nils-herrmann/clean_scirepeval huggingface dataset:
The back door: venous-mediated metastasis into cervical lymph nodes as an alternative metastatic pathway for oropharyngeal squamous cell carcinoma.

Circulating lymphocytes may home to lymph nodes (LNs) via paracortical postcapillary venules, high-endothelial venules (HEVs) that recognize circulating lymphocytes, enabling them to migrate into nodal cortical/paracortical regions. Our goal was to find any histologic, immunohistochemical, or in vitro evidence to support the hypothesis that squamous cell carcinoma (SCC) may gain access to LNs via an alternative venolymphatic pathway. Slides from 67 neck dissections with SCC were studied. Standard criteria for lymphatic-mediated metastasis were used; criteria for evidence ofvenolymphatic metastasis were tumor nests localized to the paracortical regions, directly contiguous to HEVs but not marginal sinuses. Cases in which LN architecture was obliterated by metastases were excluded. A modified Stamper-Woodruff assay, which assesses HEV binding, incubated cell lines from oral carcinomas and lung adenocarcinomas with fresh frozen LN sections. Double-blinded cell counts were performed by two observers and analyzed by Student's t test. Immunohistochemical examination was undertaken on 19 paraffin-embedded cases with the monoclonal antibody, MoAb CD44v6, to discover whether expression of this adhesion molecule correlated with metastatic pattern. Twenty-nine cases (66%) revealed evidence only for the LN route of metastasis; 4 cases (9%) were classified as venolymphatic metastasis; 11 cases (25%) showed evidence for both pathways. The Stamper-Woodruff assay confirmed that SCC cells preferentially bound to HEV within cervical LN sections more than did adenocarcinoma cells (P = .02). Strong staining to MoAb CD44v6 was seen in 18 (95%) of 19 SCCs with a membranous pattern. Occasionally, CD44v6 highlighted tumor emboli adjacent to HEVs, but no overall correlation could be made between CD44v6 expression and pattern of spread. Tumor metastasis via lymphatic channels is the predominant mode of metastatic spread, but the venolymphatic route is a plausible alternative pathway. The preferential attachment of SCC cells to HEVs supports this theory.
Modeling Bayesian estimation for deformable contours

A novel trainable snake model called EigenSnake, is presented in the Bayesian framework. In the EigenSnake, prior knowledge of a specific object shape, such as that of face outlines and facial features, is derived from a training set of the shape and incorporated into a Bayesian snake model in the form of the prior distribution. Further, a "shape space", which is constructed on the basis of a set of eigenvectors obtained from principle component analysis, is used to restrict and stabilize the search for the optimal solution. The effectiveness is demonstrated by experiments, which shows that the EigenSnake produces more reliable and accurate results than existing models.
Face recognition using the nearest feature line method

In this paper, we propose a novel classification method, called the nearest feature line (NFL), for face recognition. Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can capture more variations of face images than the original points and thus expands the capacity of the available database. The classification is based on the nearest distance from the query feature point to each FL. With a combined face database, the NFL error rate is about 43.7-65.4% of that of the standard eigenface method. Moreover, the NFL achieves the lowest error rate reported to date for the ORL face database.
Bayesian object matching

A Bayesian approach to object matching is presented. An object and a scene are each represented by features, such as critical points, line segments and surface patches, constrained by unary properties and contextual relations. The matching is presented as a labeling problem, where each feature in the scene is assigned (associated with) a feature of the known model objects. The prior distribution of a scene's labeling is modeled as a Markov random field, which encodes the between-object constraints. The conditional distribution of the observed features labeled is assumed to be Gaussian, which encodes the within-object constraints. An optimal solution is defined as a maximum a posteriori estimate. Relationships with previous work are discussed. Experimental results are shown.
Close-Form Solution and Parameter Selection for Convex Minimization-Based Edge-Preserving Smoothing

This work presents a new approach for the analysis of convex minimization-based edge-preserving image smoothing and the parameter selection therein. The global solution, that is, the response of a convex smoothing model to the ideal step edge, is derived in close-form. By analyzing the close-form solution, insights are drawn into how the optimal solution responds to edges in the data and how the parameter values affect resultant edges in the solution. Based on this, a scheme is proposed for selecting parameters to achieve desirable responses at edges. The theoretic results are substantiated by experiments.
A Two-Stage Probabilistic Approach for Object Recognition

Assume that some objects are present in an image but can be seen only partially and are overlapping each other. To recognize the objects, we have to firstly separate the objects from one another, and then match them against the modeled objects using partial observation. This paper presents a probabilistic approach for solving this problem. Firstly, the task is formulated as a two-stage optimal estimation process. The first stage, matching, separates different objects and finds feature correspondences between the scene and each potential model object. The second stage, recognition, resolves inconsistencies among the results of matching to different objects and identifies object categories. Both the matching and recognition are formulated in terms of the maximum a posteriori (MAP) principle. Secondly, contextual constraints, which play an important role in solving the problem, are incorporated in the probabilistic formulation. Specifically, between-object constraints are encoded in the prior distribution modeled as a Markov random field, and within-object constraints are encoded in the likelihood distribution modeled as a Gaussian. They are combined into the posterior distribution which defines the MAP solution. Experimental results are presented for matching and recognizing jigsaw objects under partial occlusion, rotation, translation and scaling.
Hierarchical linear combinations for face recognition

A hierarchical representation consisting of two level linear combinations (LC) is proposed for face recognition. At the first level, a face image is represented as a linear combination (LC) of a set of basis vectors, i.e. eigenfaces. Thereby a face image corresponds to a feature vector (prototype) in the eigenface space. Normally several such prototypes are available for a face class, each representing the face under a particular condition such as in viewpoint, illumination, and so on. We propose to use the second level LC, that of the prototypes belonging to the same face class, to treat the prototypes coherently. The purpose is to improve face recognition under a new condition not captured by the prototypes by using a linear combination of them. A new distance measure called nearest LC (NLC) is proposed as opposed to the NN. Experiments show that our method yields significantly better results than the one level eigenface methods.
Edge-Preserving Smoothing by Convex Minimization

This work presents a new approach for analyzing the problem of edge-preserving image smoothing using convex minimization and for selecting smoothing parameters. The close-form (global) solution is derived as the response of a convex smoothing model to the ideal step edge. Insights into how the minimal solution responds to edges in the data and how the parameter values affect resultant edges in the solution are drawn from the analytic expression of the close-form solution. Based on this, a scheme is proposed for selecting parameters to achieve desirable response at edges.
Model-Based Active Object Recognition Using MRF Matching and Sensor Planning

This paper presents an active object recognition algorithm using MAP-MRF matching and sensor planning strategy. The matching between the sensed and model object is based on surface properties. A new measure of surface distinguishability is defined for sensor planning. MAP-MRF framework is used for generating matching label set. A measure of confidence of correct match is determined based on the posterior energy. An active object recognition algorithm is used for determining the next viewpoint of the camera if ambiguity exists in the matching result. The next viewpoint is chosen based the surface with highest distinguishability. Experimental results on images under perfect and imperfect segmentation are presented.
Face recognition based on nearest linear combinations

This paper proposes a novel pattern classification approach, called the nearest linear combination (NLC) approach, for eigenface based face recognition. Assume that multiple prototypical vectors are available per class, each vector being a point in an eigenface space. A linear combination of prototypical vectors belonging to a face class is used to define a measure of distance from the query vector to the class, the measure being defined as the Euclidean distance from the query to the linear combination nearest to the query vector (hence NLC). This contrasts to the nearest neighbor (NN) classification where a query vector is compared with each prototypical vector individually. Using a linear combination of prototypical vectors, instead of each of them individually, extends the representational capacity of the prototypes by generalization through interpolation and extrapolation. Experiments show that it leads to better results than existing classification methods.
Generalizing capacity of face database for face recognition

A face image can be represented by a point in a feature space such as spanned by a number of eigenfaces. In methods based on nearest neighbor classification, the representational capacity of a face database depends on how prototypical face images are chosen to account for possible image variations and also how many prototypical images or their feature points are available. We propose a novel method for generalizing the representational capacity of available face database. Any two feature points of the same class (individual) are generalized by the feature line passing through the points. The feature line covers more of the face space than the feature points and thus expands the capacity of the available database. In the feature line representation, the classification is based on the distance between the feature point of the query image and each of the feature lines of the prototypical images. Experiments are presented using a data set from five databases: the MIT, Cambridge, Bern, Yale and our own. There are 620 images of 124 individuals subject to varying viewpoint, illumination, and expression. The results show that the error rate of the proposed method is about 55%-60% of that of the standard eigenface method of M.A. Turk and A.P. Pentland (1991). They also demonstrate that the recognition result can be used for inferring how the position of the input face relative to the two retrieved faces.
Document segmentation and classification with top-down approach

A robust algorithm for document segmentation and classification has been implemented. In this algorithm, a top-down approach is adopted due to its fast processing speed and reliability. The approach accounts for every portion of the processed page. It is also capable of image display at different scales and allows editing of classified blocks such as the changing, merging and splitting of blocks. The segmentation and classification processes are explained using examples. A large number of sample document images have been tested with satisfactory results.
Toward Global Solution to MAP Image Estimation: Using Common Structure of Local Solutions

The maximum a posteriori (MAP) principle is often used in image restoration and segmentation to define the optimal solution when both the prior and likelihood distributions are available. MAP estimation is equivalent to minimizing an energy function. It is desirable to find the global minimum. However, the minimization in the MAP image estimation is non-trivial due to the use of contextual constraints between pixels. Steepest descent methods such as ICM quickly finds a local minimum but the solution quality depends much on the initialization. Some initializations are better than others. In this paper, we present an iterative optimization algorithm, called the Comb algorithm, for approximating the global minmum. The Comb maintains a number of best local minima found so far. It uses the Common structure of Best local minima (hence “Comb”) to derive new initial configurations. Because the derived configurations contain some structure resembling that of the global minimum, they may provide good starting points for local search to approach the global minimum. Experimental comparisons show that the Comb produces solutions of quality much better than ICM and comparable to simulated annealing.
Energy Minimization and Relaxation Labeling

Recently, there has been increasing interest in Markov random eld (MRF) modeling for solving a variety of computer vision problems formulated in terms of the maximum a posteriori (MAP) probability. When the label set is discrete, such as in image segmentation and matching, the minimization is combinatorial. The objective of this paper is twofold: Firstly, we propose to use the continuous relaxation labeling (RL) as an alternative approach for the minimization. The motivation is that it provides a good compromise between the solution quality and the computational cost. We show how the original combinatorial optimization can be converted into a form suitable for continuous RL. Secondly, we compare various minimization algorithms, namely, the RL algorithms proposed by Rosenfeld et al. and by Hummel and Zucker, the mean eld annealing of Peterson and Soderberg, simulated annealing of Kirkpatrick, the iterative conditional modes (ICM) of Besag and an annealing version of ICM proposed in this paper. The comparisons are in terms of the minimized energy value (i.e. the solution quality), the required number of iterations (i.e. the computational cost), and also the dependence of each algorithm on heuristics.
Light‐scattering photometer with optical microscope for the in‐line study of polymer extrusion

A light-scattering photometer with optical microscope for in-line studies during polymer processing is described. The instrument utilizes a commercial twinscrew extruding device that feeds molten polymer into a narrow slit die equipped with sapphire optical windows. The flow rate through the die is controlled via the feed rate, screw rate, and a by-pass valve. The optics for light scattering and microscopy can be switched back and forth via simple translation of a rail-mounted optical platform without realignment, allowing efficient in situ morphological studies in both real and reciprocal space. Extruded polystyrene/polyethylene blends and melts are used to demonstrate the performance and versatility of the instrument.
Bayesian image restoration and segmentation by constrained optimization

A constrained optimization method, called the Lagrange-Hopfield (LH) method, is presented for solving Markov random field (MRF) based Bayesian image estimation problems for restoration and segmentation. The method combines the augmented Lagrangian multiplier technique with the Hopfield network to solve a constrained optimization problem into which the original Bayesian estimation problem is reformulated. The LH method effectively overcomes instabilities that are inherent in the penalty method (e.g. Hopfield network) or the Lagrange multiplier method in constrained optimization. An additional advantage of the LH method is its suitability for neural-like analog implementation. Experimental results are presented which show that LH yields good quality solutions at reasonable computational costs.
Improving convergence and solution quality of Hopfield-type neural networks with augmented Lagrange multipliers

Hopfield-type networks convert a combinatorial optimization to a constrained real optimization and solve the latter using the penalty method. There is a dilemma with such networks: when tuned to produce good-quality solutions, they can fail to converge to valid solutions; and when tuned to converge, they tend to give low-quality solutions. This paper proposes a new method, called the augmented Lagrange-Hopfield (ALH) method, to improve Hopfield-type neural networks in both the convergence and the solution quality in solving combinatorial optimization. It uses the augmented Lagrange method, which combines both the Lagrange and the penalty methods, to effectively solve the dilemma. Experimental results on the travelling salesman problem (TSP) show superiority of the ALH method over the existing Hopfield-type neural networks in the convergence and solution quality. For the ten-city TSPs, ALH finds the known optimal tour with 100% success rate, as the result of 1000 runs with different random initializations. For larger size problems, it also finds remarkably better solutions than the compared methods while always converging to valid tours.
A Fast and Robust Approach for Document Segmentation and Classification

We report in this paper a robust implementation of document segmentation and classification. In this algorithm, top-down approach is adopted due to its fast processing speed and reliability. In addition, the approach accounts for every portion of the processed page and it is also capable of image display a t different scale and allowing editing of classified blocks such as changing, merging and splitting of blocks. In classification stage, relevant attributes extracted out from t,he segmentation process are utilised. The algorithm was tested on a number of sample document images and the results were satisfactory.
Discontinuity-adaptive Mrf Prior and Robust Statistics: a Comparative Study

Discontinuity adaptive MRF priors have been used for modeling vision problems involving discontinuities and robust statistics models for solving regression problems involving outliers. This paper presents a comparative study of the two kinds of models. We analyze the mechanisms of adaptation (to discontinuities) and robustness (to outliers) and give a necessary condition for the adaptation and the robustness. We then give a common deenition of both models. The deenition captures the essence of the adaptation ability and gives in theory innnitely many choices of functions suitable for the adaptation in MRF and robust models. The likeness between the two models suggests that results in the two areas are interchangeable to beneet each other.
On Discontinuity-Adaptive Smoothness Priors in Computer Vision

A variety of analytic and probabilistic models in connection with Markov random fields (MRFs) have been proposed in the last decade for solving low level vision problems involving discontinuities. This paper presents a systematic study of these models and defines a general discontinuity adaptive (DA) MRF model. By analyzing the Euler equation associated with the energy minimization, it shows that the fundamental difference between different models lies in the behavior of interaction between neighboring points, which is determined by the a priori smoothness constraint encoded into the energy function. An important necessary condition is derived for the interaction to be adaptive to discontinuities to avoid oversmoothing. This forms the basis on which a class of adaptive interaction functions (AIFs) is defined. The DA model is defined in terms of the Euler equation constrained by this class of AIFs. Its solution is C/sup 1/ continuous and allows arbitrarily large but bounded slopes in dealing with discontinuities. Because of the continuous nature, it is stable to changes in parameters and data, a good property for regularizing ill-posed problems. Experimental results are shown.
Markov Random Field Modeling in Computer Vision

From the Publisher: 
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition, and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Parallel Distributed Relaxation Labeling

A parallel distributed relaxation labeling (RL) method, called the Lagrange-Hopfield (LH) method, is presented. RL is treated as a constrained optimization problem. The LH method solves the problem using the augmented Lagrangian multiplier technique and the graded Hopfield network. The LH method effectively overcomes instabilities that are inherent in the penalty method (e.g. Hopfield network) or the Lagrange multiplier method in constrained optimization. Due to the use of Lagrangian multipliers, the normalization operation in traditional RL methods is dispensed with. This makes the LH algorithm fully parallel and distributed and is suitable for analog implementation. Experiments also show that the method is able to produce good solutions in terms of the optimized objective values.
Convex MRF potential functions

A general definition of convex potential functions is given for discontinuity-preserving MRF restoration models. This gives a class of Bayesian (image) MRF models which satisfy several desirable analytical and computational properties for regularization of ill-posed problems. The relationship between potential functions in MRF models and their discontinuity-preserving property is discussed and an important guideline is derived for devising potential functions in MRF models to be adaptive to discontinuities.
RTP Payload Format for MIDI

This memo describes a Real-time Transport Protocol (RTP) payload
format for the MIDI (Musical Instrument Digital Interface) command
language. The format encodes all commands that may legally appear on a
MIDI 1.0 DIN cable. The format is suitable for interactive
applications (such as network musical performance) and content-
delivery applications (such as file streaming). The format may be used
over unicast and multicast UDP and TCP, and it defines tools for
graceful recovery from packet loss. Stream behavior, including the
MIDI rendering method, may be customized during session setup. The
format also serves as a mode for the mpeg4-generic format, to support
the MPEG 4 Audio Object Types for General MIDI, Downloadable Sounds
Level 2, and Structured Audio.