nils-herrmann/clean_scirepeval huggingface dataset:
A Device Model for Analog VLSI Circuits

This paper presents a new formulation of a CMOS device model. The model is primarily intended for analog circuit analysis. Exponential (subthreshold) and square-law (above threshold) behavior is modeled, with a smooth and accurate threshold transition. The new formulation is based on three independent physical process parameters: Na – bulk doping concentration, Qss – fixed oxide charge, and μ0 – effective carrier mobility. The mutual independence greatly simplifies the use of the model for prediction and for understanding the significance of process changes. This contrasts with the present standard practice of using, for example, the threshold voltage, Vt, as a model parameter. A change in Vt must be due to a change in the flatband voltage, which depends on Cox, φms, and Qss, or the Fermi potential which depends on Na and ni. Changes to these parameters in turn imply changes in the value of other model parameters such as β (μ × Cox) or γ (body effect). Thus, it is not physically realistic to change Vt while implicitly holding β or γ constant. This mistake is often made by users of models, such as SPICE, which permit changing Vt directly. The model has a simple closed form and therefore is well-suited to circuit simulation. Its incorporation into the anaLOG circuit simulator is described. Finally, two examples of the use of the model for devices from a 1.2um and a 0.8um process are given. In the course of reviewing the theoretical and empirical origins of the parameters for the standard device models, some anomalies were uncovered. These are discussed, and more accurate parameter values are derived. Some of these more accurate values would likely be useful in other models. By far the most important inaccuracy is the accepted value of the intrinsic carrier concentration in silicon (ni). The widely accepted value (as uniformly used in SPICE models, for example) of ni is 1.45× 1010cm−3 at 300oK. The accurately measured value is 0.99976× 1010. In a typical case, this 45% error in ni from its correct value changes Vt from 0.602 to 0.623. A 20mV change in Vt represents, for a transistor operating in subthreshold, an approximately 70% change in current, and thus can have a significant effect on the behavior of many analog circuits. The accurate value of ni has been known by people working on CCD’s and by people developing silicon-based solar cells, however the information has not been noticed by the CMOS device and circuits community. Quite a lot of time has been spent making “adjustments” for the resulting mismatch between “theory” and observation of device behavior.  2.4 29 April 2014.  First published: July 1997 Current version: 2.4 29 April 2014.
A case for network musical performance

A Network Musical Performance (NMP) occurs when a group of musicians, located at different physical locations, interact over a network to perform as they would if located in the same room. In this paper, we present a case for NMP as a practical Internet application, and describe a method to ameliorate the effect of late and lost packets on NMP. We describe an NMP system that embodies this concept, that combines several existing standards (MIDI, MPEG 4 Structured Audio, RTP/AVP, and SIP) with a new RTP packetization for MIDI performance. We analyze NMP experiments performed on CalREN2 hosts on the UC Berkeley, Stanford, and Caltech campuses.
Compiling MPEG 4 structured audio into C

Structured audio (SA) is an MPEG 4 audio standard for algorithmic sound encoding, using the programming language SAOL. The paper describes a SA decoder, sfront, that translates a SAOL program into a C program, which is then compiled and executed to create audio. Performance data shows a 7.6/spl times/ to 20.4/spl times/ speedup compared to the SA reference MPEG decoder.
JPEG Quality Transcoding Using Neural Networks Trained with a Perceptual Error Measure

A JPEG Quality Transcoder (JQT) converts a JPEG image file that was encoded with low image quality to a larger JPEG image file with reduced visual artifacts, without access to the original uncompressed image. In this article, we describe technology for JQT design that takes a pattern recognition approach to the problem, using a database of images to train statistical models of the artifacts introduced through JPEG compression. In the training procedure for these models, we use a model of human visual perception as an error measure. Our current prototype system removes 32.2 of the artifacts introduced by moderate compression, as measured on an independent test database of linearly coded images using a perceptual error metric. This improvement results in an average PSNR reduction of 0.634 dB.
A micropower analog circuit implementation of hidden Markov model state decoding

We describe the implementation of a hidden Markov model state decoding system, a component for a wordspotting speech recognition system. The key specification for this state decoder design is microwatt power dissipation: this requirement led to a continuous-time, analog circuit implementation. We describe the tradeoffs inherent in the choice of an analog design and explain the mapping of the discrete-time state decoding algorithm into the continuous domain. We characterize the operation of a ten-word (81-state) state decoder test chip.
A Micropower Analog VLSI HMM State Decoder for Wordspotting

We describe the implementation of a hidden Markov model state decoding system, a component for a wordspotting speech recognition system. The key specification for this state decoder design is microwatt power dissipation; this requirement led to a continuous-time, analog circuit implementation. We characterize the operation of a 10-word (81 state) state decoder test chip.
Silicon Models for Auditory Scene Analysis

We are developing special-purpose, low-power analog-to-digital converters for speech and music applications, that feature analog circuit models of biological audition to process the audio signal before conversion. This paper describes our most recent converter design, and a working system that uses several copies of the chip to compute multiple representations of sound from an analog input. This multi-representation system demonstrates the plausibility of inexpensively implementing an auditory scene analysis approach to sound processing.
Efficient geometric algorithms for parsing in two dimensions

Grammars are a powerful technique for modeling and extracting the structure of documents. One large challenge, however, is computational complexity. The computational cost of grammatical parsing is related to both the complexity of the input and the ambiguity of the grammar. For programming languages, where the terminals appear in a linear sequence and the grammar is unambiguous, parsing is O(N). For natural languages, which are linear yet have an ambiguous grammar, parsing is O(N/sup 3/). For documents, where the terminals are arranged in two dimensions and the grammar is ambiguous, parsing time can be exponential in the number of terminals. In this paper we introduce (and unify) several types of geometrical data structures which can be used to significantly accelerate parsing time. Each data structure embodies a different geometrical constraint on the set of possible valid parses. These data structures are very general, in that they can be used by any type of grammatical model, and a wide variety of document understanding tasks, to limit the set of hypotheses examined and tested. Assuming a clean design for the parsing software, the same parsing framework can be tested with various geometric constraints to determine the most effective combination.
Detecting Pedestrians Using Patterns of Motion and Appearance

This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on motion information or detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20 × 15 pixels), and has a very low false positive rate.Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: (i) development of a representation of image motion which is extremely efficient, and (ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow).
Multiple Instance Boosting for Object Detection

A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MIL-Boost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost. This increased detection rate shows the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier.
Optical Character Recognition low-resolution camera for documents acquired

A system that facilitates optical character recognition, OCR, symbol low resolution, in which a string of symbols is representative of a word, and in which the symbols represent characters, comprising: a component segmentation to detect spaces between symbols to determine lines of text, and fragmenting the text lines into individual words; and a recognition component for recognizing characters (206) using a character recognizer based on machine learning to scan through each of the individual words to predict what character is likely to occur at a given location, to recognize the punctuation and word recognition; in said recognizing punctuation is used to identify whether a final character of a word is a punctuation mark, comprising: determining a most likely position for each possible final character of the word character; generate a score for each character more likely; determine whether the word is punctuated word, in which the word is punctuated word if the most likely character with the highest score is a punctuation mark and if the score of the most likely character with the highest score is above a predetermined threshold; and wherein said word recognition includes: recognize the word using the rest of the word without punctuation, and add punctuation to the recognized word; and recognizing words (208) a sequence of individual reconciling character recognizer outputs with a particular word using dynamic programming and a dictionary.
Learning silhouette features for control of human motion

We present a vision-based performance interface for controlling animated human characters. The system interactively combines information about the user's motion contained in silhouettes from three viewpoints with domain knowledge contained in a motion capture database to produce an animation of high quality. Such an interactive system might be useful for authoring, for teleconferencing, or as a control interface for a character in a game. In our implementation, the user performs in front of three video cameras; the resulting silhouettes are used to estimate his orientation and body configuration based on a set of discriminative local features. Those features are selected by a machine-learning algorithm during a preprocessing step. Sequences of motions that approximate the user's actions are extracted from the motion database and scaled in time to match the speed of the user's motion. We use swing dancing, a complex human motion, to demonstrate the effectiveness of our approach. We compare our results to those obtained with a set of global features, Hu moments, and ground truth measurements from a motion capture system.
Learning to extract information from semi-structured text using a discriminative context free grammar

In recent work, conditional Markov chain models (CMM) have been used to extract information from semi-structured text (one example is the Conditional Random Field ). Applications range from finding the author and title in research papers to finding the phone number and street address in a web page. The CMM framework combines a priori knowledge encoded as features with a set of labeled training data to learn an efficient extraction process. We will show that similar problems can be solved more effectively by learning a discriminative context free grammar from training data. The grammar has several distinct advantages: long range, even global, constraints can be used to disambiguate entity labels; training data is used more efficiently; and a set of new more powerful features can be introduced. The grammar based approach also results in semantic information (encoded in the form of a parse tree) which could be used for IR applications like question answering. The specific problem we consider is of extracting personal contact, or address, information from unstructured sources such as documents and emails. While linear-chain CMMs perform reasonably well on this task, we show that a statistical parsing approach results in a 50% reduction in error rate. This system also has the advantage of being interactive, similar to the system described in . In cases where there are multiple errors, a single user correction can be propagated to correct multiple errors automatically. Using a discriminatively trained grammar, 93.71% of all tokens are labeled correctly (compared to 88.43% for a CMM) and 72.87% of records have all tokens labeled correctly (compared to 45.29% for the CMM).
Text recognition of low-resolution document images

Cheap and versatile cameras make it possible to easily and quickly capture a wide variety of documents. However, low resolution cameras present a challenge to OCR because it is virtually impossible to do character segmentation independently from recognition. In this paper we solve these problems simultaneously by applying methods borrowed from cursive handwriting recognition. To achieve maximum robustness, we use a machine learning approach based on a convolutional neural network. When our system is combined with a language model using dynamic programming, the overall performance is in the vicinity of 80-95% word accuracy on pages captured with a 1024/spl times/768 webcam and 10-point text.
Learning nongenerative grammatical models for document analysis

We present a general approach for the hierarchical segmentation and labeling of document layout structures. This approach models document layout as a grammar and performs a global search for the optimal parse based on a grammatical cost function. Our contribution is to utilize machine learning to discriminatively select features and set all parameters in the parsing process. Therefore, and unlike many other approaches for layout analysis, ours can easily adapt itself to a variety of document analysis problems. One need only specify the page grammar and provide a set of correctly labeled pages. We apply this technique to two document image analysis tasks: page layout structure extraction and mathematical expression interpretation. Experiments demonstrate that the learned grammars can be used to extract the document structure in 57 files from the UWIII document image database. We also show that the same framework can be used to automatically interpret printed mathematical expressions so as to recreate the original LaTeX
Spatial recognition and grouping of text and graphics

We present a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagrams. The approach is completely spatial, that is it does not require any ordering on the strokes. It also does not place any constraint on the relative placement of the shapes or symbols. Initially each of the strokes on the page is linked in a proximity graph. A discriminative classifier is used to classify connected subgraphs as either making up one of the known symbols or perhaps as an invalid combination of strokes (e.g. including strokes from two different symbols). This classifier combines the rendered image of the strokes with stroke features such as curvature and endpoints. A small subset of very efficient features is selected, yielding an extremely fast classifier. An A-star search algorithm over connected subsets of the proximity graph is used to simultaneously find the optimal segmentation and recognition of all the strokes on the page. Experiments demonstrate that the system can achieve 97% segmentation/recognition accuracy on a cross-validated shape dataset from 19 different writers.
Learning to parse hierarchical lists and outlines using conditional random fields

Handwritten notes are complex structures, which include blocks of text, drawings, and annotations. The main challenge for the newly emerging tablet computer is to provide high-level tools for editing and authoring handwritten documents using a natural interface. One frequent component of natural notes are lists and hierarchical outlines, which correspond directly to the bulleted lists and itemized structures in conventional text, editing tools. We present a system, which automatically recognizes lists and hierarchical outlines in handwritten notes, and then computes the correct structure. This inferred structure provides the foundation for new user interfaces and facilitates the importation of handwritten notes into conventional editing tools.
Recognition and grouping of handwritten text in diagrams and equations

We present a framework for grouping and recognition of characters and symbols in online free-form ink expressions. The approach is completely spatial; it does not require any ordering on the strokes. It also does not place any constraints on the layout of the symbols. Initially each of the strokes on the page is linked in a proximity graph. A discriminative recognizer is used to classify connected subgraphs as either making up one of the known symbols or perhaps as an invalid combination of strokes (e.g. including strokes from two different symbols). This recognizer operates on the rendered image of the strokes plus stroke features such as curvature and endpoints. A small subset of very efficient image features is selected, yielding an extremely fast recognizer. Dynamic programming over connected subsets of the proximity graph is used to simultaneously find the optimal grouping and recognition of all the strokes on the page. Experiments demonstrate that the system can achieve 94% grouping/recognition accuracy on a test dataset containing symbols from 25 writers held out from the training process.
Alignment by Maximization of Mutual Information

A new information-theoretic approach is presented for finding the pose of an object in an image. The technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. In our derivation few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and may foreseeably be used in a wide variety of imaging situations.Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images, aligning a complex 3D object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2D object model to real images.The method is based on a formulation of the mutual information between the model and the image. As applied here the technique is intensity-based, rather than feature-based. It works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation. Additionally, it has an efficient implementation that is based on stochastic approximation.
Robust Real-Time Face Detection

This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.
Boosting Image Retrieval

We present an approach for image retrieval using a very large number of highly selective features and efficient learning of queries. Our approach is predicated on the assumption that each image is generated by a sparse set of visual “causes” and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 46,000 highly selective features). At query time a user selects a few example images, and the AdaBoost algorithm is used to learn a classification function which depends on a small number of the most appropriate features. This yields a highly efficient classification function. In addition we show that the AdaBoost framework provides a natural mechanism for the incorporation of relevance feedback. Finally we show results on a wide variety of image queries.
Automatic Fax Routing

We present a system for automatic FAX routing which processes incoming FAX images and forwards them to the correct email alias. The system first performs optical character recognition to find words and in some cases parts of words (we have observed error rates as high as 10 to 20 percent). For all these “noisy” words, a set of features is computed which include internal text features, location features, and relationship features. These features are combined to estimate the relevance of the word in the context of the page and the recipient database. The parameters of the word relevance function are learned from training data using the AdaBoost learning algorithm. Words are then compared to the database of recipients to find likely matches. The recipients are finally ranked by combining the quality of the matches and the relevance of the words. Experiments are presented which demonstrate the effectiveness of this system on a large set of real data.