gebhart/patrepeval huggingface dataset:
Review of Deep Learning Methods for Individual Treatment Effect Estimation with Automatic Hyperparameter Optimization (2022)

&lt;p&gt;Abstract—Estimation of individual treatment effect (ITE) for&lt;/p&gt; &lt;p&gt;different types of treatment is a common challenge in therapy assessments, clinical trials and diagnosis. Deep learning methods, namely representation based, adversarial, and variational, have shown promising potential in ITE estimation. However, it was unclear whether the hyperparameters of the originally proposed methods were well optimized for different benchmark datasets. To solve these problems, we created a public code library containing representation-based, adversarial, and variational methods written in TensorFlow. In order to have a broader collection of ITE estimation methods, we have also included neural network based meta-learners. The code library is made accessible for reproducibility and facilitating future works in the field of causal inference. Our results demonstrate that performance of most methods can be improved using automatic hyperparameter optimization. Additionally, we review the methods and compare the performance of the optimized models from our library on publicly available datasets. The potential of hyperparameter optimization may encourage researchers to focus on this aspect when creating new methods for inferring individual treatment effect.&lt;/p&gt;
Predictive energy management of residential buildings while self-reporting flexibility envelope (2021)

In recent years, renewable energy resources have been increasingly embedded in the distribution grids, raising new issues such as reverse power flows, and challenging the traditional distribution system operation. In order to mitigate these issues, it has been proposed to operate the distribution system more flexibly. For instance, residential buildings are ideal candidates to offer energy flexibility locally and defer avoidable and expensive system expansions. Due to advances in smart meter technologies and trends towards digitalization, it becomes more and more common that electrical appliances in residential buildings are equipped with remote communication and control capabilities. This paper aims at quantifying a flexibility envelope of actively controlled flexible buildings and at analyzing the sensitivity of flexibility levels with respect to system configuration, control strategy and objective function settings. We consider rooftop photovoltaic units, air-sourced heat pumps for space heating and domestic hot water, thermal energy storage and electric vehicles. The results show that an optimal control aiming at minimizing energy costs while limiting peak power can lead to savings of up to 25% compared to the existing rule-based control. When carbon emissions are considered in the cost function, the optimized controller leads to an emission reduction of up to 21%. The time-dependent quantification of the flexibility envelope further reveals that high and low power levels can only be sustained for a limited period, whereas medium power levels can be sustained the longest.
Spatial Analogue of Quantum Spin Dynamics via Spin-Orbit Interaction (2011)

We map electron spin dynamics from time to space in quantum wires with spatially uniform and oscillating Rashba spin-orbit coupling. The presence of the spin-orbit interaction introduces pseudo-Zeeman couplings of the electron spins to effective magnetic fields. We show that by periodically modulating the spin-orbit coupling along the quantum wire axis, it is possible to create the spatial analogue of spin resonance, without the need for any real magnetic fields. The mapping of time-dependent operations onto a spatial axis suggests a new mode for quantum information processing in which gate operations are encoded into the band structure of the material. We describe a realization of such materials within nanowires at the interface of LaAlO3/SrTiO3 heterostructures.
Towards Seamless Support of Natural Collaborative Interactions (2000)

In order to effectively support collaboration it is important that computer technology seamlessly support users' natural interactions instead of inhibiting or constraining the collaborative process. The research presented in this paper examines the human-human component of computer supported cooperative work and how the design of technology can impact how people work together. In particular, this study examined children's natural interactions when working in a physical medium compared to two computer-based environments (a traditional desktop computer and a system augmented to provide each user with a mouse and a cursor). Results of this research demonstrate that given the opportunity, children will take advantage of the ability to interact concurrently. In addition, users' verbal interactions and performance can be constrained when they are forced to interact sequentially, as in the traditional computer setup. Supporting concurrent interactions with multiple input devices is a first step towards developing effective collaborative environments that support users' natural collaborative interactions.
High Throughput Development of Selective Oxidation Catalysts at Sud-Chemie (2011)

The Temkin reactor concept was successfully extended to the high throughput operation mode and it could be considerably improved as compared to the original design with respect to an optimized gas flow pattern over the full size beads. This improved parallel reactor design was successfully used for the high throughput optimization of an innovative new class of physically coated VAM shell catalysts. Exploiting this novel, improved Temkin reactor concept allowed Süd-Chemie not only to optimize the multiparameter compositional space of noble metal and promoter loadings on the support spheres but for the first time to combine this "chemical optimization" with the high throughput improvement of catalytically decisive parameters as the active shell thickness, the metal distribution cross the shell, the pore diameters, and the pore volumes. This new class of physically impregnated VA catalysts, called VAM²ax, impress by its exceptionally high VA selectivity of above 94% at 50% oxygen conversion and the very high space time yields of > 1000 g VAM/l*h which easily can be reached over these shell catalysts with optimized mass and heat transport properties.
Convenient synthesis of c2‐symmetric diazepinium salts derived from 1,1′‐binaphthyl‐2,2′‐diamine (2007)

Abstract magnified image New C 2 ‐symmetric atropisomeric diazepinium salts were prepared utilizing Vilsmeier‐Haack activation of corresponding formamide precursors by phosgene solution in toluene. The structures of these substances were verified by X‐ray diffraction.
Resampling and harmonization for mitigation of heterogeneity in image parameters of baseline scans (2022)

Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset-Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset-Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution-c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing-c-score: 0.54, large pixel spacing-c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario-harmonize by voxel spacing parameters: IO dataset; thin slice-c-score: 0.62, thick slice-c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making.
Fundamental Aspects of Thermal Conductivity Aging and Dimensional Stability of Rigid Polyurethane Foams (1991)

One of the possible alternatives to eliminate the future use of CFC-11 as an essential blowing agent in rigid polyurethane foams, consists of a partial or complete replacement by carbon dioxide. The two major problems associated with the use of high levels of CO2 are first, a significant deterioration of thermal insulation capacity with time, and second, a potential loss of dimensional stability due to the rapid effusion of CO2. The paper gives an overview of the scope and limitations of this approach based on the results of extensive computational analysis and simulation.
SEMANTIC MEMORY LOSS IN DEMENTIA OF ALZHEIMER'S TYPE (1990)

This paper examines three methodological issues concerning the measurement of semantic memory impairment in brain-damaged patients. Ten carefully selected patients with dementia of Alzheimer's type (DAT) and anomia were studied. A battery of perceptual tests and direct tests of semantic memory led to the conclusion that these patients represented a homogeneous group having a prominent deterioration of their semantic memory store without visual perceptual deficits. The first issue addressed in this patient group was whether verbal fluency impairment accurately reflected the loss of semantic memory. It was found that verbal fluency (generation of semantic category lists) was impaired due to two major constraints: deterioration of semantic memory store, and variable difficulties in semantic search. Verbal fluency, therefore, reflects semantic memory loss to some degree, but is not a direct test of semantic memory store in DAT. The second issue was whether semantic memory impairment in our patients conformed to the 'semantic storage disorder' syndrome hypothesized by Shallice (1987). It was shown that, consistent with this hypothesis, the patients demonstrated co-occurrence of consistency of errors, loss of semantic cueing, and preserved superordinate knowledge with loss of detailed knowledge of concept items. The third issue was whether semantic cueing and semantic priming are altered in a similar manner in DAT. It demonstrated that semantic cueing and semantic priming, using the same words whose concepts were degraded in semantic memory, yielded an entirely different pattern of results. Cueing and priming therefore may not be used interchangeably in the study of semantic loss after brain damage.
Two-Dimensional Black Phosphorus and Graphene Oxide Nanosheets Synergistically Enhance Cell Proliferation and Osteogenesis on 3D Printed Scaffolds (2019)

Two-dimensional (2D) materials have emerged as a new promising research topic for tissue engineering because of their ability to alter the surface properties of tissue scaffolds and thus improve their biocompatibility and cell affinity. Multiple 2D materials, such as graphene and graphene oxide (GO), have been widely reported to enhance cell adhesion and proliferation. Recently, a newly emerged black phosphorus (BP) 2D material has attracted attention in biomedical applications because of its unique mechanical and electrochemical characteristics. In this study, we investigated the synergistic effect of these two types of 2D materials on cell osteogenesis for bone tissue engineering. BP was first wrapped in negatively charged GO nanosheets, which were then adsorbed together onto positively charged poly(propylene fumarate) three-dimensional (3D) scaffolds. The increased surface area provided by GO nanosheets would enhance cell attachment at the initial stage. In addition, slow oxidation of BP nanosheets wrapped within GO layers would generate a continuous release of phosphate, an important osteoblast differentiation facilitator designed to stimulate cell osteogenesis toward the new bone formation. Through the use of 3D confocal imaging, unique interactions between cells and BP nanosheets were observed, including a stretched cell shape and the development of filaments around the BP nanosheets, along with increased cell proliferation when compared with scaffolds incorporating only one of the 2D materials. Furthermore, the biomineralization of 3D scaffolds, as well as cellular osteogenic markers, was all measured and improved on scaffolds with both BP and GO nanosheets. All these results indicate that the incorporation of 2D BP and GO materials could effectively and synergistically stimulate cell proliferation and osteogenesis on 3D tissue scaffolds.
ChemInform Abstract: (14)Annulenequinones with Anthraquinone Periphery: “Isopyrenequinone”. (1991)

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.
Closed-loop identification of carotid sinus baroreflex open-loop transfer characteristics in rabbits (1997)

In the circulatory system, a change in blood pressure operates through the baroreflex to alter sympathetic efferent nerve activity, which in turn affects blood pressure. Existence of this closed feedback loop makes it difficult to identify the baroreflex open-loop transfer characteristics by means of conventional frequency domain approaches. Although several investigators have demonstrated the advantages of the time domain approach using parametric models such as the autoregressive moving average model, specification of the model structure critically affects their results. Thus we investigated the applicability of a nonparametric closed-loop identification technique to the carotid sinus baroreflex system by using an exogenous perturbation according to a binary white-noise sequence. To validate the identification method, we compared the transfer functions estimated by the closed-loop identification with those estimated by open-loop identification. The transfer functions determined by the two identification methods did not differ statistically in their fitted parameters. We conclude that exogenous perturbation to the baroreflex system enables us to estimate the open-loop baroreflex transfer characteristics under closed-loop conditions.
Anti-endotoxin properties of cationic host defence peptides and proteins (2005)

The innate immune system of mammals contains a series of peptides with overall positive charge and an amphipathic structure which have a variety of important properties in host defences. Although these are often termed cationic antimicrobial peptides, they have numerous roles in innate defences in all complex species of life and thus we prefer to refer to them as host defence peptides. These roles include: (i) an ability to kill micro-organisms directly, ranging from bacteria to viruses, fungi, parasites and helminths; (ii) an adjuvant activity in the adaptive response; and (iii) a multiplicity of roles in modulating innate immunity, including an apparent ability to stimulate protective innate immunity while suppressing harmful inflammatory/septic responses. This latter property may be one of the more important activities of these peptides in vivo. Innate immunity is thought to be triggered by the interaction of conserved bacterial components with particular receptors including Toll-like receptors (TLRs) on host cells. However, the initiation of the innate immune response through this route may trigger a pro-inflammatory cascade that is the principle cause of harmful conditions such as sepsis. Since we are exposed to potentially dangerous pathogens on a daily basis, the host response must contain certain checks and balances. We propose that host defence peptides have a role in feed-back modulation of inflammation under normal (low-pathogen exposure) conditions. This review surveys the available information regarding the antiendotoxic/anti-inflammatory properties of host defence peptides, and will address whether this potential might be exploited for therapeutic benefit in sepsis.
Array Placement in Distributed Massive MIMO for Power Saving considering Environment Information (2021)

Distributed massive MIMO (D-mMIMO) has been considered for future networks as it holds the potential to offer superior capacity while enabling energy savings in the network. A D-mMIMO system has multiple arrays. Optimizing the locations of the arrays is essential for the energy efficiency of the system. In existing works, array placement has been optimized mostly based on common channel models, which rely on a given statistical distribution and Euclidean distance between user locations and arrays. These models are justified if applied to sufficiently large cells, where the statistical description of the channel is expected to fit its empirical condition. However, with the advent of small cells, this is no longer the case. The channel propagation condition becomes highly environment-specific. This paper investigates array placement optimization with different ways of modeling the propagation conditions taking the environment information (e.g., buildings) into account. We capture the environment information via a graph. Two shortest path-based propagation models are introduced based on the graph. We validate the performance of the models with a ray-tracing simulator considering different signal coverage levels. The simulation results demonstrate that higher energy efficiency is achieved by using the array placements found via the proposed models compared to a Euclidean distance-based propagation model in small cells. The average power saving at 96% signal coverage, for example, reaches more than 5 dB.
Highly Atom Economical Molecular Transformation via Hetero-Nickelacycle (2017)

Abstract Nickel is one of the most popular first row transition metals for coupling reactions. For the past two decades, catalytic multi-component coupling reactions via nickelacycles have been developed. Although the formation of nickelacycles has been believed an important key step in the catalytic reactions, the generation of nickelacycles by oxidative cyclization has been less studied. Thus, we have been focusing on the formation of nickelacycles from nickel(0) species and development of catalytic reactions without using coupling reagents to construct highly atom economical nickel-catalyzed multi-component connecting reactions.
Exploring the use of deep learning in task-flexible ILC* (2023)

Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
Learning from Examples in Optical Imaging (2016)

Imaging systems can be designed using examples and methods similar to the techniques used in deep learning. We describe experimental results demonstrating optical tomography based on the learning approach.
Depolymerization of Bottlebrush Polypentenamers and Their Macromolecular Metamorphosis (2019)

The depolymerization of bottlebrush (BB) polymers with varying lengths of polycyclopentene (PCP) backbone and polystyrene (PS) grafts is investigated. In all cases, ring closing metathesis (RCM) depolymerization of the PCP BB backbone appears to occur through an end-to-end depolymerization mechanism as evidenced by size exclusion chromatography. Investigation on the RCM depolymerization of linear PCP reveals a more random chain degradation process. Quantitative depolymerization occurs under thermodynamic conditions (higher temperature and dilution) that drives RCM into cyclopentenes (CPs), each bearing one of the original PS grafts from the BB. Catalyst screening reveals Grubbs' third (G3) and second (G2) generation catalyst depolymerize BBs significantly faster than Grubbs' first generation (G1) and Hoveyda-Grubbs' second generation (HG2) catalyst under identical conditions while solvent (toluene versus CHCl<sub>3</sub>) plays a less significant role. The length of the BB backbone and PS side chains also play a minor role in depolymerization kinetics, which is discussed. The ability to completely deconstruct these BB architectures into linear grafts provides definitive insights toward the ATRP "grafting-from" mechanism originally used to construct the BBs. Core-shell BB block copolymers (BBCPs) are shown to quantitatively depolymerize into linear diblock polymer grafts. Finally, the complete depolymerization of BBs into α-cyclopentenyl-PS allows further transformation to other architectures, such as 3-arm stars, through thiol-ene coupling onto the CP end group. These unique materials open the door to stimuli-responsive reassembly of BBs and BBCPs into new morphologies driven by macromolecular metamorphosis.
Blue thermally activated delayed fluorescence materials based on bis(phenylsulfonyl)benzene derivatives (2015)

Two blue thermally activated delayed fluorescence molecules based on bis(phenylsulfonyl)benzene with very small singlet-triplet splitting energy were designed and synthesized by combining 3,6-di-tert-butylcarbazole with 1,4-bis(phenylsulfonyl)benzene and 1,3-bis(phenylsulfonyl)benzene, and a maximum external quantum efficiency of 11.7% was achieved for an electroluminescent device.
Sage (2022)

We propose Sage, a system for uncertain network analysis. Algorithms for uncertain network analysis require large amounts of memory and computing resources as they sample a large number of network instances and run analysis on them. Sage makes uncertain network analysis simple and efficient. By extending the edge-centric programming model, Sage makes writing sampling-based analysis algorithms as simple as writing conventional graph algorithms in Pregel-like systems. Moreover, Sage proposes four optimization techniques, namely, deterministic sampling, hybrid gathering, schedule-aware caching, and copy-on-write attributes, that exploit common properties of uncertain network analysis. Extensive evaluation of Sage with eight algorithms on six real-world networks shows that the four optimizations in Sage jointly improve performance by up to 13.9X and on average 2.7X.