Dear Editor,Circulating tumor cells(CTCs)are instrumental in hematogenous metastasis and are widely studied using liquid biopsy *** involve analysis and characterization of CTCs from fractionally small blood samples d...
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Dear Editor,Circulating tumor cells(CTCs)are instrumental in hematogenous metastasis and are widely studied using liquid biopsy *** involve analysis and characterization of CTCs from fractionally small blood samples drawn from *** biopsy implicitly assumes that the number and phenotypical distribution of CTCs in small blood samples is representative of the full peripheral blood volume,and furthermore that CTC numbers are approximately constant over the days and hours surrounding the blood draw.
This paper presents a novel simultaneous iterative learning control and dynamic modeling (SILCDM) approach. For a class of unknown and repeatable nonlinear discrete-time systems, a model-free iterative learning contro...
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Energy-efficient (green) supercomputing has evolved to improve system reliability and provide better availability and productivity. Green Destiny supercomputing system is a Linux-based cluster that uses low-power comp...
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Energy-efficient (green) supercomputing has evolved to improve system reliability and provide better availability and productivity. Green Destiny supercomputing system is a Linux-based cluster that uses low-power components whose performance could be optimized for supercomputing. Low-power supercomputing focuses on energy efficiency at system integration time, selects low-power chips as the building blocks for power reduction and system reliability, and achieves better performance by scaling up to a larger number of processors. Researchers have implemented various power-aware software prototypes for commodity supercomputing starting with cluster of high-performance, high-power processors that support mechanism called dynamic voltage and frequency scaling (DVFS). Various approaches for parallel supercomputing nodes are studied by aiming execution phases that are not on the critical execution path.
We are currently developing Willow, a shared memory multiprocessor whose design provides system capacity and performance capable of supporting over a thousand commercial microprocessors. Most recently, we have focused...
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This paper presents a flexible controller structure for concurrent processing of memory centric coarse grain data flows. We propose a design flow based on block level pipelining where concurrency among processing bloc...
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This paper develops two theorems concerning the small-signal behavior of nonlinear time-varying networks whose state equations are of the form x= f(x, u, t). The conclusions of the theorems are supported by experiment...
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This paper develops two theorems concerning the small-signal behavior of nonlinear time-varying networks whose state equations are of the form x= f(x, u, t). The conclusions of the theorems are supported by experiments. The input is of the form U(t) + u(t), where the bias U(t) is allowed to be time-varying (typically, slowly varying) and u(t) is the small signal. The bias induces a moving operation point X(t). Given some simple assumptions concerning the linearized small-signal equivalent circuit it is shown that provided u(t) is sufficiently small on [0, ), the state trajectory about the operating point is bounded on [0, ) and tends to zero as u 0. The method of proof also shows that this result applies to some distributed circuits. The second theorem shows that the push-pull connection reduces the distortion due to the nonlinearities of both resistors and energy storing elements. The third part of the paper describes numerical experiments that support the conclusions of the theory and a design procedure for nonlinear networks to be operated in the small-signal mode.
There has recently been much interest in stream processing, both in industry (e.g., Cell, NVIDIA G80, ATI R580) and academia (e.g., Stanford Merrimac, MIT RAW), with stream programs becoming increasingly popular for b...
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Power system simulations that extend over a time period of minutes,hours,or even longer are called extendedterm *** power systems evolve into complex systems with increasing interdependencies and richer dynamic behavi...
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Power system simulations that extend over a time period of minutes,hours,or even longer are called extendedterm *** power systems evolve into complex systems with increasing interdependencies and richer dynamic behaviors across a wide range of timescales,extendedterm simulation is needed for many power system analysis tasks(e.g.,resilience analysis,renewable energy integration,cascading failures),and there is an urgent need for efficient and robust extendedterm simulation *** conventional approaches are insufficient for dealing with the extendedterm simulation of multitimescale *** paper proposes an extendedterm simulation approach based on the semianalytical simulation(SAS)*** accuracy and computational efficiency are backed by SAS's high accuracy in eventdriven simulation,larger and adaptive time steps,and flexible switching between fulldynamic and quasisteadystate(QSS)*** used this proposed extendedterm simulation approach to evaluate bulk power system restoration plans,and it demonstrates satisfactory accuracy and efficiency in this complex simulation task.
This study presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO 2 emissions and global temperature anomalies. Unlike prior re...
This study presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO 2 emissions and global temperature anomalies. Unlike prior research that typically addresses these components in isolation, this work concurrently applies and compares five advanced ML models—Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), Facebook Prophet, and a hybrid CNN-LSTM—alongside two physics-based models: a zero-dimensional Energy Balance Model (EBM) and a simplified General Circulation Model (GCM) adapted from NASA's GISS *** monthly global datasets from January 2000 to April 2024, obtained from the National Oceanic and Atmospheric Administration (NOAA) and the Scripps Institution of Oceanography, the models are evaluated based on predictive accuracy (RMSE, MSE, MAE, R 2 ), scalability, and interpretability. Prophet demonstrated the highest accuracy for CO 2 emission forecasting (RMSE = 0.035), while LSTM achieved the best performance in temperature anomaly prediction (RMSE = 0.086). Physics-based models provided interpretable and computationally efficient long-term projections but lacked short-term *** facilitate reproducibility and practical application, we developed ClimateChange-ML, an open-source software package that implements all proposed models, includes trained weights, and provides full documentation and visualization *** novelty of this work lies in its dual-modeling strategy and comprehensive comparative evaluation, highlighting the complementary strengths of data-driven and physically grounded methods. This integrated approach offers a more holistic framework for climate forecasting across multiple temporal scales, providing valuable insights for both scientific understanding and climate policy planning.
The authors describe a LISP microprocessor which includes over 550 K transistors, has 114 K of on-chip RAM, and runs instructions in a single 30-ns clock cycle. The chip is implemented in 1.25-/spl mu/m double-level-m...
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The authors describe a LISP microprocessor which includes over 550 K transistors, has 114 K of on-chip RAM, and runs instructions in a single 30-ns clock cycle. The chip is implemented in 1.25-/spl mu/m double-level-metal (DLM) CMOS, has 224 pins, and is packaged in a custom pin-grid array. The microinstruction and macroinstruction sets of this chip are compatible with an existing LISP processor. An extensive discussion of test features designed into the processor chip is given.
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