LLukas22/scidocs huggingface dataset:
Fundamentals of Biomass Pretreatment by Fractionation

With the rise in global energy demand and environmental concerns about the use of fossil fuels, the need for rapid development of alternative fuels from sustainable, non-food sources is now well acknowledged. The effective utilization of low-cost high-volume agricultural and forest biomass for the production of transportation fuels and bio-based materials will play a vital role in addressing this concern [1]. The processing of lignocellulosic biomass, especially from mixed agricultural and forest sources with varying composition, is currently significantly more challenging than the bioconversion of corn starch or cane sugar to ethanol [1,2]. This is due to the inherent recalcitrance of lignocellulosic biomass to enzymatic and microbial deconstruction, imparted by the partly crystalline nature of cellulose and its close association with hemicellulose and lignin in the plant cell wall [2,3]. Pretreatments that convert raw lignocellulosic biomass to a form amenable to enzymatic degradation are therefore an integral step in the production of bioethanol from this material [4]. Chemical or thermochemical pretreatments act to reduce biomass recalcitrance in various ways. These include hemicellulose removal or degradation, lignin modification and/or delignification, reduction in crystallinity and degree of polymerization of cellulose, and increasing pore volume. Biomass pretreatments are an active focus of industrial and academic research efforts, and various strategies have been developed. Among commonly studied pretreatments, organosolv pretreatment, in which an aqueous organic solvent mixture is used as the pretreatment medium, results in the fractionation of the major biomass components, cellulose, lignin, and hemicellulose into three process streams [5,6]. Cellulose and lignin are recovered as separate solid streams, while hemicelluloses and sugar degradation products such as furfural and hydroxymethylfurfural (HMF) are released as a water-soluble fraction. The combination of ethanol as the solvent and
Attention-based Encoder-Decoder Networks for Spelling and Grammatical Error Correction

Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of many language processing problems. We demonstrate that neural machine translation models can be successfully applied to the task of error correction. While the experiments of this research are performed on an Arabic corpus, our methods in this thesis can be easily applied to any language. Keywords— natural language error correction, recurrent neural networks, encoderdecoder models, attention mechanism
An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

In this correspondence we have not addressed the problem of constructing actual codebooks. Information theory indicates that, in principle , one can construct a codebook by drawing each component of each codeword independently, using the distribution obtained from the Blahut algorithm. This procedure is not in general practical. Practical ways to construct codewords may be found in the extensive literature on vector quantization (see, e.g., the tutorial paper by R. M. Gray [19] or the book [20]). It is not clear at this point if codebook constructing methods from the vector quantizer literature are practical in the setting of this correspondence. Alternatively, one can trade complexity and performance and construct a scalar quantizer. In this case, the distribution obtained from the Blahut algorithm may be used in the Max–Lloyd algorithm [21], [22]. Grenander, " Conditional-mean estimation via jump-diffusion processes in multiple target tracking/recog-nition, " IEEE Trans.matic target recognition organized via jump-diffusion algorithms, " IEEE bounds for estimators on matrix lie groups for atr, " IEEE Trans. Abstract—A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity, and discrimination. In this correspondence, we present a new hyperspectral measure, Spectral Information Measure (SIM), to describe spectral variability and two criteria, spectral information divergence and spectral discriminatory probability, for spectral similarity and discrimination, respectively. The spectral information measure is an information-theoretic measure which treats each pixel as a random variable using its spectral signature histogram as the desired probability distribution. Spectral Information Divergence (SID) compares the similarity between two pixels by measuring the probabilistic discrepancy between two corresponding spectral signatures. The spectral discriminatory probability calculates spectral probabilities of a spectral database (library) relative to a pixel to be identified so as to achieve material identification. In order to compare the discriminatory power of one spectral measure relative to another , a criterion is also introduced for performance evaluation, which is based on the power of discriminating one pixel from another relative to a reference pixel. The experimental results demonstrate that the new hyper-spectral measure can characterize spectral variability more effectively than the commonly used Spectral Angle Mapper (SAM).
Mining Chat-room Conversations for Social and Semantic Interactions

The ephemeral nature of human communication via networks today poses interesting and challenging problems for information technologists. The Intelink intelligence network, for example, has a need to monitor chat-room conversations to ensure the integrity of sensitive data being transmitted via the network. However, the sheer volume of communication in venues such as email, newsgroups, and chat precludes manual techniques of information management. It has been estimated that over 430 million instant messages, for example, are exchanged each day on the America Online network [3]. Although a not insignificant fraction of such data may be temporarily archived (e.g., newsgroups), no systematic mechanisms exist for accumulating these artifacts of communication in a form that lends itself to the construction of models of semantics [12]. In essence, dynamic techniques of analysis are needed if textual data of this nature is to be effectively mined. This article reports our progress in developing a text mining tool for analysis of chat-room conversations. Central to our efforts is the development of functionality to answer questions such as "What topics are being discussed in a chat-room?", "Who is discussing which topics?" and "Who is interacting with whom?" The objective of our research is to develop technology that can automatically identify such patterns of interaction in both social and semantic terms. In this article we report our preliminary findings in identifying threads of conversation in multi-topic, multi-person chat-rooms. We have achieved promising results in terms of precision and recall by employing pattern recognition techniques based on finite state automata. We also report the design of our approach to building models of social and semantic interactions based on our HDDI text mining infrastructure [13].
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are a remarkable demonstration of deep reinforcement learning’s capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available. 1
Considering Games as Cognitive Tools: In Search of Effective "Edutainment"

DOCUMENT RESUME
Two-Handed Sign Language Recognition for Bangla Character Using Normalized Cross Correlation

Sign language detection and recognition (SLDR) using computer vision is a very challenging task. In respect to Bangladesh, sign language users are around 2.4 million [1]. In this paper, we try to focus for communicating with those users by computer vision. In this respect, an efficient method is propose consists of two basic steps: (a) refinement and (b) recognition. Initially in refinement, a Red-Green-Blue (RGB) color model is adopted to select heuristically threshold value for detecting candidate regions (i.e. hand and wrist band sign regions). After the candidate regions are obtained by applying color segmentation, then procedures for refining the candidate region are followed by using two different color wrist band regions and filtering. Finally, statistically based template matching technique is used for recognition of hand sign regions. Various hand sign images are used to test the proposed method and results are presented to provide its effectiveness.
Impact of BCL2 and p53 on postmastectomy radiotherapy response in high-risk breast cancer. A subgroup analysis of DBCG82 b&c.

PURPOSE
To examine p53 and BCL2 expression in high-risk breast cancer patients randomized to postmastectomy radiotherapy (PMRT).


PATIENTS AND METHODS
The present analysis included 1 000 of 3 083 high-risk breast cancer patients randomly assigned to PMRT in the DBCG82 b&c studies. Tissue microarray sections were stained with immunohistochemistry for p53 and BCL2. Median potential follow-up was 17 years. Clinical endpoints were locoregional recurrence (LRR), distant metastases (DM), overall mortality, and overall survival (OS). Statistical analyses included Kappa statistics, chi(2) or exact tests, Kaplan-Meier probability plots, Log-rank test, and Cox univariate and multivariate regression analyses.


RESULTS
p53 accumulation was not significantly associated with increased overall mortality, DM or LRR probability in univariate or multivariate Cox regression analyses. Kaplan-Meier probability plots showed reduced OS and improved DM and LRR probabilities after PMRT within subgroups of both p53 negative and p53 positive patients. Negative BCL2 expression was significantly associated with increased overall mortality, DM and LRR probability in multivariate Cox regression analyses. Kaplan-Meier probability plots showed a significantly improved overall survival after PMRT for the BCL2 positive subgroup, whereas practically no survival improvement was seen after PMRT for the BCL2 negative subgroup. In multivariate analysis of OS, however, no significant interaction was found between BCL2 and randomization status. Significant reductions in LRR probability after PMRT were recorded within both the BCL2 positive and BCL2 negative subgroups.


CONCLUSION
p53 was not associated with survival after radiotherapy in high-risk breast cancer, but BCL2 might be.
Automated de-identification of free-text medical records

BACKGROUND
Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification.


METHODS
We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus.


RESULTS
Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus.


CONCLUSION
We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.
The BBN Byblos Pashto OCR system

The BBN Byblos OCR system implements a script-independent methodology for OCR using Hidden Markov Models (HMMs). We have successfully tested the system with Arabic, English, and Chinese documents. In this paper, we describe our recent effort in training the system to perform recognition of documents in Pashto, one of the national languages of Afghanistan. We discuss the availability and characteristics of suitable experimental data and the methods we used to assemble Pashto training and test corpora. We modeled each form of each Pashto character with an HMM and tested the models on several varieties of document images. On a fair test set consisting of synthetic images alone we measured a character error rate of 1.6%. The character error rate on a fair test set consisting of scanned pages was 2.1%, and the character error rate on a fair test set of faxed pages was 3.1%. On other types of document images, character error rates increased in rough proportion to the level of degradation of the image.
Real-Time Cutting Tool Condition Monitoring in Milling

Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach. © 2011 Journal of Mechanical Engineering. All rights reserved.
A Low-Cost EEG System-Based Hybrid Brain-Computer Interface for Humanoid Robot Navigation and Recognition

This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.
Prolonged intravenous therapy versus early transition to oral antimicrobial therapy for acute osteomyelitis in children.

OBJECTIVES
Early transition from intravenous to oral antimicrobial therapy for acute osteomyelitis in children has been suggested as a safe and effective alternative to traditional prolonged intravenous therapy via central venous catheter, but no studies have directly compared these 2 treatment modalities. We sought to compare the effectiveness of early transition from intravenous to oral antimicrobial therapy versus prolonged intravenous antimicrobial therapy for the treatment of children with acute osteomyelitis.


METHODS
We conducted a retrospective cohort study of children aged 2 months to 17 years diagnosed with acute osteomyelitis between 2000 and 2005 at 29 freestanding children's hospitals in the United States to confirm the extent of variation in the use of early transition to oral therapy. We used propensity scores to adjust for potential differences between children treated with prolonged intravenous therapy and logistic regression to model the association of outcome (treatment failure rates within 6 months of diagnosis) and difference in the mode of therapy within hospitals and across hospitals.


RESULTS
Of the 1969 children who met inclusion criteria, 1021 received prolonged intravenous therapy and 948 received oral therapy. The use of prolonged intravenous therapy varied significantly across hospitals (10%-95%). The treatment failure rate was 5% (54 of 1021) in the prolonged intravenous therapy group and 4% (38 of 948) in the oral therapy group. There was no significant association between treatment failure and the mode of antimicrobial therapy. Thirty-five (3.4%) children in the prolonged intravenous therapy group were readmitted for a catheter-associated complication.


CONCLUSIONS
Treatment of acute osteomyelitis with early transition to oral therapy is not associated with a higher risk of treatment failures and avoids the risks of prolonged intravenous therapy through central venous catheters.
Randomized trial of deep brain stimulation for Parkinson disease: thirty-six-month outcomes.

OBJECTIVES
Our objective was to compare long-term outcomes of deep brain stimulation (DBS) of the globus pallidus interna (GPi) and subthalamic nucleus (STN) for patients with Parkinson disease (PD) in a multicenter randomized controlled trial.


METHODS
Patients randomly assigned to GPi (n = 89) or STN DBS (n = 70) were followed for 36 months. The primary outcome was motor function on stimulation/off medication using the Unified Parkinson's Disease Rating Scale motor subscale. Secondary outcomes included quality of life and neurocognitive function.


RESULTS
Motor function improved between baseline and 36 months for GPi (41.1 to 27.1; 95% confidence interval [CI] -16.4 to -10.8; p < 0.001) and STN (42.5 to 29.7; 95% CI -15.8 to -9.4; p < 0.001); improvements were similar between targets and stable over time (p = 0.59). Health-related quality of life improved at 6 months on all subscales (all p values significant), but improvement diminished over time. Mattis Dementia Rating Scale scores declined faster for STN than GPi patients (p = 0.01); other neurocognitive measures showed gradual decline overall.


CONCLUSIONS
The beneficial effect of DBS on motor function was stable and comparable by target over 36 months. Slight declines in quality of life following initial gains and gradual decline in neurocognitive function likely reflect underlying disease progression and highlight the importance of nonmotor symptoms in determining quality of life.


CLASSIFICATION OF EVIDENCE
This study provides Class III evidence that improvement of motor symptoms of PD by DBS remains stable over 3 years and does not differ by surgical target. Neurology® 2012;79:55-65.