nils-herrmann/clean_scirepeval huggingface dataset:
Discriminative, generative and imitative learning

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the ExpectationMaximization (EM) algorithm for latent discriminative learning (or latent MED). While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior.  Toshiba Professor of Media Arts and Sciences, MIT Media Lab Discriminative, Generative and Imitative Learning
Image Retrieval with Fisher Vectors of Binary Features

Recently, the Fisher vector representation of local features has attracted much attention because of its effectiveness in both image classification and image retrieval. Another trend in the area of image retrieval is the use of binary feature such as ORB, FREAK, and BRISK. Considering the significant performance improvement in terms of accuracy in both image classification and retrieval by the Fisher vector of continuous feature descriptors, if the Fisher vector were also to be applied to binary features, we would receive the same benefits in binary feature based image retrieval and classification. In this paper, we derive the closed-form approximation of the Fisher vector of binary features which are modeled by the Bernoulli mixture model. In experiments, it is shown that the Fisher vector representation improves the accuracy of image retrieval by 25% compared with a bag of binary words approach.
Learning to Run Heuristics in Tree Search

“Primal heuristics” are a key contributor to the improved performance of exact branch-and-bound solvers for combinatorial optimization and integer programming. Perhaps the most crucial question concerning primal heuristics is that of at which nodes they should run, to which the typical answer is via hard-coded rules or fixed solver parameters tuned, offline, by trial-and-error. Alternatively, a heuristic should be run when it is most likely to succeed, based on the problem instance’s characteristics, the state of the search, etc. In this work, we study the problem of deciding at which node a heuristic should be run, such that the overall (primal) performance of the solver is optimized. To our knowledge, this is the first attempt at formalizing and systematically addressing this problem. Central to our approach is the use of Machine Learning (ML) for predicting whether a heuristic will succeed at a given node. We give a theoretical framework for analyzing this decision-making process in a simplified setting, propose a ML approach for modeling heuristic success likelihood, and design practical rules that leverage the ML models to dynamically decide whether to run a heuristic at each node of the search tree. Experimentally, our approach improves the primal performance of a stateof-the-art Mixed Integer Programming solver by up to 6% on a set of benchmark instances, and by up to 60% on a family of hard Independent Set instances.
Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection

The maximum clique problem is a well known NP-Hard problem with applications in data mining, network analysis, information retrieval and many other areas related to the World Wide Web. There exist several algorithms for the problem with acceptable runtimes for certain classes of graphs, but many of them are infeasible for massive graphs. We present a new exact algorithm that employs novel pruning techniques and is able to find maximum cliques in very large, sparse graphs quickly. Extensive experiments on different kinds of synthetic and real-world graphs show that our new algorithm can be orders of magnitude faster than existing algorithms. We also present a heuristic that runs orders of magnitude faster than the exact algorithm while providing optimal or near-optimal solutions. We illustrate a simple application of the algorithms in developing methods for detection of overlapping communities in networks.
Differential Evolution with an Evolution Path: A DEEP Evolutionary Algorithm

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.
Effective Background Model-Based RGB-D Dense Visual Odometry in a Dynamic Environment

This paper proposes a robust background model-based dense-visual-odometry (BaMVO) algorithm that uses an RGB-D sensor in a dynamic environment. The proposed algorithm estimates the background model represented by the nonparametric model from depth scenes and then estimates the ego-motion of the sensor using the energy-based dense-visual-odometry approach based on the estimated background model in order to consider moving objects. Experimental results demonstrate that the ego-motion is robustly obtained by BaMVO in a dynamic environment.
Development of a framework for fire risk assessment using remote sensing and geographic information system technologies.

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents
Rarely seen complications of circumcision, and their management.

Circumcision, performed for religious or medical reasons is the procedure of surgical excision of the skin covering the glans penis, preputium in a certain shape and dimension so as to expose the tip of the glans penis. Short- and long- term complication rates of up to 50% have been reported, varying due to the recording system of different countries in which the procedure has been accepted as a widely performed simple surgical procedure. In this study, treatment procedures in patients presented to our clinic with complications after circumcision are described and methods to decrease the rate of the complications are reviewed.



Cases that presented to our clinic between 2010 and 2013 with early complications of circumcision were retrospectively reviewed. Cases with acceptedly major complications as excess skin excision, skin necrosis and total amputation of the glans were included in the study, while cases with minor complications such as bleeding, hematoma and infection were excluded from the study.



Repair with full- thickness skin grafts was performed in patients with excess skin excision. In cases with skin necrosis, following the debridement of the necrotic skin, primary repair or repair with full- thickness graft was performed in cases where full- thickness skin defects developed and other cases with partial skin loss were left to secondary healing. Repair with an inguinal flap was performed in the case with glans amputation.



Circumcisions performed by untrained individuals are to be blamed for the complications of circumcision reported in this country. The rate of complications increases during the "circumcision feasts" where multiple circumcisions were performed. This also predisposes to transmission of various diseases, primarily hepatitis B/C and AIDS. Circumcision is a surgical procedure that should be performed by specialists under appropriate sterile circumstances in which the rate of complications would be decreased. The child may be exposed to recurrent psychosocial and surgical trauma when it is performed by incompetent individuals.
A mechanistic model quantifies artemisinin-induced parasite growth retardation in blood-stage Plasmodium falciparum infection.

Falciparum malaria is a major parasitic disease causing widespread morbidity and mortality globally. Artemisinin derivatives-the most effective and widely-used antimalarials that have helped reduce the burden of malaria by 60% in some areas over the past decade-have recently been found to induce growth retardation of blood-stage Plasmodium falciparum when applied at clinically relevant concentrations. To date, no model has been designed to quantify the growth retardation effect and to predict the influence of this property on in vivo parasite killing. Here we introduce a mechanistic model of parasite growth from the ring to trophozoite stage of the parasite's life cycle, and by modelling the level of staining with an RNA-binding dye, we demonstrate that the model is able to reproduce fluorescence distribution data from in vitro experiments using the laboratory 3D7 strain. We quantify the dependence of growth retardation on drug concentration and identify the concentration threshold above which growth retardation is evident. We estimate that the parasite life cycle is prolonged by up to 10 hours. We illustrate that even such a relatively short delay in growth may significantly influence in vivo parasite dynamics, demonstrating the importance of considering growth retardation in the design of optimal artemisinin-based dosing regimens.
Multibeam SIW Slotted Waveguide Antenna System Fed by a Compact Dual-Layer Rotman Lens

A multibeam antenna system fed by a compact Rotman lens is presented in multilayer substrate integrated waveguide (SIW) technology. The lens is implemented in two layers using a new transition based on several star-shaped coupling slots and an SIW integrated reflector. Compared to standard rectangular coupling slots, this transition is broadband and maximizes power transfer between the two layers of the lens regardless the position of the beam port along the focal arc of the Rotman lens. The radiating part is an array of 15 slotted waveguides. These waveguides are centrally fed to enhance their bandwidth and reduce the “long line effect.” To validate the proposed concepts, an antenna system has been prototyped at 24.15 GHz. The designed transition leads to a physical size reduction of about 50% for the Rotman lens, and more than 33% for the antenna footprint compared to standard single-layer implementations. The experimental results demonstrate a scanning range of ±33° and a 4% bandwidth for VSWR <; 2; the isolation between input ports is better than -10 dB.
Contextual Prediction Models for Speech Recognition

We introduce an approach to biasing language models towards known contexts without requiring separate language models or explicit contextually-dependent conditioning contexts. We do so by presenting an alternative ASR objective, where we predict the acoustics and words given the contextual cue, such as the geographic location of the speaker. A simple factoring of the model results in an additional biasing term, which effectively indicates how correlated a hypothesis is with the contextual cue (e.g., given the hypothesized transcript, how likely is the user’s known location). We demonstrate that this factorization allows us to train relatively small contextual models which are effective in speech recognition. An experimental analysis shows a perplexity reduction of up to 35% and a relative reduction in word error rate of 1.6% on a targeted voice search dataset when using the user’s coarse location as a contextual cue.
Better External Memory Suffix Array Construction

Suffix arrays are a simple and powerful data structure for text processing that can be used for full text indexes, data compression, and many other applications, in particular, in bioinformatics. However, so far, it has appeared prohibitive to build suffix arrays for huge inputs that do not fit into main memory. This paper presents design, analysis, implementation, and experimental evaluation of several new and improved algorithms for suffix array construction. The algorithms are asymptotically optimal in the worst case or on average. Our implementation can construct suffix arrays for inputs of up to 4-GB in hours on a low-cost machine. As a tool of possible independent interest, we present a systematic way to design, analyze, and implement pipelined algorithms.
Knowledge Discovery in Textual Databases (KDT)

The information age is characterized by a rapid growth in the amount of information available in electronic media. Traditional data handling methods are not adequate to cope with this information flood. Knowledge Discovery in Databases (KDD) is a new paradigm that focuses on computerized exploration of large amounts of data and on discovery of relevant and interesting patterns within them. While most work on KDD is concerned with structured databases, it is clear that this paradigm is required for handling the huge amount of information that is available only in unstructured textual form. To apply traditional KDD on texts it is necessary to impose some structure on the data that would be rich enough to allow for interesting KDD operations. On the other hand, we have to consider the severe limitations of current text processing technology and define rather simple structures that can be extracted from texts fairly automatically and in a reasonable cost. We propose using a text categorization paradigm to annotate text articles with meaningful concepts that are organized in hierarchical structure. We suggest that this relatively simple annotation is rich enough to provide the basis for a KDD framework, enabling data summarization, exploration of interesting patterns, and trend analysis. This research combines the KDD and text categorization paradigms and suggests advances to the state of the art in both areas.
Direct to consumer genetic testing: a systematic review of position statements, policies and recommendations.

In healthcare settings, genetic tests to determine whether an individual had inherited a genetic mutation are ordered by a health professional, and the results are interpreted and conveyed to the patient by that person. However, direct to consumer genetic testing (DTCGT) has enabled individuals to purchase genetic tests and receive results without the intervention of a health professional. To inform a set of guidelines for consumers and health professionals, we undertook a systematic review of position statements, policies and recommendations on the use of DTCGT. We performed a search of seven databases and the Internet for relevant documents. The search terms were 'direct to consumer' and 'genetic test', and documents in English published from 2002 to 2011 were included. The search retrieved 314 items, of which 14 were eligible for review. Five themes were derived from thematic analysis: motivation for use, potential benefits, potential harms, recommendations to guide consumers and need for research. The authors of these documents described more potential harms than benefits, but, although some stated that direct to consumer testing should be actively discouraged, others supported consumer rights to make autonomous choices. Further research into the impact of direct to consumer testing on health services and consumers is required to inform policies.
Morphological analysis of Bangla words for Universal Networking Language

This paper focuses on morphological analysis of Bangla words to incorporate them into Bangla to universal networking language (UNL) processors. Researchers have been working on morphological structure of Bangla for machine translation and a considerable volume of work is available. So far, no attempt has been made to integrate the works for a concrete computational output. In this paper we particularly emphasize on bringing previous works on morphological analysis in the framework of UNL, with the goal to produce a Bangla-UNL dictionary, as UNL structures can provide, for any morphological analysis, a unified base to fit into already developed universal conversion systems of UNL. We explain the morphological rules of Bangla words for UNL structures. These rules tend to expose the modifications of parts of speech with regards to tense, person, subject etc. of the words of a sentence. Here we outline the morphology of nouns, verbs and adjective phrases only.
Characterising Behavior and Emotions on Social Media for Safety: Exploring Online Communication between Police and Citizens

Increased use of social media by police to connect with citizens has encouraged researchers to study different aspects of information exchange (e.g. type of information, credibility and propagation) during emergency and crisis situation. Research studies lack understanding of human behavior such as engagement, emotions and social interaction between citizen and police department on social media. Several social media studies explore and show technological implications of human behavioral aspects in various contexts such as workplace interaction and depression in young mothers. In this paper, we study online interactions between citizens and Indian police in context of day-to-day policing, including safety concerns, advisories, etc. Indian police departments use Facebook to issue advisories, send alerts and receive citizen complaints and suggestions regarding safety issues and day-today policing. We explore how citizens express their emotions and social support on Facebook. Our work discusses technological implications of behavioral aspects on social well being of citizens.
The melanocyte lineage in development and disease

Melanocyte development provides an excellent model for studying more complex developmental processes. Melanocytes have an apparently simple aetiology, differentiating from the neural crest and migrating through the developing embryo to specific locations within the skin and hair follicles, and to other sites in the body. The study of pigmentation mutations in the mouse provided the initial key to identifying the genes and proteins involved in melanocyte development. In addition, work on chicken has provided important embryological and molecular insights, whereas studies in zebrafish have allowed live imaging as well as genetic and transgenic approaches. This cross-species approach is powerful and, as we review here, has resulted in a detailed understanding of melanocyte development and differentiation, melanocyte stem cells and the role of the melanocyte lineage in diseases such as melanoma.
Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise

Biometric fingerprint scanners are positioned to provide improved security in a great span of applications from government to private. However, one highly publicized vulnerability is that it is possible to spoof a variety of fingerprint scanners using artificial fingers made from Play-Doh, gelatin and silicone molds. Therefore, it is necessary to offer protection for fingerprint systems against these threats. In this paper, an anti-spoofing detection method is proposed which is based on ridge signal and valley noise analysis, to quantify perspiration patterns along ridges in live subjects and noise patterns along valleys in spoofs. The signals representing gray level patterns along ridges and valleys are explored in spatial, frequency and wavelet domains. Based on these features, separation (live/spoof) is performed using standard pattern classification tools including classification trees and neural networks. We test this method on a larger dataset than previously considered which contains 644 live fingerprints (81 subjects with 2 fingers for an average of 4 sessions) and 570 spoof fingerprints (made from Play-Doh, gelatin and silicone molds in multiple sessions) collected from the Identix fingerprint scanner. Results show that the performance can reach 99.1% correct classification overall. The proposed anti-spoofing method is purely software based and integration of this method can provide protection for fingerprint scanners against gelatin, Play-Doh and silicone spoof fingers. ∗Phone: (1)315-2686536 ∗∗Fax: (1)315-2687600 Email addresses: tanb@clarkson.edu, sschucke@clarkson.edu (Bozhao Tan, Stephanie Schuckers)  2009