gebhart/patrepeval huggingface dataset:
Vertebrae Transmission Probe Testing - Preliminary Bone Measurements (2023)

We are developing a new transmission-based probe for assessing vertebrae strength via the differences between the dielectric properties of normal and osteoporotic bone. The probes are simple open-ended coaxial cables that will fit easily into the pedicle canals drilled by the surgeons before implanting screws to secure the supporting instrumentation. If the screws pull because of bone weakness, it can inflict a wide range of complications. In the progression towards actual clinical trials, we have already validated the approach on different liquids with varying dielectric properties. In this study, we assess actual animal bone samples as a precursor to human investigations. We exploit the fact that the trabecular bone is considerably different on opposite sides of the growth plate of long bones such as the femur - somewhat mimicking differences between normal and osteoporotic bone. For these experiments, we show that the properties of the distal and proximal sections are considerably different and present opportunities to exploit them in a diagnostic setting.
Correction: Corrigendum: Rapid generation of hypomorphic mutations (2017)

Nature Communications 8: Article number: 14112 (2017); Published: 20 January 2017; Updated: 16 February 2017 The original version of this Article contained a typographical error in the spelling of the Author Preetam Janakirama, which was incorrectly given as Preetam Jankirama. This has now been corrected in both the PDF and HTML versions of the Article.
P1–030: Analysis of gene expression in brains of tg–APP–ArcSwe mice, a model for Alzheimer's disease (2006)

We have developed APP transgenic mice with both the Swedish and Arctic mutations expressed exclusively in the brain (tg–APP–ArcSwe). Interestingly, these mice develop strong intraneuronal Aβ aggregation already at one month of age, well before extracellular plaque formation at five to six months of age. Moreover, data strongly suggest that the intraneuronally deposited Aβ is soluble, as opposed to the insoluble Aβ found in extracellular deposits. Several recent studies have demonstrated that it might be the smaller, soluble Aβ–aggregates that are neurotoxic. Intraneuronal Aβ aggregation may therefore represent a crucial pathogenic feature in early stages of Alzheimer's disease. To investigate potential transcriptional changes associated with early amyloid aggregation, we have performed microarray expression analyses on tg–APP–ArcSwe mouse brain. By comparing levels of different mRNA transcripts in brain from four–to–five–month old tg–APP–ArcSwe mice with nontransgenic mice, we may be able to reveal new pathways involved in the early response to Aβ aggregation. The identification of such pathways would increase our understanding of the mechanisms for Aβ aggregation and the early stages of Alzheimer's disease. To investigate what effects early intraneuronal Aβ aggregation may have on gene expression in the brains of tg–APP–ArcSwe. Eight tg–APP–ArcSwe mice and eight control mice, all four to five months old, were included. Individual total RNA samples from forebrains were hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays. Robust Multichip Average (RMA) expression values were obtained from six arrays per group and tested for differential expression. Selected candidate genes will be confirmed by quantitative PCR, using the SYBRgreen system. Two–sample t–test identified 95 differentially expressed candidate genes with p<0.01. Notably, expression fold changes were very modest, ranging from 88–122 %. The presence of soluble intraneuronal Aβ in young tg–APP–ArcSwe mice provides us with a model to study transcriptional alterations in early stages of disease. Our ongoing studies on this model may reveal new candidate genes involved in early Aβ aggregation.
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale (2021)

Tremendous success of machine learning (ML) and the unabated growth in ML model complexity motivated many ML-specific designs in both CPU and accelerator architectures to speed up the model inference. While these architectures are diverse, highly optimized low-precision arithmetic is a component shared by most. Impressive compute throughputs are indeed often exhibited by these architectures on benchmark ML models. Nevertheless, production models such as recommendation systems important to Facebook's personalization services are demanding and complex: These systems must serve billions of users per month responsively with low latency while maintaining high prediction accuracy, notwithstanding computations with many tens of billions parameters per inference. Do these low-precision architectures work well with our production recommendation systems? They do. But not without significant effort. We share in this paper our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the design and development of tool chain so as to maintain our models' accuracy throughout their lifespan during which topic trends and users' interests inevitably evolve. Practicing these low-precision technologies helped us save datacenter capacities while deploying models with up to 5X complexity that would otherwise not be deployed on traditional general-purpose CPUs. We believe these lessons from the trenches promote better co-design between hardware architecture and software engineering and advance the state of the art of ML in industry.
Method for Unknown Vapor Characterization and Classification Using a Multivariate Sorption Detector. Initial Derivation and Modeling Based on Polymer-Coated Acoustic Wave Sensor Arrays and Linear Solvation Energy Relationships (1999)

A novel method for the characterization and classification of unknown vapors based on the response on an array of polymer-coated acoustic wave vapor sensors is presented. Unlike existing classification algorithms, the method does not require that the system be trained on all samples to be identified. Instead, the solvation parameters of the unknown vapor are estimated given the sensor responses and the linear solvation energy relationship coefficients of the sorbent polymer coatings. The vapors can then be identified from a database of candidate vapor parameters. The method is implemented in a way that is analogous to multivariate calibration with classical least squares, where the individual vapor parameters are treated as pure compounds. It is not necessary to know the vapor concentration of the vapor to perform the classification. In principle, it is possible to estimate the concentration of an unknown vapor for which the system has not been trained or calibrated. It is also possible to implement the method using inverse least-squares models, based on training samples. This new method for characterizing and classifying unknown compounds based on the responses of a multivariate sorption detector is demonstrated with synthetic data.
Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions (2021)

This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial network (GAN) with marker-controlled watershed segmentation scheme. The GAN model performed RBC segmentations and classifications to develop ageing markers, and the watershed segmentation was used to completely separate overlapping RBCs. Our approach achieved good segmentation and classification accuracy with a Dice's coefficient of 0.94 at a high throughput rate of about 152 cells per second. These results were compared with other deep neural network architectures. Moreover, our image-based deep learning models recognized the morphological changes that occur in RBCs during storage. Our deep learning-based classification results were in good agreement with previous findings on the changes in RBC markers (dominant shapes) affected by storage duration. We believe that our image-based deep learning models can be useful for automated assessment of RBC quality, storage lesions for safe transfusions, and diagnosis of RBC-related diseases.
Practical implementation of congestion cluster pricing method (2001)

The paper describes a practical approach to implementing the congestion cluster pricing method as a viable congestion management system (CMS) in the operation of electric power systems. The congestion cluster pricing method is formulated as a stochastic optimization problem of the cluster design. In the formulation the performance of the pricing method is introduced as a measurable function of cluster design, based on the conceptual criteria necessary for an effective CMS. We define the search space from which a particular design may be selected. Following the formulation the stochastic elements to the optimization problem are discussed by developing suitable representation of various uncertainties in the system. It is shown that the complexity of the problem leads to the search based methods as the preferred option for solving for the optimal cluster design. Finally, some reasonable approximations are suggested to solving the problem thus making it a practical approach to implementing the congestion cluster pricing method. A numerical example is given to illustrate the proposition.
Recombinant baculoviruses for insect control (2001)

Baculoviruses are double-stranded DNA viruses which are highly selective for several insect groups. They are valuable natural control agents, but their utility in many agricultural applications has been limited by their slow speed of kill and narrow host specificity. Baculoviruses have been genetically modified to express foreign genes under powerful promoters in order to accelerate their speed of kill. In our and other laboratories, the expression of genes coding for insect juvenile hormone esterases and various peptide neurotoxins has resulted in recombinant baculoviruses with promise as biological insecticides. These viruses are efficacious in the laboratory, greenhouse and field and dramatically reduce damage caused by insect feeding. The recombinant viruses synergize and are synergized by classical pesticides such as pyrethroids. Since they are highly selective for pest insects, they can be used without disrupting biological control. Because the recombinant virus produces fewer progeny in infected larvae than the wild-type virus, they are rapidly out-competed in the ecosystem. The viruses can be used effectively with crops expressing endotoxins of Bacillus thuringiensis. They can be produced industrially but also by village industries, indicating that they have the potential to deliver sustainable pest control in developing countries. It remains to be seen, however, whether the current generation of recombinant baculoviruses will be competitive with the new generation of synthetic chemical pesticides. Current research clearly indicates, though, that the use of biological vectors of genes for insect control will find a place in agriculture. Baculoviruses will also prove valuable in testing the potential utility of proteins and peptides for insect control.
Binding Profiles of a Series of 2-Arylpropionic Acid Esters on Cloned Human Muscarinic Receptor Subtypes (m1-m5) and Their Relationship to Nootropic Activity (2011)

The muscarinic binding profile of a series of 2-arylpropionic acid esters on cloned human muscarinic receptor subtypes (m1-m5) was determined to investigate whether there is a correlation between pharmacological activity and muscarinic receptor subtype selectivity. Among the tested compounds, 1, 7 and 9 showed the highest affinity for the m2 and m4 receptors. Compounds 1, 7 and 9 show good affinity for m4 receptors (pKi = 7.87; 7.73 and 7.10, respectively) and are able to discriminate 10-60 fold between m4/m1, m4/m3, and m4/m5 subtypes. Conversely, these compounds are able only to weakly discriminate between m4/m2. Compounds 1 (50-300 micrograms kg-1 i.p.) and 7 (1-10 micrograms kg-1 i.p.), injected 20 min before the training session, are able to prevent the amnesia induced by dicyclomine (2 mg kg-1 i.p.) in the mouse passive-avoidance test. Compounds 1 and 7, at the highest antiamnesic doses, do not modify motor coordination and spontaneous motility as evaluated by the rota-rod test and Animex apparatus experiments.
Distributed Quantization for Measurement of Correlated Sparse Sources over Noisy Channels (2014)

In this paper, we design and analyze distributed vector quantization (VQ) for compressed measurements of correlated sparse sources over noisy channels. Inspired by the framework of compressed sensing (CS) for acquiring compressed measurements of the sparse sources, we develop optimized quantization schemes that enable distributed encoding and transmission of CS measurements over noisy channels followed by joint decoding at a decoder. The optimality is addressed with respect to minimizing the sum of mean-square error (MSE) distortions between the sparse sources and their reconstruction vectors at the decoder. We propose a VQ encoder-decoder design via an iterative algorithm, and derive a lower-bound on the end-to-end MSE of the studied distributed system. Through several simulation studies, we evaluate the performance of the proposed distributed scheme.
Internet-based telemedicine: an experimental study to provide decision support using real-time assessment of video quality (2006)

Telemedicine and related healthcare technologies aim to provide efficient health services to improve the well being of patients and bring medical expertise at a lower cost to individuals who are geographically separated by distance. The goal of this dissertation was three-fold: (1) to provide a classification framework for telemedicine/telehealth programs, (2) to develop new quality models/measures that are suitable for telemedicine applications over the Internet, and (3) to apply these models in a prototype that can provide real-time quality information to the users at the point of service. 
Telemedicine taxonomy was developed as a framework to guide program planners and users of telemedicine during the planning, implementation, and evaluation of their programs. Telemedicine has various dimensions that require expertise in different domains. Relationships that occur between different dimensions in the existing pool of telemedicine programs were identified and reported. 
Telemedicine has been quite slow in utilizing Internet technologies because of quality concerns. This study investigated the relationship between objective and subjective quality measures, as well as clinical decision making capability that is affected by the impairments that occur during the transmission of video over the Internet. Existing quality measures were tested on degraded videos generated over an emulated Internet testbed using NISTNet. The original telemedicine video sequence selected for the experiments was a telediagnosis case in ophthalmology (this is a video recording of a general eye examination conducted by a physician from a remote location) obtained from the Regenstrief Institute for Health Care. A new clinical decision making capability scale was also introduced and linked to the traditional objective and subjective values. Findings regarding the relationship between different telemedicine video quality measurements were reported. For example, experts were sometimes able to make clinical decisions with low objective quality scores because the impairment in the video occurred at non-critical frames. The results of the quality measurements were stored in a quality database. This database was utilized in a prototype decision support tool that predicts quality and decision making capability based on the previously collected data. Such tools will be very useful for practitioners during telemedicine sessions.
Inteligência Artificial e Função como Serviço: Provisionando Aplicações com o AWS Lambda (2022)

Neste livro estão compilados os seis minicursos apresentados durante o XXIII Simpósio em Sistemas Computacionais de Alto Desempenho (WSCAD 2022), realizado entre os dias 19 e 21 de outubro de 2022 em Florianópolis, SC. Em todos os minicursos destaca-se o viés prático que incentiva os participantes e os leitores a utilizarem alto desempenho de maneira efetiva. O capítulo 1 "OneAPI: Uma Abordagem para a Computação Heterogênea Centrada no Desenvolvedor", apresenta o padrão OneAPI, que é um padrão aberto para a aceleração de computação que adota uma abordagem single-source, na qual todo o código da aplicação pode ser especificado uniformemente usando a linguagem C++, independente de executar no host ou em aceleradores. O capítulo 2 "Inteligência Artificial e Função como Serviço: Provisionando Aplicações com o AWS Lambda", apresenta o AWS Lambda, que é uma das principais ferramentas do chamado serviço de computação sem servidor. Dentro desse contexto, são introduzidos os conceitos de Função como Serviço (FaaS), englobando o uso de AWS Lambda para provisionar aplicações de AI. O capítulo 3 "Coisas para Fazer antes de Paralelizar", apresenta práticas que devem ser feitas para melhorar o desempenho de um código antes de se pensar em paralelização e revisa conceitos essenciais para a avaliação e comparação de programas, incluindo exemplos e demonstrações ao vivo de como aplicá-los. O capítulo 4 "Fundamentos de Computação Acelerada com CUDA C/C++", ensina as ferramentas e técnicas fundamentais para acelerar aplicações escritas em linguagens C/C++ para execução em arquiteturas massivamente paralelas com CUDA. O capítulo 5 "From sequence assembly to ancestry testing: HPC challenges for bioinformatics" fornece uma visão introdutória do processo de obtenção até a análise de dados biológicos, mostrando os principais algoritmos e banco de dados em cada etapa e uma discussão sobre as formas de paralelização destes algoritmos e os principais desafios que ainda carecem de uma solução. O capítulo 6, "Ferramentas para Configuração e Gerenciamento de Cluster de Alto Desempenho em Nuvem Pública", apresenta as melhores práticas adotadas pelo NACAD-COPPE/UFRJ para a implantação de um cluster de Alto Desempenho usando uma Nuvem Pública focando no uso de ferramentas para implantar e gerenciar clusters de Computação de Alto Desempenho (HPC) na Nuvem AWS.
Defeating Smart and Reactive Jammers with Unlimited Power (2020)

Among all wireless jammers, dealing with reactive ones is most challenging. This kind of jammer attacks the channel whenever it detects transmission from legitimate radios. With recent advances in self-interference suppression or in-band full-duplex radios, a reactive jammer can jam and simultaneously sense/discern/detect the legitimate transmission. Such a jammer is referred to as a smart reactive jammer. However, all existing solutions, e.g., frequency hopping and rate adaptation, cannot effectively deal with this type of jammer. This is because a smart reactive jammer with sufficient power budget can theoretically jam most, if not all, frequency channels at sufficiently high power. This work proposes to augment the transmitter with an ambient backscatter tag. Specifically, when the jammer attacks the channel, the transmitter deceives it by continuing to transmit data to attract the jammer while the tag backscatters data based on both the jamming signals and active signals from the jammer and transmitter, respectively. However, backscattering signals from multiple radio sources results in a high bit error rate (BER). Thus, we propose to use multiple antennas at the receiver. The theoretical analysis and simulation results show that by using multiple antennas at the receiver, the BER and hence the throughput of the system can be significantly improved. More importantly, we demonstrate that with our proposed solutions, the average throughput increases and the BER decreases when the jammer attacks with higher power levels. We believe that this is the first anti-jamming solution that can cope effectively with a high- or even unlimited-power jammers.
Speed regularization and optimality in word classing (2013)

Word-classing has been used in language modeling for two distinct purposes: to improve the likelihood of the language model, and to improve the runtime speed. In particular, frequency-based heuristics have been proposed to improve the speed of recurrent neural network language models (RNN-LMs). In this paper, we present a dynamic programming algorithm for determining classes in a way that provably minimizes the runtime of the resulting class-based language models. However, we also find that the speed-based methods degrade the perplexity of the language models by 5-10% over traditional likelihood-based classing. We remedy this via the introduction of a speed-based regularization term in the likelihood objective function. This achieves a runtime close to that of the speed based methods without loss in perplexity performance. We demonstrate these improvements with both an RNN-LM and the Model M exponential language model, for three different tasks involving two different languages.
Large‐scale genomic instability in colon adenocarcinomas and correlation with patient outcome (2009)

The purpose of this study was to evaluate the association between DNA content in colon adenocarcinomas using high-resolution image cytometry and patient outcome. Tumours from 219 patients operated for colon adenocarcinoma were analysed using high-resolution image cytometry. Proteins involved in cell cycle propulsion (cyclins A, D1, D3 and E) and cell proliferation (c-Myc and non-membranous beta-catenin) have previously been reported in the same cohort and were included in this study. The results were related to disease-free survival and to cancer-specific death. Patients with aneuploid tumours showed shorter relapse-free survival than patients with euploid tumours (univariate log-rank test, p = 0.004 and multivariate Cox regression model p = 0.009, HR 0.51, 95% CI 0.31-0.84). Also the risk of death from cancer was greater in patients with aneuploid tumours (log-rank test, p = 0.006 multivariate Cox regression model p = 0.014, HR 0.47, 95% CI 0.26-0.86). When analysing patients with Dukes stages A and B, nuclear expression of beta-catenin was highly significantly associated with both shorter relapse-free survival (p < 0.005, HR 5.0, 95% CI 1.6-15.5) and cancer-specific death (p = 0.036, HR 6.9, 95% CI 1.1-42.1). DNA content in colon adenocarcinomas measured by image cytometry is an independent predictor of prognosis in our patients operated for colon adenocarcinoma.
Microstructure overlapping image application with optical decryption (2020)

Currently, valuable tickets are scanned and verified by embedding quick response (QR) codes, but few studies have embedded encrypted information into QR codes to prevent counterfeiting. In the existing literature on color image-based QR codes, there is room for improvement in image quality and anti-counterfeiting functions. We propose an optically decrypted microstructure overlapping image technology, hiding the information points of two-dimensional barcodes on the image reservation area in the ticket, and using the optical principle to use the lenticular lens as a decryption component, to make the image produce continuous dynamic effects and increase the difficulty of forgery. In addition, the distribution of embedded parameters and differences will also affect the distortion of hidden information, which may cause the QR code to fail to read, so the median edge detection predictor is proposed for distortion control comparison. The experimental results prove that our method provides low distortion of information-hiding technology.
Coagulation Management in Jersey Calves: An ex vivo Study (2017)

In this ex vivo study, coagulation factors, factor concentrate, antifibrinolytic reagents, and anticoagulants regularly used in the clinic positively impacted coagulation parameters in Jersey calf blood.
Optimization of the piezoelectric response of 0–3 composites: a modeling approach (2011)

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