gebhart/patrepeval huggingface dataset:
Lie Group Statistics and Lie Group Machine Learning Based on Souriau Lie Groups Thermodynamics & Koszul-Souriau-Fisher Metric: New Entropy Definition as Generalized Casimir Invariant Function in Coadjoint Representation (2020)

In 1969, Jean-Marie Souriau introduced a "Lie Groups Thermodynamics" in Statistical Mechanics in the framework of Geometric Mechanics. This Souriau's model considers the statistical mechanics of dynamic systems in their "space of evolution" associated to a homogeneous symplectic manifold by a Lagrange 2-form, and defines in case of non null cohomology (non equivariance of the coadjoint action on the moment map with appearance of an additional cocyle) a Gibbs density (of maximum entropy) that is covariant under the action of dynamic groups of physics (e.g., Galileo's group in classical physics). Souriau Lie Group Thermodynamics was also addressed 30 years after Souriau by R.F. Streater in the framework of Quantum Physics by Information Geometry for some Lie algebras, but only in the case of null cohomology. Souriau method could then be applied on Lie groups to define a covariant maximum entropy density by Kirillov representation theory. We will illustrate this method for homogeneous Siegel domains and more especially for Poincaré unit disk by considering SU(1,1) group coadjoint orbit and by using its Souriau's moment map. For this case, the coadjoint action on moment map is equivariant. For non-null cohomology, we give the case of Lie group SE(2). Finally, we will propose a new geometric definition of Entropy that could be built as a generalized Casimir invariant function in coadjoint representation, and Massieu characteristic function, dual of Entropy by Legendre transform, as a generalized Casimir invariant function in adjoint representation, where Souriau cocycle is a measure of the lack of equivariance of the moment mapping.
Beat-to-beat cardiac output inference using heart sounds (2011)

Cardiac output (CO) change is the primary compensatory mechanism that responds to oxygenation demand. Its continuous monitoring has great potential for the diagnosis and management of cardiovascular diseases, both in hospital as well as in ambulatory settings. However, CO measurements are currently limited to hospital settings only. In this paper, we present an extension of the model proposed by Finkelstein for beat-to-beat CO assessment. We use a nonlinear model consisting of a two-layer feed-forward artificial neural network. In addition to demographic (body surface area and age) and physiological parameters (HR), surrogates of contractility, afterload and mean arterial pressure based on systolic time intervals (STIs), estimated from echocardiography and heart sounds are used as inputs to our models. The results showed that the proposed models--with echocardiography as reference--produce better estimations of stroke volume/CO than the Finkelstein model (12.83 ± 10.66 ml vs 7.23 ± 6.6 ml), as well as higher correlation (0.46 vs 0.82).
ChemInform Abstract: Stereoselective Partial Hydrogenation of the Triple Bond in Methyl 2‐Ethyl‐6,6‐dimethylhept‐2‐en‐4‐ynoate: A Comparison of Lindlar and Montmorillonite Catalysts. (1990)

Abstract Hydrogenations of the triple bond in (I) are carried out using Lindlar and montmorillonite catalysts in various solvents.
Electromagnetics and Antenna Theory (2015)

This chapter contains sections titled: 1.1. Introduction, 1.2 Bandwidth, 1.3 Ultrawideband Phased Arrays, 1.4 Wavefront Time Delay, 1.5 Electromagnetic Radiation: Maxwell's Equations, 1.6 Fields from Time-Varying Electric and Magnetic Current Sources, 1.7 Boundary Conditions, 1.8 Wave Equation for Conducting Media, Propagation Parameters, 1.9 Electromagnetic Energy Flow, 1.10 Fields of Short Electric and Magnetic Dipoles, 1.11 Electromagnetic Wave Polarization and Receive Antennas, 1.12 Summary, References
The Value of Population-Based Studies in the Genomic Era (2007)

Since the landmark Framingham Study in the 1950s, there have been many large population-based studies conducted in the United States and around the world. For over half a century, the contribution ...
Distortion measures for vector quantization of noisy spectrum (2005)

In this paper we address the problem of vector quantization of speech in a noisy environment. We show that the performance of a vector quantization system can be improved by adapting the distortion measure to the changing environmental conditions. The proposed method emphasizes the distortion in spectral regions where the speech signal dominates. The method functions well even when conventional pre-processor methods fail because the noise statistics cannot be estimated reliably from speech pauses (as, e.g., in tandeming operations). Objective tests confirm that the use of environmentally adaptive measures significantly improves estimation accuracy in noisy speech, while preserving the quality in the case of clean input.
Energy losses in an acoustical resonator (2001)

A one-dimensional model has recently been developed for the analysis of nonlinear standing waves in an acoustical resonator. This model is modified to include energy losses in the boundary layer along the resonator wall. An investigation of the influence of the boundary layer on the acoustical field in the resonator and on the energy dissipation in the resonator is conducted. The effect of the boundary layer is taken into account by introducing an additional term into the continuity equation to describe the flow from the boundary layer to the volume. A linear approximation is used in the development of the boundary layer model. In addition to the viscous attenuation in the boundary layer, the effect of acoustically generated turbulence is modeled by an eddy viscosity formulation. Calculatons of energy losses and a quality factor of a resonator are included into the numerical code. Results are presented for resonators of three different geometries: a cylinder, a horn cone, and a bulb-type resonator. A comparison of measured and predicted dissipation shows good agreement.
Formation of Paramagnetic Defect Centers in Aluminophosphate Molecular Sieves (1996)

The formation of paramagnetic defect centers in aluminophosphate (AlPO4) molecular sieves with different structures is investigated by electron spin resonance (ESR) spectroscopy. Distinct differences in the position and number of the ESR signals generated by dehydroxylation of AlPO4 molecular sieves under a vacuum of 10-5 Torr at 773 K clearly show that the nature of the paramagnetic defect centers formed is significantly different in the structure type of the molecular sieves. In addition, it is found that the spin concentration of these defects depends strongly on the pore size of the AlPO4 molecular sieves as well as their hydration level before dehydroxylation. The larger ring size the molecular sieve has, the higher spin concentration of paramagnetic defects it shows. Thus, the extra-large-pore VPI-5 shows the highest spin concentration value among the molecular sieves studied here, which corresponds to 6.4 defects per 1000 unit cells. The ESR results obtained from AlPO4-based molecular sieves containing heteroatoms other than aluminum and phosphorus (such as silicon or cobalt) reveal that the formation of paramagnetic defect centers is severely restricted when the amount of heteroatoms in their framework increases. On the basis of the overall ESR results of this study, a reliable mechanism of formation of paramagnetic defect centers in AlPO4 molecular sieves is proposed.
Resource allocation for distributed cloud: concepts and research challenges (2011)

In a cloud computing environment, dynamic resource allocation and reallocation are keys for accommodating unpredictable demands and, ultimately, contribute to investment return. This article discusses this process in the context of distributed clouds, which are seen as systems where application developers can selectively lease geographically distributed resources. This article highlights and categorizes the main challenges inherent to the resource allocation process particular to distributed clouds, offering a stepwise view of this process that covers the initial modeling phase through to the optimization phase.
Gaze Visual - A Graphical Software Tool for Performance Evaluation of Eye Gaze Estimation Systems (2018)

The concept of an open source software developed for all round performance evaluation of gaze tracking systems is presented. The capabilities of this software towards quantitative, statistical and visual analysis of gaze data are discussed. Potential utilities of this software are towards understanding a gaze tracker's behavior, gaze data quality, and improving usability of gaze based applications that are currently very popular in augmented/virtual reality, gaming and multimedia domains.
A Low-Temperature Batch Mode Packaging Process for Submillimeter Microsystems in Harsh Environment Applications (2016)

This paper presents a low-temperature batch mode packaging process for microsystems that are intended for harsh environments, in particular those with high pressure and high salinity, such as encountered in downhole data logging for oil exploration and production. The package consists of a shell made from a high-strength material, such as stainless steel (SS), and an insert made from a deformable material, such as aluminum (Al). The process includes a batch mode method for chip integration and a batch mode microcrimping method for package assembly. In a process demonstration, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5 \times 5$ </tex-math></inline-formula> array of packages was made from SS 316 and Al alloy 3003, with dummy silicon chips encapsulated inside. Each package had an outer dimension of 0.5 mm along each axis. Two layers of coatings were deposited on the packages, including 50-nm-thick Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> deposited by atomic layer deposition and 5- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> -thick Parylene C made by vapor phase deposition to protect against corrosion. The packages survive at least 72 h in the standard American Petroleum Institute hot brine at 80 °C. The packages also survive pressure >200 MPa.
Object-Oriented Software Development Based On Clusters: Concepts, Consequences and Examples. (1992)

This paper focuses on object-oriented system development, presenting insights gained from the objectoriented design and implementation of a graphic user interface prototyping tool. The development of this tool corroborates the cluster model of the software life cycle—a model that was first described by Meyer [Meye88, Meye89]. We sketch basic ideas of the cluster model and draw general statements (e.g., how clusters are formed; implications on design methods) from the clusteroriented design and implementation of a user interface prototyping tool. Examples substantiate that the development of object-oriented systems is characterized best by the cluster model.
Leakage-aware adaptive routing for pipelined on-chip networks in ultra-deep sub-micron technologies (2012)

As semiconductor process technology continues to scale down to the ultra-deep sub-micron level, leakage power becomes a critical design constraint for on-chip networks (OCNs). Power gating is widely used to reduce the OCN leakage power; however, it does not work well with adaptive routing owing to its aggressive use of free links and router buffers to achieve high performance. In this paper, a novel leakage-aware adaptive routing algorithm to increase the power-gating effect by routing packets with minimal link activation is proposed. Experimental results show that the proposed algorithm effectively achieves a reduction in the overall network leakage power of up to 11.6% greater than the conventional adaptive routing algorithm, with a little sacrificing network bandwidth.
Sustained Phenotypic Correction of Murine Hemophilia A by In Vivo Gene Therapy (1998)

Abstract Hemophilia A is caused by a deficiency of blood coagulation factor VIII (FVIII) and has been widely discussed as a candidate for gene therapy. While the natural canine model of hemophilia A has been valuable for the development of FVIII pharmaceutical products, the use of hemophiliac dogs for gene therapy studies has several limitations such as expense and the long canine generation time. The recent creation of two strains of FVIII-deficient mice provides the first small animal model of hemophilia A. Treatment of hemophiliac mice of both genotypes with potent, human FVIII-encoding adenoviral vectors resulted in expression of biologically active human FVIII at levels, which declined, but remained above the human therapeutic range for over 9 months. The duration of expression and FVIII plasma levels achieved were similar in both hemophiliac mouse strains. Treated mice readily survived tail clipping with minimal blood loss, thus showing phenotypic correction of murine hemophilia A by in vivo gene therapy.
Correlation of the Oncotype DX recurrence score with a composite index comprised of ER, PR, HER2, and breast tumor grade. (2010)

10647 Background: The Oncotype Dx recurrence score (RS) has been widely adopted by oncologists to guide adjuvant chemotherapy in patients with ER-positive breast carcinomas. The majority of genes included in the Oncotype assay are components of the ER/PR and HER2 pathways or reflect proliferative potential, which is correlated with tumor grade. Methods: 177 consecutive samples of ER-positive primary breast carcinomas treated at Duke University Medical Center were submitted to Genomic Health at the clinicians' request. From the corresponding pathology reports we extracted the following information: ER and PR (Allred scores), HER2 status (negative, borderline, positive), and the three components of the Nottingham tumor grade. For 160 cases, the specific blocks submitted to Genomic Health were centrally typed and graded by the study pathologist (JG). A multivariate linear regression model was used to generate a new index from the 6 parameters, named the breast cancer prognostic score (BCPS). Results: RS ranged from 0 to 71 (median 18). 85 cases were in the low (< 18), 74 in the intermediate, and 18 in the high (> 30) category. The RS was negatively correlated with the Allred score for ER and PR, and positively correlated with HER2, architectural grade (AG), nuclear grade (NG), mitotic grade (MG), and combined Nottingham grade. The multivariate linear model yielded the following formula: BCPS = 33.1 – 2.88[ER] – 1.48[PR] + 7.05[HER2] + 1.27[AG] + 1.32[NG] + 3.54[MG]. The BCPS predicted the RS with an R2 of 0.42 (p < 10−15). Setting thresholds at 17.8 and 28.1, there was complete categorical concordance between BCPS and RS in 56% of cases (weighted kappa = 0.46). When the RS was categorized as in the TailorX trial (cutoffs 11, 25) and the BCPS cutoffs adjusted accordingly, the complete concordance rate increased to 68%. Central grading did not improve the correlation between BCPS and RS. Conclusions: A composite index incorporating routinely reported ER, PR, HER2, and tumor grade data is strongly correlated with the Oncotype RS but does not involve added expense. Whether the BCPS has similar or better prognostic/predictive value compared to the RS, can be tested in appropriate breast cancer cohorts with outcome data. No significant financial relationships to disclose.
N-acetyl-L-leucine for Niemann-Pick type C: a multinational double-blind randomized placebo-controlled crossover study (2023)

gov (NCT05163288) and www.clinicaltrialsregister.eu (EudraCT: 2021-005356-10). Registered on 20 December 2021.
Automatic segmentation of 3D cardiac SPECT imagery (2002)

The automatic visualization and quantitative analysis of cardiac SPECT data requires the ability to automatically segment and extract voxels representing the heart. The attributes of the 3D data make this task quite challenging. The authors attempt to address these issues and propose an algorithm which successfully detects the voxels belonging to the left ventricle (LV) of the heart and filters out the noise and all other interfering organs. The algorithm relies on various image processing and pattern analysis techniques as well as the constraints put forward by the anatomy. The final outcome of this algorithm is a segmented 3D dataset containing voxels pertaining only to the LV. This filtered dataset is then employed for automatic determination of LV orientation. The results show that this methodology is a very promising approach to segmentation of cardiac SPECT imagery.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>
Modeling and data-driven isolation of two-way wavefield constituents (2020)

ABSTRACT Synthesizing individual wavefield constituents (such as primaries, first-order scattering, and free-surface or internal multiples) is important in the development of seismic data processing algorithms, for instance, for seismic multiple removal and imaging. A range of methods that allow for the computation of such wavefield constituents exist, but they are generally restricted to relatively simple, horizontally layered media. For wave simulations on more complex models, a straightforward and performant alternative are finite-difference methods. They are, however, generally not perceived as being capable of delivering isolated wavefield constituents. Based on recent advances, we found how this can be achieved for (nonhorizontally) piecewise constant layered media. For example, we were able to accurately retrieve the isolated direct arrival of the transmission response (including tunneled waves), primary reflection data (without internal multiples), and all events related to a single (or multiple) interface(s) in a medium. Our methods required detailed knowledge of discretized medium parameters. Alternatively, if a medium is known only implicitly via recordings of reflection data, interface-related events can still be isolated through a combination of subdomain-related wavefields. We found how Marchenko redatuming can be used to derive these, which enables data-driven identification (and removal) of interface-related events from surface data.
An Inhibitory Role of the Phosphatidylinositol 3-Kinase-signaling Pathway in Vascular Endothelial Growth Factor-induced Tissue Factor Expression (2001)

Vascular endothelial growth factor (VEGF) is not only essential for vasculogenesis and angiogenesis but is also capable of inducing tissue factor, the prime initiator of coagulation, in endothelial cells. In this study we have analyzed the VEGF-elicited pathways involved in the induction of tissue factor in human umbilical cord vein endothelial cells. Using specific low molecular weight inhibitors we could demonstrate a crucial role of the p38 and Erk-1/2 mitogen-activated protein (MAP) kinases. In contrast, treatment with wortmannin or LY294002, inhibitors of phosphatidylinositol 3 (PI3)-kinase, resulted in a strong enhancement of the VEGF-induced tissue factor production, indicating a negative regulatory role of the PI3-kinase on tissue factor-inducing pathways. Accordingly, transduction with constitutively active Akt led to a reduction of VEGF-induced tissue factor production. Western blot analyses using antibodies specific for phosphorylated p38 showed an enhanced activation of this MAP kinase in human umbilical cord vein endothelial cells when stimulated with VEGF in the presence of wortmannin in comparison to either agent alone. Thus, the negative regulation of the PI3-kinase pathway on endothelial tissue factor activity can be explained at least in part by a suppression of this MAP kinase-signaling pathway. This is the first demonstration of a reciprocal relationship between procoagulant activity and the PI3-kinase-Akt signaling pathway, and it reveals a novel mechanism by which tissue factor expression can be controlled in endothelial cells.
A distributed dynamic channel allocation technique for throughput improvement in a dense WLAN environment (2004)

In a dense WLAN environment, the signal coverage area of each access point (AP) typically has significant overlap with that of the neighboring APs. This is a problem if there are limited frequency channels. This paper presents an algorithm that can improve per-user throughput significantly, particularly for nonuniform traffic conditions. It is based on a cellular neural network model. Like a cellular neuron changing its state, based on the information of its neighboring neurons, every AP determines the best channel it should use in the next time slot, based solely on the traffic load of its neighboring APs and the channels used by them in the current time slot, but it actually switches to that channel with some fixed probability less than one. All APs in the network perform the above operation simultaneously. Computer simulations show that (1) given any traffic load distribution and any initial channel allocation, the algorithm converges to an equilibrium state in a short time, in which the overall throughput of the network is significantly improved; and (2) there exists an optimal switching probability that can minimize the time for the algorithm to reach the equilibrium state. The proposed technique has significant practical value due to its simplicity and effectiveness.
Chapter 24. Few-Shot Continual Learning Based on Vector Symbolic Architectures (2023)

Vector Symbolic Architecture (VSA) is a powerful computing model that is built on a rich algebra in which all representations—from atomic to composite structures—are high-dimensional holographic distributed vectors of the same, fixed dimensionality. VSA is mainly characterized by the following intriguing properties: (i) quasi-orthogonality of a randomly chosen vector to other random vectors with very high probability, aka concentration of measure; (ii) exponential growth of the number of such quasi-orthogonal vectors with the dimensionality, which provides a sufficiently large capacity to accommodate novel concepts over time; (iii) availability of these vectors to be composed, decomposed, probed, and transformed in various ways using a set of well-defined operations. Motivated by these properties, this chapter presents a summary of recently developed methodologies on the integration of VSA with deep neural networks that enabled impactful applications to few-shot [1] and continual [2, 3] learning. Resorting to VSA-based embedding allows deep neural networks to quickly learn from few training samples by storing them in an explicit memory, where many more class categories can be continually expressed in the abstract vector space of VSA with fixed dimensions, without causing interference among the learned classes. Experiments on various image datasets show that the considered neuro-symbolic AI approach outperforms pure deep neural network baselines with remarkable accuracy, scalability, and compute/memory efficiency.