gebhart/patrepeval huggingface dataset:
Particle swarm optimization for programming deep brain stimulation arrays (2017)

OBJECTIVE: Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. APPROACH: Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. MAIN RESULTS: The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. SIGNIFICANCE: The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.
Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs (2015)

At the atomic-cluster scale, pure boron is markedly similar to carbon, forming simple planar molecules and cage-like fullerenes. Theoretical studies predict that two-dimensional (2D) boron sheets will adopt an atomic configuration similar to that of boron atomic clusters. We synthesized atomically thin, crystalline 2D boron sheets (i.e., borophene) on silver surfaces under ultrahigh-vacuum conditions. Atomic-scale characterization, supported by theoretical calculations, revealed structures reminiscent of fused boron clusters with multiple scales of anisotropic, out-of-plane buckling. Unlike bulk boron allotropes, borophene shows metallic characteristics that are consistent with predictions of a highly anisotropic, 2D metal.
H<sub>2</sub>S Scavenging Capacity and Rheological Properties of Water-Based Drilling Muds (2020)

Drilling hydrocarbon formations where hydrogen sulfide (H<sub>2</sub>S) is present could lead to the carryover of H<sub>2</sub>S with the drilling mud (i.e., drilling fluid) to the surface, exposing working personnel to this lethal gas. Additionally, H<sub>2</sub>S is very corrosive, causing severe corrosion of metal parts of the drilling equipment, which in turn results in serious operational problems. The addition of an effective H<sub>2</sub>S scavenger(s) in the drilling mud formulations will overcome these health, safety, and operational issues. In this work, zinc oxide (ZnO), which is a common H<sub>2</sub>S scavenger, has been incorporated into water-based drilling mud. The H<sub>2</sub>S scavenging performance of this ZnO-containing drilling mud has been assessed. Additionally, drilling mud formulations containing either copper nitrate (Cu(NO<sub>3</sub>)<sub>2</sub>·3H<sub>2</sub>O) or potassium permanganate (KMnO<sub>4</sub>) have been prepared, and their H<sub>2</sub>S scavenging performances have been studied and compared to that of the ZnO-containing drilling mud. It has been observed that the scavenging performance (in terms of the H<sub>2</sub>S amounts scavenged up to the breakthrough time and at the saturation condition) of the ZnO-containing drilling mud is very poor compared to those of the copper nitrate-containing and KMnO<sub>4</sub>-containing drilling muds. For instance, the amounts of H<sub>2</sub>S scavenged up to the breakthrough time by ZnO-containing, copper nitrate-containing, and KMnO<sub>4</sub>-containing drilling muds were 5.5, 15.8, and 125.3 mg/g, respectively. Furthermore, the amounts of H<sub>2</sub>S scavenged at the saturation condition by these drilling muds were, respectively, 35.1, 146.8, and 307.5 mg/g, demonstrating the superiority of the KMnO<sub>4</sub>-containing drilling mud. Besides its attractive H<sub>2</sub>S scavenging performance, the KMnO<sub>4</sub>-containing drilling mud possessed more favorable rheological properties [i.e., plastic viscosity (PV), yield point (YP), carrying capacity of the drill cuttings, and gelling characteristics] relative to the base and the ZnO-containing and copper nitrate-containing drilling muds. The addition of KMnO<sub>4</sub> to the base drilling mud increased its apparent viscosity, PV, and YP by 20, 33, and 10%, respectively. Additionally, all tested drilling muds possessed acceptable fluid loss characteristics. To the best of our knowledge, there are so far no published studies concurrently tackling the H<sub>2</sub>S scavenging (i.e., breakthrough time, breakthrough capacity, saturation time, saturation capacity, and scavenger utilization) and the rheological properties of water-based drilling muds, as demonstrated in the current study, highlighting the novelty of this work.
Combining Human Organoids and Organ-on-a-Chip Technology to Model Intestinal Region-Specific Functionality (2022)

The intestinal mucosa is a complex physical and biochemical barrier that fulfills a myriad of important functions. It enables the transport, absorption, and metabolism of nutrients and xenobiotics while facilitating a symbiotic relationship with microbiota and restricting the invasion of microorganisms. Functional interaction between various cell types and their physical and biochemical environment is vital to establish and maintain intestinal tissue homeostasis. Modeling these complex interactions and integrated intestinal physiology in vitro is a formidable goal with the potential to transform the way new therapeutic targets and drug candidates are discovered and developed. Organoids and Organ-on-a-Chip technologies have recently been combined to generate human-relevant intestine chips suitable for studying the functional aspects of intestinal physiology and pathophysiology in vitro. Organoids derived from the biopsies of the small (duodenum) and large intestine are seeded into the top compartment of an organ chip and then successfully expand as monolayers while preserving the distinct cellular, molecular, and functional features of each intestinal region. Human intestine tissue-specific microvascular endothelial cells are incorporated in the bottom compartment of the organ chip to recreate the epithelial-endothelial interface. This novel platform facilitates luminal exposure to nutrients, drugs, and microorganisms, enabling studies of intestinal transport, permeability, and host-microbe interactions. Here, a detailed protocol is provided for the establishment of intestine chips representing the human duodenum (duodenum chip) and colon (colon chip), and their subsequent culture under continuous flow and peristalsis-like deformations. We demonstrate methods for assessing drug metabolism and CYP3A4 induction in duodenum chip using prototypical inducers and substrates. Lastly, we provide a step-by-step procedure for the in vitro modeling of interferon gamma (IFNγ)-mediated barrier disruption (leaky gut syndrome) in a colon chip, including methods for evaluating the alteration of paracellular permeability, changes in cytokine secretion, and transcriptomic profiling of the cells within the chip.
High Performance Architecture of an Application Specific Processor for the H.264 Deblocking Filter (2008)

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents an efficient architecture of an application specific processor (ASP) designed for the deblocking filter algorithm of the H.264 video compression standard. Several optimization techniques at different design levels, such as vector register, pipeline processing, very long instruction word (VLIW) processor, and predication, are utilized in this design. The proposed ASP can meet the real time constraint of the deblocking filter algorithm for the 16:9 video format (4690<formula formulatype="inline"><tex Notation="TeX">$\,\times\,$</tex> </formula>2304) at 30 frames per second (fps) at 200-MHz clock rate. </para>
MODELZ: Monitoring, Detection, and Analysis of Energy-Greedy Anomalies in Mobile Handsets (2011)

It is of great importance to protect rapidly-spreading and widely-used small mobile devices like smartphones and PocketPCs from energy-depletion attacks by monitoring software (processes) and hardware (especially, battery) resources. The ability to use these devices for on- and/or off-job functions, and even for medical emergencies or disaster recovery is often dictated by their limited battery capacity. However, traditional malware detection systems and antivirus solutions based on matching signatures are limited to detection of only known malware, and hence, cannot deal with battery-depletion attacks. To meet this challenge, we propose to develop, implement, and evaluate a comprehensive framework, called MODELZ, that MOnitors, DEtects, and anaLyZes energy-greedy anomalies on small mobile devices. MODELZ comprises 1) a charge flow meter that allows infrequent sampling of energy consumption without losing accuracy, 2) a power monitor, in coordination with the charge flow meter, that samples and builds a power-consumption history, and 3) a data analyzer that generates a power signature from the power-consumption history. To generate a power signature, we devise and apply light-weighted, effective noise filtering and data compression, reducing the detection overhead significantly. The similarities between power signatures are measured by the χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -distance and used to lower both false-positive and false-negative detection rates. Our experimental results on an HP iPAQ running the Windows Mobile OS have shown that MODELZ achieves significant (up to 95 percent) storage-savings without losing detection accuracy, and a 99 percent true-positive rate in differentiating legitimate programs from suspicious ones while the monitoring consumes 50 percent less energy than the case of keeping the Bluetooth radio turned on.
Pedestrian detection using wavelet templates (2002)

This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. This problem poses several challenges. People are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or on motion. The detection technique is based on the novel idea of the wavelet template that defines the shape of an object in terms of a subset of the wavelet coefficients of the image. It is invariant to changes in color and texture and can be used to robustly define a rich and complex class of objects such as people. We show how the invariant properties and computational efficiency of the wavelet template make it an effective tool for object detection.
Salus (2012)

Secure function evaluation (SFE) allows a set of mutually distrustful parties to evaluate a function of their joint inputs without revealing their inputs to each other. SFE has been the focus of active research and recent work suggests that it can be made practical. Unfortunately, current protocols and implementations have inherent limitations that are hard to overcome using standard and practical techniques. Among them are: (1) requiring participants to do work linear in the size of the circuit representation of the function; (2) requiring all parties to do the same amount of work; and (3) not being able to provide complete fairness.
Discovery of Pyrophosphate Diesters as Tunable, Soluble, and Bioorthogonal Linkers for Site-Specific Antibody–Drug Conjugates (2016)

As part of an effort to examine the utility of antibody-drug conjugates (ADCs) beyond oncology indications, a novel pyrophosphate ester linker was discovered to enable the targeted delivery of glucocorticoids. As small molecules, these highly soluble phosphate ester drug linkers were found to have ideal orthogonal properties: robust plasma stability coupled with rapid release of payload in a lysosomal environment. Building upon these findings, site-specific ADCs were made between this drug linker combination and an antibody against human CD70, a receptor specifically expressed in immune cells but also found aberrantly expressed in multiple human carcinomas. Full characterization of these ADCs enabled procession to in vitro proof of concept, wherein ADCs 1-22 and 1-37 were demonstrated to afford potent, targeted delivery of glucocorticoids to a representative cell line, as measured by changes in glucocorticoid receptor-mediated gene mRNA levels. These activities were found to be antibody-, linker-, and payload-dependent. Preliminary mechanistic studies support the notion that lysosomal trafficking and enzymatic linker cleavage are required for activity and that the utility for the pyrophosphate linker may be general for internalizing ADCs as well as other targeted delivery platforms.
Introduction to Combining MRI With PET (2016)

The combination of magnetic resonance imaging (MRI) with positron emission tomography (PET) into an integrated MRI–PET device is considered to be enriching the field of molecular imaging. The main advantages of this new combination are the outstanding soft-tissue contrast of the anatomical and functional information from MRI combined with the high sensitivity of PET, which allows the observation of molecular. Developments in the new field of MRI-PET are oriented along several directions that are all related to the three major technical challenges: understanding and reducing the electronic interference, finding suitable approaches to system integration, and implementing accurate and reliable attenuation and motion-correction techniques. Both PET and MR imaging involve active and passive electronic circuits, which usually operate at a very low noise level. The term equipment' comprises all devices that are necessary to put and maintain the animal in a state and position appropriate for imaging and to carry out the imaging operations.
Nuclear reactor cooling system decontamination reagent regeneration. [PWR; BWR] (1980)

An improved method for decontaminating the coolant system of water-cooled nuclear power reactors and for regenerating the decontamination solution is described. A small amount of one or more weak-acid organic complexing agents is added to the reactor coolant, and the pH is adjusted to form a decontamination solution which is circulated throughout the coolant system to dissolve metal oxides from the interior surfaces and complex the resulting metal ions and radionuclide ions. The coolant containing the complexed metal ions and radionuclide ions is passed through a strong-base anion exchange resin bed which has been presaturated with a solution containing the complexing agents in the same ratio and having the same pH as the decontamination solution. As the decontamination solution passes through the resin bed, metal-complexed anions are exchanged for the metal-ion-free anions on the bed, while metal-ion-free anions in the solution pass through the bed, thus removing the metal ions and regenerating the decontamination solution.
Variable bit-rate video coding for ATM and broadcast applications (1993)

Video data compression algorithms typically produce a bit rate that varies both within the picture and over time. If a constant-bit-rate channel is used to transmit the data, then the picture quality must be varied to avoid buffer overflow and underflow. Variable-bit-rate channels offer the promise of relatively constant image quality, as well as statistical multiplexing between users. However, with variable bit rates, buffer control and timing recovery are much less straightforward than with constant bit rate. Buffer control and policing functions, multiplexing of variable-rate bit streams, and timing recovery are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Chromatic and polarization mode dispersion measurement technique using phase-sensitive sideband detection (2002)

A new technique for measuring chromatic dispersion and PMD is presented that does not require a tunable laser. To demonstrate the technique, measurements on a fiber Bragg grating are compared to the modulation phase-shift method. The widely used modulation phase-shift technique for chromatic dispersion measurement utilizes a double sideband modulated signal and a swept carrier frequency to measure delay. The sidebands must have the same amplitude, and the change in delay and frequency must be accurately measured to obtain the dispersion. In the new technique, phase-sensitive detection is performed for each sideband individually.
Estimation of optimal elevator scheduling performance (2006)

Group elevator scheduling is important for transportation efficiency in mid-rise and high-rise buildings, and incessant efforts have been made to improve the service efficiency of elevators. Although these efforts have achieved performance improvements, the performance limit remains an open issue. This paper tries to address that goal by estimating the optimal performance of group elevator scheduling with complete knowledge of future traffic information. A two-level minimization formulation is presented, with passenger-to-car at the high level, and single car dispatching at the low level. The low level is formulated as a passenger-to-trip assignment problem by using a concept trip to facilitate the description of single car dispatching strategies. In view of the difficulty to obtain the absolute optimal performance, our goal turns into its upper and lower bounds. The upper bound is obtained by finding a good feasible solution to this problem. The lower bound is obtained by finding the lower bound for a newly constructed problem whose optimal performance is less than or equal to that of the original problem. Numerical results demonstrate the effectiveness and the scalability of our method
Collaborative Trial for Validation of a Real-Time Reverse Transcriptase-Polymerase Chain Reaction Assay for Detection of Central Nervous System Tissues as Bovine Spongiform Encephalopathy RiskMaterial: Part 1 (2006)

A collaborative trial was conducted to evaluate a real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay for detection of central nervous system (CNS) tissues in meat products (e.g., sausages). The method is based on the detection of ruminant glial fibrillary acidic protein (GFAP) mRNA by applying real-time RT-PCR. The assay was evaluated through a multicenter trial involving 12 participating laboratories that received coded cDNA obtained from 3 different types of sausages. The participants used 5 different real-time detection systems. The results obtained in this validation revealed that this real-time RT-PCR assay performed well in the different laboratories with a detection limit of at least 0.1% CNS in those test materials that contained strongly heat-treated samples (sausages cooked at 120 degrees C) and the medium heat-treated samples (sausages cooked at 80 degrees C). The detection limit of liver sausages was determined to be 0.2% of CNS. Neither the samples with no CNS additive nor the bovine DNA and the negative control containing 100% swine brain gave any positive signals. The presented results indicate that the real-time RT-PCR assay was just as reproducible between laboratories, as repeatable within a laboratory, could reliably be used for detection of bovine spongiform encephalopathy risk material in meat and meat products, and signify that it may be used with confidence in any laboratory.
Applying data mining in investigating money laundering crimes (2003)

In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.
Improving Statistical Model-Based Speech Enhancement with Deep Neural Networks (2018)

This paper presents a framework for improving the performance of statistical model-based single-channel speech enhancement systems by using a deep neural network (DNN). A DNN is trained to predict speech presence in the input signal, and this information is leveraged to design novel methods for noise tracking and a priori signal-to-noise ratio (SNR) estimation, which remain the most challenging tasks in conventional systems. The proposed framework provides increased flexibility for various aspects of system design such as gain estimation, relative to end-to-end DNN-based systems. Additionally, the DNN can be trained to detect speech in the presence of both noise and reverberation, leading to joint suppression of additive noise and reverberation. The proposed framework provides significant improvements in objective speech quality metrics relative to baseline systems, and the proposed system was heavily favored in a subjective preference test.