core aims to be the basis of an ecosystem of shared tools and research data that will enable researchers to focus on their research questions at hand, rather than spend time on re-implementing analysis tools. In this paper, we present the Alitheia Core platform in detail and demonstrate its usefulness in mining software repositories by guiding the reader through the steps required to execute a simple experiment.
Mining knowledge and data to discover intelligent molecular biomarkers: Prostate cancer i-Biomarkers

Currently, there are some paradigm shifts in medicine, from the search for a single ideal biomarker, to the search for panels of molecules, and from a reductionistic to a systemic view, placing these molecules on functional networks. There is also a general trend to favor non-invasive biomarkers. Identifying non-invasive biomarkers in high-throughput data, having thousands of features and only tens of samples is not trivial. Here, we proposed a methodology and the related concepts to develop intelligent molecular biomarkers, via knowledge mining and knowledge discovery in data, illustrated on prostate cancer diagnosis. An informed feature selection is done by mining knowledge about pathways involved in prostate cancer, in specialized data bases. A knowledge discovery in data approach, with soft computing methods, is used to identify the relevant features and discover their relationships with clinical outcomes. The intelligent non-invasive diagnosis systems, is based on a team of mathematical models, discovered with genetic programming, and taking as inputs eight serum angiogenic molecules and PSA. This systems share with other intelligent systems we build, using this methodology but different soft computing techniques, and in different clinical settings—chronic hepatitis, bladder cancer, and prostate cancer—the best published accuracy, even 100%. Soft computing could be a strong foundation for the newly emerging Knowledge-Based-Medicine. The impact on medical practice could be enormous. Instead of offering just hints to the clinicians, like Evidence-Based-Medicine, Knowledge-Based-Medicine which is made possible and co-exists with Evidence-Based-Medicine, offers intelligent clinical decision supports systems.
Infection With Viruses From Several Families Triggers Autoimmune Diabetes in LEW.1WR1 Rats

OBJECTIVE The contribution of antecedent viral infection to the development of type 1 diabetes in humans is controversial. Using a newer rat model of the disease, we sought to 1) identify viruses capable of modulating diabetes penetrance, 2) identify conditions that increase or decrease the diabetogenicity of infection, and 3) determine whether maternal immunization would prevent diabetes. RESEARCH DESIGN AND METHODS About 2% of LEW.1WR1 rats develop spontaneous autoimmune diabetes, but disease penetrance is much higher if weanling rats are exposed to environmental perturbants including Kilham rat virus (KRV). We compared KRV with other viruses for diabetogenic activity. RESULTS Both KRV and rat cytomegalovirus (RCMV) induced diabetes in up to 60% of LEW.1WR1 rats, whereas H-1, vaccinia, and Coxsackie B4 viruses did not. Simultaneous inoculation of KRV and RCMV induced diabetes in 100% of animals. Pretreatment of rats with an activator of innate immunity increased the diabetogenicity of KRV but not RCMV and was associated with a moderate rate of diabetes after Coxsackie B4 and vaccinia virus infection. Inoculation of LEW.1WR1 dams with both KRV and RCMV prior to pregnancy protected weanling progeny from virus-induced diabetes in a virus-specific manner. CONCLUSIONS Exposure to viruses can affect the penetrance of autoimmune diabetes in genetically susceptible animals. The diabetogenicity of infection is virus specific and is modified by immunomodulation prior to inoculation. Maternal immunization protects weanlings from virus-induced diabetes, suggesting that modification of immune responses to infection could provide a means of preventing islet autoimmunity.
Distributed beamforming for Cognitive Radio

The goal of the Distributed beamforming (DB) method is to collaboratively forward the Cognitive Radio (CR) signal to the Distant CR (DCR) user. We propose a novel Nodes Selection (NS) method which is based on the differences in beam width of a broadside array and an end-fire array. In this scheme the CR nodes which are able to form a full size end-fire array and a reduced size broadside array are selected for forming the beam. The method chooses those CR nodes which are located in the “belt” area along the direction of the DCR user. Simulation results of the average beampattern of the method show that the main beams are successfully directed towards the DCR users and are enlarged for practical applications in CR networks. Furthermore, for a CR network with a large physical size, the proposed node selection method can widen the main beam while maintaining sufficiently low sidelobe levels for CR transmission.
A Survey on QoS-aware Web Service Composition

A comprehensive summary for QoS-aware web service composition, including QoS models and searching algorithms, has been presented in this paper. First, we have introduced the basic workflow models for service composition and their corresponding QoS aggregation functions. Then, traditional approaches for QoS-aware composition are divided into two groups: mathematical programming and heuristic algorithms. The dynamic service composition has also been discussed.
A Block-Based Hough Transform Mapping for Text Line Detection in Handwritten Documents

In this paper, we present a new text line detection method for unconstrained handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes preprocessing for image enhancement, connected component extraction and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible false alarms. The performance of the proposed methodology is based on a consistent and concrete evaluation technique that relies on the comparison between the text line detection result and the corresponding ground truth annotation.
Summer and Winter Effect of Innovative Cool Roof Tiles on the Dynamic Thermal Behavior of Buildings

Cool roofs represent an acknowledged passive cooling technique to reduce building energy consumption for cooling and to mitigate urban heat island effects. This paper concerns the evaluation of the dynamic effect of new cool roof clay tiles on building thermal performance in summer and winter conditions. To this end, these properties have been analyzed on traditional roof brick tiles through an indoor and outdoor two-year long continuous monitoring campaign set up in a residential building located in central Italy. The analysis and the cooperation with industrial companies producing brick tiles and reflective coatings allowed the production of a new tile with notable “cool roof” properties through the traditional industrial manufacturing path of such tiles. Notable results show that during summer the high reflection tiles are able to decrease the average external roof surface temperature by more than 10 °C and the indoor operative temperature by more than 3 °C. During winter the average external surface temperature is lower with high reflection tiles by about 1 °C. Singular optic-thermal phenomena are registered while evaluating the dynamics of the cool roof effect. Interesting findings show how the sloped cool roof application could suggest further considerations about the dynamic effect of cool roofs.
Launching a Thousand Ships : Incentives for Parallel Innovation

A crucial aspect of the innovation process is the exchange of ideas when several workers or teams explore new work methods in parallel. In such a setting, under-exploration may result as workers attempt to free-ride on the new ideas generated by co-workers. This paper studies optimal incentive schemes for innovation and shows that when workers can learn from each other’s experience, incentives for innovation fundamentally differ from incentives for routine activities. Optimal incentives for routine activities take the form of standard pay-for-performance where only individual success determines compensation. In contrast, the optimal incentive scheme for parallel innovation tolerates early failure and provides workers with long-term group incentives for joint success. This result is in line with the empirical regularity that profit sharing, employee ownership, and broad-based stock option plans are positively associated with innovative activity. When subjects in a controlled laboratory experiment are asked to perform a task that requires creativity and exploration, they attempt to free-ride on exploration if given standard pay-forperformance contracts. Innovation success and performance is highest when subjects receive a group incentive scheme that rewards long-term joint success. E-mail: ederer@mit.edu / Land-mail: Department of Economics, E52-391; Massachusetts Institute of Technology; 50 Memorial Drive; Cambridge, MA 02142. I would like to thank Robert Gibbons, Bengt Holmstrom, Gustavo Manso and Ernst Fehr for their encouragement and valuable discussions. I also thank Josh Angrist, Abhijit Banerjee, Arthur Campbell, Lorenz Goette, Martin Oehmke, Nancy Rose, Robert Shimer, Julia Simon-Kerr, Alp Simsek, Johannes Spinnewijn, Jean Tirole, Robert Townsend, Eric Van den Steen, Birger Wernerfelt, Christian Zehnder and seminar audiences at Columbia Business School, HBS, Kellogg School of Management, MIT, Rochester (Simon), UBC (Sauder), UCLA (Anderson), UCSD (Rady), the University of Pennsylvania (Wharton), Washington University (Olin), Yahoo! Research and the IIOC 2009 for helpful comments. I gratefully acknowledge the financial support of the NBER Innovation, Policy, and the Economy group.
Extracorporeal staple technique: an alternative approach to the treatment of critical colostomy stenosis

We describe an extracorporeal staple technique used to treat severe colostomy stenosis under analgo-sedation, thus avoiding relaparotomy. The surgery is performed under short-term sedation. The orifice of the stoma is widened and overgrowing skin is excised. The volume and diameter of the stoma are assessed. The anvil of a circular stapler device is inserted into the lumen of the colostomy. First bowel layers and then skin are closed with purse-string sutures. One firing of the stapler is used to reshape the stoma. The procedure takes around 20–30 min. One circular stapler is used. The patient can be discharged the same day or a day after surgery. No complications were noted in operated patients. At 6- and 12-month follow-ups, a slight narrowing of the colostomy was visible, but no recurrence of the stricture was noted. The described technique is an interesting, easy and safe alternative to previous methods of treatment for stenosed end-colostomy. Importantly, it is an extra-abdominal procedure and may be offered to patients with a history of multiple abdominal operations or with serious coexisting medical conditions in the one-day surgery setting.
Advances in Geosciences Model-based scenarios of Mediterranean droughts

This study examines the change in current 100year hydrological drought frequencies in the Mediterranean in comparison to the 2070s as simulated by the global model WaterGAP. The analysis considers socio-economic and climate changes as indicated by the IPCC scenarios A2 and B2 and the global general circulation model ECHAM4. Under these conditions today’s 100-year drought is estimated to occur 10 times more frequently in the future over a large part of the Northern Mediterranean while in North Africa, today’s 100-year drought will occur less frequently. Water abstractions are shown to play a minor role in comparison to the impact of climate change, but can intensify the situation.
Lessons learned in building a corporate metrics program

The achievements of the Contel Technology Center's software-metrics project, which brought metrics use from concept to standard operating procedure throughout the corporation, are reviewed. Metrics and process maturity pilot projects, metrics tools, metric databases, and metric data analysis are discussed. The lessons learned from the project, which could help metrics programs in other large, diverse organizations, are also discussed. <<ETX>>
A peer to peer social networking service exploiting triangle relationship among friends

These days popular social networking services (SNS) are provided as a centralized database service storing all the data at data centers. Due to the privacy concerns of such centralized systems, Distributed Hash Table (DHT) based P2P SNSes have been proposed, where data query can be done through DHT routing. However, DHT based systems have their own problems. First, DHTs are optimized for universal data dissemination among arbitrary users so that it cannot exploit specific characteristics of SNSes. Furthermore, the frequent data splitting and shifting for the node join/leave operations of DHTs becomes a critical overhead. To tackle the problems of both the centralized and the DHT based systems, in this paper, we exploit the intrinsic characteristics of the social networks. In our proposed system, the data is stored only at the originator and its one-hop neighbors, i.e., the consumers. For a friend without the data to retrieve the data, the friend simply sends query messages to its own one-hop neighbors, which are likely to be the one-hop neighbors of the data originator. This approach reduces the query latency, is highly scalable, and more importantly does not require any data splitting or shifting as the nodes join and leave. We show the performance of our system through extensive simulations over a social network topology collected from a real SNS.
Do Glioma Patients Derive Any Therapeutic Benefit From Taking a Higher Cumulative Dose of Temozolomide Regimens?

AbstractTemozolomide (TMZ) is an oral alkylating agent with established effects on the central nervous system of glioblastoma (GBM) patients. Clinical trials have demonstrated a significant impact on overall survival (OS) with TMZ. Ever since, several TMZ regimens have been designed to improve treatment efficacy by increasing the cumulative dose per cycle. We report a meta-analysis to systematically evaluate different treatment schedules of TMZ in GBM patients.All searches that were conducted in the Cochrane library, Science Direct, and PubMed Databases, and 3 randomized controlled trials (1141 patients) were included. OS and progression-free survival (PFS) were the primary outcomes to be pooled.Unexpectedly, this analysis did not reveal any OS or PFS advantage for the high cumulative dose (HCD) regimen compared with the normal cumulative dose regimen (1141 total patients; hazard ratio [HR] 1.07, 95% CI 0.94–1.22, P = 0.31). Then after analyzing the characteristics of the results from each trial, we found that the regimen with a higher peak concentration during a short-term period (daily doses ≥150 mg/m2/d within ⩽7 days/cycle) always had a more superior clinical benefit. So we generated a new pooled HR of 1.10 with a 95% CI of 0.96–1.25 (P = 0.17), which prefers the high peak concentration schedule even without a significant difference. The adverse outcome also indicates a significant increased risk of leukopenia (risk ratio 1.59, 95% CI 1.03–2.46, P = 0.04) among the HCD group.Our study suggests that increasing the cumulative dose per cycle is not an ideal way to improve the efficacy of TMZ, and it will lead to increased risk for leukopenia. Future trials should be designed to examine schedules of higher peak concentration rather than the cumulative dose per cycle.
MPTCP Is Not Pareto-Optimal: Performance Issues and a Possible Solution

Multipath TCP (MPTCP) has been proposed recently as a mechanism for transparently supporting multiple connections to the application layer. It is under discussion at the IETF. We nevertheless demonstrate that the current MPTCP suffers from two problems: P1) Upgrading some TCP users to MPTCP can reduce the throughput of others without any benefit to the upgraded users, which is a symptom of not being Pareto-optimal; and P2) MPTCP users could be excessively aggressive toward TCP users. We attribute these problems to the linked-increases algorithm (LIA) of MPTCP and, more specifically, to an excessive amount of traffic transmitted over congested paths. The design of LIA forces a tradeoff between optimal resource pooling and responsiveness. We revisit the problem and show that it is possible to provide these two properties simultaneously. We implement the resulting algorithm, called the opportunistic linked-increases algorithm (OLIA), in the Linux kernel, and we study its performance over our testbed by simulations and by theoretical analysis. We prove that OLIA is Pareto-optimal and satisfies the design goals of MPTCP. Hence, it can avoid the problems P1 and P2. Our measurements and simulations indicate that MPTCP with OLIA is as responsive and nonflappy as MPTCP with LIA and that it solves problems P1 and P2.
Thematic relations as a cue to verb class: 2-year-olds distinguish unaccusatives from unergatives

Previous work shows that children use syntactic information to guide their hypotheses about verb meaning. Bunger & Lidz 2004 demonstrated that 2-year-olds map novel unaccusative verbs onto just the result subevent of a complex causative event and novel transitive verbs onto the entire causative event. We present data from a new preferential looking study demonstrating that 2-yearolds map novel unergative verbs onto the means subevent of a causative. We conclude that the interpretation of novel verbs is driven not only by the number of arguments in a given syntactic frame, but also by the semantic roles played by those arguments.
Dynamic positioning for an underactuated marine vehicle using hybrid control

The increasing interest in autonomous marine systems and related applications has motivated, among others, the development of systems and algorithms for the dynamic positioning of underactuated marine vehicles (ships, surface vessels and underwater vehicles) under the influence of unknown environmental disturbances. In this paper, we present a state feedback control solution for the navigation and practical stabilisation of an underactuated marine vehicle under non-vanishing current disturbances, by means of hybrid control. The proposed solution involves a logic-based switching control strategy among simple state feedback controllers, which renders the position trajectories of the vehicle practically stable to a goal set around a desired position. The control scheme consists of three control laws; the first one is active out of the goal set and drives the system trajectories into this set, based on a novel dipolar vector field. The other two control laws are active in the goal set and alternately regulate the position and the orientation of the vehicle, so that the switched system is practically stable around the desired position. The overall system is shown to be robust, in the sense that the vehicle enters and remains into the goal set even if the external current disturbance is unknown, varying and only its maximum bound (magnitude) is given. The efficacy of the proposed solution is demonstrated through simulation results.