snu-aidas/HVMap-dataset huggingface dataset:
Robust solutions for network design under transportation cost and demand uncertainty

In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty. Journal of the Operational Research Society advance online publication, 7 February 2007 doi:10.1057/palgrave.jors.2602362
Three-column fixation for complex tibial plateau fractures.

OBJECTIVES
1) To introduce a computed tomography-based "three-column fixation" concept; and 2) to evaluate clinical outcomes (by using a column-specific fixation technique) for complex tibial plateau fractures (Schatzker classification Types V and VI).


DESIGN
Prospective cohort study.


SETTING
Level 1 trauma center.


PATIENTS
Twenty-nine cases of complex tibial plateau fractures were included. Based on routine x-ray and computed tomography images, all the fractures were classified as a "three-column fracture," which means at least one separate fragment was found in lateral, medial, and posterior columns in the proximal tibia (Schatzker classification Types V and VI).


INTERVENTION
The patients were operated on in a "floating position" with a combined approach, an inverted L-shaped posterior approach combined with an anterior-lateral approach. All three columns of fractures were fixed.


OUTCOME MEASURES
Operative time, blood loss, quality of reduction and alignment, fracture healing, complications, and functional outcomes based on Hospital for Special Surgery score and lower-extremity measure were recorded.


RESULTS
All the cases were followed for average 27.3 months (range, 24-36 months). All the cases had satisfactory reduction except one case, which had a 4-mm stepoff at the anterior ridge of the tibial plateau postoperatively. No case of secondary articular depression was found. One case had secondary varus deformity, one case had secondary valgus deformity, and two cases of screw loosening occurred postoperatively. No revision surgery was performed. Two cases had culture-negative wound drainage. No infection was noted. The average radiographic bony union time and full weightbearing time were 13.1 weeks (range, 11-16 weeks) and 16.7 weeks (range, 12-24 weeks), respectively. The mean Short Form 36, Hospital for Special Surgery score, and lower-extremity measure at 24 months postoperatively were 89 (range, 80-98), 90 (range, 84-98), and 87 (range, 80-95), respectively. The average range of motion of the affected knee was 2.7° to 123.4° at 2 years after the operation.


CONCLUSION
Three-column fixation is a new fixation concept in treating complex tibial plateau fractures, which is especially useful for multiplanar fractures involving the posterior column. The combination of posterior and anterior-lateral approaches is a safe and effective way to have direct reduction and satisfactory fixation for such difficult tibial plateau fractures.
Fitting Tweedie ’ s Compound Poisson Model to Insurance Claims Data : Dispersion Modelling

We reconsider the problem of producing fair and accurate tariffs based on aggregated insurance data giving numbers of claims and total costs for the claims. Jørgensen and de Souza (Scand. Actuarial J., 1994) assumed Poisson arrival of claims and gamma distributed costs for individual claims. Jørgensen and de Souza (1994) directly modelled the risk or expected cost of claims per insured unit, μ say. They observed that the dependence of the likelihood function on μ is as for a linear exponential family, so that modelling similar to that of generalized linear models is possible. In this paper we observe that, when modelling the cost of insurance claims, it is generally necessary to model the dispersion of the costs as well as their mean. In order to model the dispersion we use the framework of double generalized linear models. Modelling the dispersion increases the precision of the estimated tariffs. The use of double generalized linear models also allows us to handle the case where only the total cost of claims and not the number of claims has been recorded.
Robust TiN HM process to overcome under etch issue for SAV scheme on 14nm node

For advance node such as 14nm technology and beyond, back end of line interconnect has implemented self-aligned via (SAV) schemes for better via-metal short process window [1]. A TiN metal hard mask (MHM) is used for the trench pattern definition, after which via lithography and partial via (PV) etch is performed where the TiN was opened. The via etch condition has very good selectivity so that via is formed in a self-aligned fashion by TiN HM [2]. It is indeed to have significant benefit of via to metal short [3]. However, one of the trade off in SAV scheme is the via under etch that whether or not via can well land on an opened oxide area during PV etch. To define the contact area between via resist hole and opened HM oxide as an effective area for via to open successfully. “Fig. 1” shows the effective area change during process variation due to Via alignment, Via photo resist CD variation (Via ADICD) and post hard mask etch CD variation (AMICD). “Fig. 2” shows the mechanism of this under etch failure mode and typical TEM image from 64nm metal pitch test vehicle. In this work, we try to enlarge the process window by an aggressive AMICD targeting in combining with a higher density TiN material to maintain profile.
Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.02.075 ⇑ Corresponding author. Tel.: +4773597119. E-mail addresses: hanhong@stud.ntnu.no (H (Y. Wang), Kesheng.wang@ntnu.no (K. Wang), (Y. Chen). One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers. 2011 Elsevier Ltd. All rights reserved.
Proposal Encoding and Decoding Graph Representations of Natural Language
The Relevance of Data Warehousing and Data Mining in the Field of Evidence-based Medicine to Support Healthcare Decision Making

Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient’s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine. Keywords—data mining, data warehousing, decision-support systems, evidence-based medicine.
Towards an implementation framework for business intelligence in healthcare

As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization’s decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes. © 2013 Elsevier Ltd. All rights reserved.
Stock Selection and Trading Based on Cluster Analysis of Trend and Momentum Indicators
Neural Probabilistic Language Models
Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM

Now-a-days, diabetes disease is considered one of the key reasons of death among the people in the world. The availability of extensive medical information leads to the search for proper tools to support physicians to diagnose diabetes disease accurately. This research aimed at improving the diagnostic accuracy and reducing diagnostic missclassification based on the extracted significant diabetes features. Feature selection is critical to the superiority of classifiers founded through knowledge discovery approaches, thereby solving the classification problems relating to diabetes patients. This study proposed an integration approach between the SVM technique and K-means clustering algorithms to diagnose diabetes disease. Experimental results achieved high accuracy for differentiating the hidden patterns of the Diabetic and Nondiabetic patients compared with the modern diagnosis methods in term of the performance measure. The T-test statistical method obtained significant improvement results based on KSVM technique when tested on the UCI Pima Indian standard dataset. Keywords—K-means Clustering; Diabetes Patients; SVM;
Experiments with a New Boosting Algorithm

In an earlier paper [9], we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a “pseudo-loss” which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman’s [1] “bagging” method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.
Categorical equational systems : algebraic models and equational reasoning

Declaration This dissertation is the result of my own work done under the guidance of my supervisor, and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation is not substantially the same as any that I have submitted or will be submitting for a degree or diploma or other qualification at this or any other University. This dissertation does not exceed the regulation length of 60,000 words, including tables and footnotes. 5 Summary We introduce two abstract notions of equational algebraic system, called Equational System (ES) and Term Equational System (TES), in order to achieve sufficient expressivity as needed in modern applications in computer science. These generalize the classical concept of (enriched) algebraic theory of Kelly and Power [1993]. We also develop a theory for constructing free algebras for ESs and a theory of equational reasoning for TESs. In Part I, we introduce the general abstract, yet practical, concept of equational system and develop finitary and transfinitary conditions under which we give an explicit construction of free algebras for ESs. This free construction extends the well-known construction of free algebras for ω-cocontinuous endofunctors to an equational setting, capturing the intuition that free algebras consist of freely constructed terms quotiented by given equations and congruence rules. We further show the monadicity and cocom-pleteness of categories of algebras for ESs under the finitary and transfinitary conditions. To illustrate the expressivity of equational systems, we exhibit various examples including two modern applications, the Σ-monoids of Fiore et al. [1999] and the π-algebras of Stark [2005]. In Part II, we introduce the more concrete notion of term equational system, which is obtained by specializing the concept of equational system, but remains more general than that of enriched algebraic theory. We first develop a sound logical deduction system, called Term Equational Logic (TEL), for equational reasoning about algebras of TESs. Then, to pursue a complete logic, we give an internal completeness result, from which together with the explicit construction of free algebras one can typically synthesize sound and complete rewriting-style equational logics. To exemplify this scenario, we give two applications: multi-sorted algebraic theories and nominal equational theories of Clouston and Pitts [2007] and of Gabbay and Mathijssen [2007]. 7 Acknowledgements First and foremost, I am deeply grateful to my Ph.D. supervisor Marcelo Fiore for his patient guidance and support, without which this thesis would …
Anatomical Basis for Safe and Effective Volumization of the Temple.

BACKGROUND
One of the earliest but often unaddressed signs of facial aging is volume loss in the temple. Treatment of the area can produce satisfying results for both patient and practitioner.


OBJECTIVE
Safe injection requires explicit knowledge of the anatomy to avoid complications related to the multitude of vessels that course throughout the region at various depths. The authors aim to detail the anatomy of the area and provide a safe and easy-to-follow method for injection.


MATERIALS AND METHODS
The authors review the relevant anatomy of the temporal region and its application to cosmetic filler injections.


RESULTS
The authors describe an easy-to-follow approach for a safe and effective injection window based on numerous anatomical studies. Injection in this area is not without risk, including potential blindness. The authors review the potential complications and their treatments.


CONCLUSION
Hollowing of the temple is an early sign of aging that, when corrected, can lead to significant patient and practitioner satisfaction. Proper anatomically knowledge is required to avoid potentially severe complications. In this study, the authors present a reliable technique to safely and effectively augment this often undertreated area of the aging face.
Learning Important Features Through Propagating Activation Differences

The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/ qKb7pL, code: http://goo.gl/RM8jvH.
Software Architecture Quality Measurement Stability and

Over the past years software architecture has become an important sub-field of software engineering. There has been substantial advancement in developing new technical approaches to start handling architectural design as an engineering discipline. Measurement is an essential part of any engineering discipline. Quantifying the quality attributes of the software architecture will reveal good insights about the architecture. It will also help architects and practioners to choose the best fit of alternative architectures that meets their needs. This work paves the way for researchers to start investigating ways to measure software architecture quality attributes. Measurement of these qualities is essential for this sub-field of software engineering. This work explores Stability and Understandability of software architecture, several metrics that affect them, and literature review of these qualities. Keywords—Software Engineering; Software Architecture; Quality Attributes; Stability; Understandability
Eye Contact Detection via Deep Neural Networks

With the presence of ubiquitous devices in our daily lives, effectively capturing and managing user attention becomes a critical device requirement. While gaze-tracking is typically employed to determine the user’s focus of attention, gaze-lock detection to sense eyecontact with a device is proposed in [16]. This work proposes eye contact detection using deep neural networks, and makes the following contributions: (1) With a convolutional neural network (CNN) architecture, we achieve superior eye-contact detection performance as compared to [16] with minimal data pre-processing ; our algorithm is furthermore validated on multiple datasets, (2) Gaze-lock detection is improved by combining head pose and eye-gaze information consistent with social attention literature, and (3) We demonstrate gaze-locking on an Android mobile platform via CNN model compression.
An interactive tool for manual, semi-automatic and automatic video annotation

The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications. The aim of annotation tools is to relieve the user from the burden of the manual annotation as much as possible. To achieve this ideal goal, many different functionalities are required in order to make the annotations process as automatic as possible. Motivated by the limitations of existing tools, we have developed the iVAT: an interactive Video Annotation Tool. It supports manual, semi-automatic and automatic annotations through the interaction of the user with various detection algorithms. To the best of our knowledge, it is the first tool that integrates several computer vision algorithms working in an interactive and incremental learning framework. This makes the tool flexible and suitable to be used in different application domains. A quantitative and qualitative evaluation of the proposed tool on a challenging case study domain is presented and discussed. Results demonstrate that the use of the semi-automatic, as well as the automatic, modality drastically reduces the human effort while preserving the quality of the annotations. 2014 Elsevier Inc. All rights reserved.
Community Detection based on Distance Dynamics

In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots communities in a network by examining the changes of "distances" among nodes (i.e. distance dynamics). The fundamental idea is to envision the target network as an adaptive dynamical system, where each node interacts with its neighbors. The interaction will change the distances among nodes, while the distances will affect the interactions. Such interplay eventually leads to a steady distribution of distances, where the nodes sharing the same community move together and the nodes in different communities keep far away from each other. Building upon the distance dynamics, Attractor has several remarkable advantages: (a) It provides an intuitive way to analyze the community structure of a network, and more importantly, faithfully captures the natural communities (with high quality). (b) Attractor allows detecting communities on large-scale networks due to its low time complexity (O(|E|)). (c) Attractor is capable of discovering communities of arbitrary size, and thus small-size communities or anomalies, usually existing in real-world networks, can be well pinpointed. Extensive experiments show that our algorithm allows the effective and efficient community detection and has good performance compared to state-of-the-art algorithms.
Urban Physics : Effect of the micro-climate on comfort , health and energy demand

Y-NC-ND license. Abstract The global trend towards urbanisation explains the growing interest in the study of the modification of the urban climate due to the heat island effect and global warming, and its impact on energy use of buildings. Also urban comfort, health and durability, referring respectively to pedestrian wind/ thermal comfort, pollutant dispersion and wind-driven rain are of interest. Urban Physics is a wellestablished discipline, incorporating relevant branches of physics, environmental chemistry, aerodynamics, meteorology and statistics. Therefore, Urban Physics is well positioned to provide keycontributions to the current urban problems and challenges. The present paper addresses the role of Urban Physics in the study of wind comfort, thermal comfort, energy demand, pollutant dispersion and wind-driven rain. Furthermore, the three major research methods applied in Urban Physics, namely field experiments, wind tunnel experiments and numerical simulations are discussed. Case studies illustrate the current challenges and the relevant contributions of Urban Physics. & 2012. Higher Education Press Limited Company. Production and hosting by Elsevier B.V. Open access under CC BY-NC-ND license.