Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Sequential Power Amplifier With Doherty-Type Active Load Modulation

This paper presents the concept and the detailed analysis of a sequential power amplifier with Doherty-type active load modulation (SPA-D). By introducing a Doherty-type output combiner into the conventional sequential power amplifier (PA), highly efficient amplification over broadband and wide dynamic range for the reconfigurable output power back-off can be achieved. The proposed amplifier mimics both the sequential and the Doherty PAs by exploiting the active load modulation region that has not been considered in the design of Doherty amplifiers. A broadband SPA-D prototype having 8-dB output back-off power (OBO) is designed and implemented. The experimental results exhibit drain efficiencies of 50%-75% at 7-10-dB OBO and 57%-70% at peak power with Doherty-like behavior over 700-MHz bandwidth centered at 2.35 GHz. The ability to easily reconfigure the breakpoint is experimentally verified, providing a convenient means for optimizing the amplifier efficiency according to various wireless standards. In addition, by using a memoryless digital predistortion, the spectrum emission mask required for long-term evolution down link can be fulfilled, making it well suited for applications in modern wireless transmitters.
Realization of feature description systems for clusters by rule generation based on genetic programming and its applications

SUMMARY This paper deals with the realization of feature description systems for clusters by rule generation based on genetic programming (GP) and its applications. First, the data are divided into several clusters by using conventional clustering algorithms. Then logical variables corresponding to the categorical variables are introduced, and the logical expressions using these logical variables are defined as rules to extract the targeted cluster from the dataset. The rules are improved by GP so that they are valid (become true) only for the targeted cluster. Unlike ordinary GP procedures, the fitness of individuals is defined as proportional to the number of hits inside the targeted cluster, but also to the inverse of the number of hits outside the targeted cluster. In simulation studies, the system is applied first to artificially generated samples and clusters to examine the performance of the system, and then to personal loan assessment problems, after which the evaluation of several kinds of clustering problems is summarized. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(9): 87–97, 2007; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/ecjb.20380
Acora: Distribution-Based Aggregation for Relational Learning from Identifier Attributes

Feature construction through aggregation plays an essential role in modeling relationaldomains with one-to-many relationships between tables. One-to-many relationshipslead to bags (multisets) of related entities, from which predictive informationmust be captured. This paper focuses on aggregation from categorical attributesthat can take many values (e.g., object identifiers). We present a novel aggregationmethod as part of a relational learning system ACORA, that combines the use ofvector distance and meta-data about the class-conditional distributions of attributevalues. We provide a theoretical foundation for this approach deriving a "relationalfixed-effect" model within a Bayesian framework, and discuss the implications ofidentifier aggregation on the expressive power of the induced model. One advantageof using identifier attributes is the circumvention of limitations caused either bymissing/unobserved object properties or by independence assumptions. Finally, weshow empirically that the novel aggregators can generalize in the presence of identi-fier (and other high-dimensional) attributes, and also explore the limitations of theapplicability of the methods.
Delineating copper accumulation pathways for the freshwater bivalve Corbicula using stable copper isotopes

Delineation of metal uptake routes in aquatic invertebrates is critical for characterizing bioaccumulation dynamics and assessing risks associated with metal exposure. Here we demonstrate that Cu stable isotopic ratios can be manipulated in both exposure media and algae to determine the efflux rate constant (ke) and to estimate Cu assimilation efficiency (AE) from ingested food in a freshwater bivalve (Corbicula fluminea). The Cu AE in Corbicula fed 65Cu‐spiked Cryptomonas ozolini was 38%. Copper uptake routes had no significant influence on efflux; ke of 0.004 per day characterized the slowest component of efflux following short‐term exposures to 65Cu in water or in both food and water. Incorporation of the physiological parameters for dietary and dissolved uptake as well as rate constants of loss into a bioaccumulation model allowed for assessing the relative contribution of water and food as Cu sources. At [65Cu2+] of 6.7 μg/L, Corbicula accumulated twice as much Cu from diet as from water. In most freshwater systems, the dietary pathway is likely to act as the major Cu uptake route for Corbicula. Extrapolation of our laboratory results to the San Francisco Bay—Delta (California, USA) indicated that our biodynamic model and the laboratory‐derived parameters for dietary 65Cu uptake provided a realistic representation of the processes involved in Cu accumulation by the bivalve Corbicula.
Hydrocortisone reduces Toll-like receptor 4 expression on peripheral CD14+ monocytes in patients undergoing percutaneous coronary intervention.

BACKGROUND Evidence from several lines of investigations suggests that Toll-like receptor 4 (TLR4) is involved in atherosclerosis as a bridge between innate and acquired immunity. Percutaneous coronary intervention (PCI) can trigger inflammation through activation of human TLR4 (hTLR4) on monocytes. Hydrocortisone as an anti-inflammatory and immuno-suppressant agent has multiple mechanisms of action. In this study, we aimed at assessing the effects of hydrocortisone on monocyte expression and activity of hTLR4 in patients underwent PCI. METHODS Blood samples were taken from a total of 71 patients with chronic stable angina who were scheduled for a PCI, before the intervention. Thirty patients received 100 mg hydrocortisone prior to the procedure. Control group was composed of 41 patients underwent PCI without receiving hydrocortisone. Blood collection was repeated 2 and 4 h after PCI. The expression of hTLR4 on the surface of CD14+ monocytes and the serum levels of TNF-α and IL-1β were measured using flowcytometry and Sandwich ELISA. RESULTS Compared with controls, hydrocortisone significantly reduced monocyte expression of hTLR4 in test group (P<0.01). In addition, it had a significant effect on reduction of serum concentrations of TNF-α and IL-1β in test group in a time-dependent manner (P<0.01). CONCLUSION In this study, hydrocortisone was able to reduce the hTLR4/CD14 positive monocytes and its related pro-inflammatory cytokines, thus it can decrease inflammatory responses following PCI.
Modified Moth Search Algorithm for Global Optimization Problems

This paper presents modified moth search algorithm for solving global optimization problems. Moth search algorithm is novel swarm intelligence metaheuristics. By analyzing original moth search approach, we noticed some deficiencies in the search process of subpopulation 2. Modified moth search addresses these weaknesses. To prove the robustness of our approach we tested our algorithm on six standard global optimization benchamarks and performed comparative analysis with original moth search, as well as with other five state-of the-art metaheuristics. Testing results show that in average modified moth search outperforms other approaches included in comparative analysis.
Mine Waste Technology Program Linking Waterfowl with Contaminant Speciation in Riparian Soils

This report summarizes the results of Mine Waste Technology Program (MWTP) Activity III, Project 38, Linking Waterfowl with Contaminant Speciation in Riparian Soils, implemented and funded by the U.S. Environmental Protection Agency (EPA) and jointly administered by EPA and the U.S. Department of Energy (DOE). This project addressed EPA’s technical issue of Mobile Toxic Constituents – Water and Acid Generation. Soil samples were collected from the Coeur d’Alene River Basin and were analyzed for mineralogy and metal contaminant speciation. Both phosphorus (P)-treated soils and untreated soils were examined to determine the effect of P-amendment on metal speciation. Previous studies suggested P-amendments result in precipitation of poorly soluble lead phosphate minerals. In this study, P appears to associate with iron bearing minerals in the soil, whereas lead associates predominantly with manganese bearing phases. The research conducted on site mineralogy and speciation generated no irrefutable evidence that Pamendments promoted formation of poorly soluble lead (Pb)-P mineral phases. In theory, such phases would lower Pb bioavailability in waterfowl exposed to Pb-contaminated soils and sediments. Thus, development of a screening-level method for assessing P-treatment effectiveness (and subsequent reduction in Pb bioavailability) becomes a critical issue. This need is addressed by the two-step sequential extraction procedure that simulates the gizzard and intestinal phases of a typical waterfowl’s gastrointestinal tract. Dr. Strawn’s approach is a modified version of the physiologically based extraction test (PBET) for estimating Pb bioaccessibility in humans, and is subsequently called W-PBET. The gizzard phase of this test demonstrated high Pb extraction reproducibility and accuracy. The Pb bioaccessibility results were positively correlated with those from waterfowl fed contaminated and in situ-treated soils from the lower Coeur d’Alene River Basin. Therefore, W-PBET is a promising, cost-effective method for initial assessment of site-specific Pb bioavailability in waterfowl.
Robust registration of coordinate data sets for noninvasive measurement of bone kinematics

Skeletal joint movement is studied by tracking corresponding points in a sequence of MRI or CT images. This paper describes a non-invasive registration method for tracking large movement of the forearm bones in a three-dimensional coordinate system. A triangulated surface is fitted to the series of MRI cross-sections to represent the three-dimensional volumetric data. The proposed refined iterative closest point (RICP) algorithm is a combination of shape measurement parameters with direct search in the transformation space of a series of local minima by minimizing registration errors. Real data acquired from a series of MRI images of the forearm are used to test the algorithm for estimating movement without attaching markers on the bones.
Implications of intelligent, integrated microsystems for product design and development

Intelligent, integrated microsystems combine some or all of the functions of sensing, processing information, actuation and communication within a single integrated package, and preferably upon a single silicon chip. As the elements of these highly integrated solutions interact strongly with each other, the microsystem can be neither designed nor fabricated piecemeal, in contrast to the more familiar assembled products. Driven by technological imperatives, microsystems will best be developed by multidisciplinary teams, most likely within flatter, less hierarchical organizations. Standardization of design and process tools around a single, dominant technology will expedite economically viable operation under a common production infrastructure. The production base for intelligent, integrated microsystems has elements in common with the mathematical theory of chaos. Similar to chaos theory, the development of microsystems technology will be strongly dependent on, and optimized to, the initial product requirements that will drive standardization-thereby further rewarding early entrants to integrated microsystem technology.
Synthesizing Reversible Circuits for Irreversible Functions

Many reversible circuit synthesis procedures have been proposed. A common feature of most methods is that the initial specification must be a completely-specified reversible function. However, often the desired functionality is a, possibly incompletely-specified, irreversible function. In this paper, we consider how to fully automate the process of synthesizing a reversible function given an irreversible specification with particular emphasis on how to embed an irreversible function into a reversible specification. Systematic procedures are presented and results for benchmark problems show the methods produce very good results compared to earlier methods.
Using Deep Learning techniques for 3 D Object Recognition

This project deals with the problem of automatic object recognition in images including both handwritten characters in documents and real world 3D objects. The former is represented by the MNIST dataset[1] and the latter by the NORB dataset[2]. I have considered only the supervised learning case where the system is first trained using a labeled set of images (’training set’) and the trained system is then tested on a new and hopefully unrelated set of images (’testing set’). The scope of this project is limited to investigating the performance of deep convolutional neural networks (CNN) for this task using several network architectures to observe how the training time and test error change by adding additional layers to the network or by changing the number and receptive field sizes of units in existing layers. My objective in this project is thus to implement and test a robust CNN system that makes it convenient for the user to design a wide range of network architectures and quickly evaluate their performance on different datasets.
Content-based image visualization

The proliferation of content based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-level image features and image clustering made by human users. In conventional image retrieval systems, images are typically characterized by a range of features such as color, texture, and shape. However, little is known to what extent these low-level features can be effectively combined with information visualization techniques such that users may explore images in a digital library according to visual similarities. The authors compare and analyze a number of Pathfinder networks of images generated based on such features. Salient structures of images are visualized according to features extracted from color, texture, and shape orientation. Implications for visualizing and constructing hypermedia systems are discussed.
Realizing Logic Functions Using Single Double-Gate Tunnel FETs: A Simulation Study

In this letter, a single double-gate tunnel field-effect transistor (DGTFET) is proposed to realize different logic functions and investigated using two-dimensional device simulations. It is shown that a single DGTFET can realize logic functions, such as AND, OR, NAND, and NOR by biasing the two gates independently. The key elements in obtaining different logic functions using a DGTFET are employing a gate–source overlap and choosing an appropriate silicon body thickness. Furthermore, it is demonstrated that two-input CMOS-type AND, OR, NAND, and NOR logic gates can be implemented using two DGTFETs that are designed to exhibit complimentary logic functions.
A mid-infrared study of very low mass stars and brown dwarfs in Upper Scorpius

Context. To investigate the formation of sub-stellar objects, we observed a sample of ultracool dwarf members of the Upper Scorpius OB association. Aims. The properties of disks, such as their composition, life-time, and frequency compared to the accretor frequency, provide important clues to the mechanisms responsible for the formation of stellar and sub-stellar objects. Methods. We report the results of mid-IR observations with VISIR at the VLT of 10 ultracool dwarf members of the nearby Upper Scorpius OB association in four filters ranging from 8.59 (PAH1) to 12.8 mu m (Ne II) and one brown dwarf obserbed with Spitzer between 3.6 and 24 mu m. Results. Seven of our targets were detected in at least one of the bands, and we derived upper limits on the fluxes of the remaining 4. These results combined with previous studies from the literature lead to an improved disk frequency of 50 +/- 12%. This frequency is significantly higher than for accretors (16.3% +/- 6.2%). Only one object showing mid-IR excess also has Ha emission at a level that indicates it must be accreting. Four of the detected targets are multiple system candidates. Conclusions. The observed disk frequency for sub-stellar objects in the Upper Scorpius association is similar to that of stars, consistent with a common formation scenario. It is also similar to the disk fractions observed in younger clusters, suggesting that the disk lifetimes might be longer for ultracool dwarfs than for higher-mass stars.
Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum

Simultaneous measurement of more than 30 properties in individual human cells is used to characterize signaling in the immune system. Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell “mass cytometry” to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.
Adaptive Randomization Study Design in Clinical Trials for Psychiatric Disorders

Abstract Background: Uncontrolled placebo response has been one of the major culprits in the failure of randomized clinical trials for depression. A major drawback associated with the presence of high placebo response is the increased noiseto- signal ratio that in a majority of cases prevents the detection of treatment effect. The aim of this work was to propose an adaptive randomization study design based on band-pass filtering and evaluate this approach when compared to the traditional study designs. Results: Clinical trial simulations demonstrated that an adaptive randomization approach always outperformed the conventional study designs in improving the signal-to-noise ratio. The improvement directly correlated with the level of placebo response and the variability in response across centers. The proposed strategy does not warrant any unblinding of data. Conclusions: The use of the adaptive randomization design provides a novel methodological approach for signal detection in clinical trials where placebo represents a known confounding factor. The improvement in signal detection was directly proportional to the level of placebo response, the degree of heterogeneity across recruitment centers with a reduced sample size as compared to the traditional study design. These findings support the use of the band-pass filtering approach in an adaptive randomization design as an efficient way to minimize the impact of uncontrolled placebo response and provide rational go/no-go decision criteria in the development of new medicines for psychiatric disorders.
The Effects of Real-world Events on Music Listening Behavior: An Intervention Time Series Analysis

We approach the research question whether real-world events, such as sport events or product launches, influence music consumption behavior. To this end, we consider events of different categories from Google Trends and model listening events as time series using Last.fm data. Performing an auto-regressive integrated moving average analysis to decompose the signal and subsequently an intervention time series analysis, we find significant signal discontinuities, in particular for the Google news category. We found that news and events are likely to increase the number of songs listened to per person per day by about 2%, while tech events commonly cause 1% less music being consumed.
Heterosexuals’ acceptance predicts the well-being of same-sex attracted young adults beyond ingroup support

Ingroup support for sexual minority individuals may be somewhat different from that of many (heterosexual) racial/ethnic minority groups whose sense of belonging may stem from a shared identity with family and similar community members. This distinction raises the important question of whether out-group (i.e., heterosexual) acceptance plays a unique role in predicting the well-being of sexual minorities even in the presence of support from their own ingroup. Findings from same-sex attracted young adults in Australia supported this hypothesis, suggesting that acceptance from the broader community is an important facet of well-being beyond the important support provided by fellow sexual-minority members. Notably, this study specifically targeted those most likely to have access to support from their sexual minority peers.
Musical Onset Detection with Convolutional Neural Networks

Detecting musical onsets is the first step for many aspects of music analysis, but still lacks accuracy for polyphonic music signals. We perform an initial exploration of the effectiveness of using Convolutional Neural Networks for this task. On a dataset of about 100 minutes of music with 26k annotated onsets, our first experiments slightly surpass the best existing method while requiring less manual preprocessing. The results suggest new directions for improving on the state of the art in onset detection.
Multiple attempt write strategy for low energy STT-RAM

In this paper, we demonstrate an energy-reduction strategy that exploits the stochastic switching characteristics of STT-RAM write operation and propose a multiple-attempt write technique needed for it. In contrast to the traditional approach which uses the pulse that guarantees writes for all cells, the proposed technique uses multiple short pulses. Individually, these pulses result in high probability of write error therefore multiple attempts are made until a successful write for all bits. Average write energy is significantly reduced because the average write duration is far shorter than the worst-case duration. We developed a self-validation write circuit that allows bit-wise validation and current de-activation without adding energy and area overhead. We evaluated the proposed circuit using a compact STT-RAM model targeting an implementation in a 10nm technology node. Results indicate that the proposed architecture reduces write energy by 94.6% compared to the conventional design. Compared to the best previously known architectures that rely on write-read-verify strategy we reduce write energy by 2.1x without area overhead.