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Energy ◽  
2022 ◽  
Vol 239 ◽  
pp. 122240
Author(s):  
Yongming Han ◽  
Xiaoyi Lou ◽  
Mingfei Feng ◽  
Zhiqiang Geng ◽  
Liangchao Chen ◽  
...  

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-31
Author(s):  
Masaomi Yamaguchi ◽  
Kazutaka Matsuda ◽  
Cristina David ◽  
Meng Wang

We propose a technique for synthesizing bidirectional programs from the corresponding unidirectional code plus a few input/output examples. The core ideas are: (1) constructing a sketch using the given unidirectional program as a specification, and (2) filling the sketch in a modular fashion by exploiting the properties of bidirectional programs. These ideas are enabled by our choice of programming language, HOBiT, which is specifically designed to maintain the unidirectional program structure in bidirectional programming, and keep the parts that control bidirectional behavior modular. To evaluate our approach, we implemented it in a tool called Synbit and used it to generate bidirectional programs for intricate microbenchmarks, as well as for a few larger, more realistic problems. We also compared Synbit to a state-of-the-art unidirectional synthesis tool on the task of synthesizing backward computations.


2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-29
Author(s):  
Rohan Bavishi ◽  
Caroline Lemieux ◽  
Koushik Sen ◽  
Ion Stoica

While input-output examples are a natural form of specification for program synthesis engines, they can be imprecise for domains such as table transformations. In this paper, we investigate how extracting readily-available information about the user intent behind these input-output examples helps speed up synthesis and reduce overfitting. We present Gauss, a synthesis algorithm for table transformations that accepts partial input-output examples, along with user intent graphs. Gauss includes a novel conflict-resolution reasoning algorithm over graphs that enables it to learn from mistakes made during the search and use that knowledge to explore the space of programs even faster. It also ensures the final program is consistent with the user intent specification, reducing overfitting. We implement Gauss for the domain of table transformations (supporting Pandas and R), and compare it to three state-of-the-art synthesizers accepting only input-output examples. We find that it is able to reduce the search space by 56×, 73× and 664× on average, resulting in 7×, 26× and 7× speedups in synthesis times on average, respectively.


2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-30
Author(s):  
Son Tuan Vu ◽  
Albert Cohen ◽  
Arnaud De Grandmaison ◽  
Christophe Guillon ◽  
Karine Heydemann

Software protections against side-channel and physical attacks are essential to the development of secure applications. Such protections are meaningful at machine code or micro-architectural level, but they typically do not carry observable semantics at source level. This renders them susceptible to miscompilation, and security engineers embed input/output side-effects to prevent optimizing compilers from altering them. Yet these side-effects are error-prone and compiler-dependent. The current practice involves analyzing the generated machine code to make sure security or privacy properties are still enforced. These side-effects may also be too expensive in fine-grained protections such as control-flow integrity. We introduce observations of the program state that are intrinsic to the correct execution of security protections, along with means to specify and preserve observations across the compilation flow. Such observations complement the input/output semantics-preservation contract of compilers. We introduce an opacification mechanism to preserve and enforce a partial ordering of observations. This approach is compatible with a production compiler and does not incur any modification to its optimization passes. We validate the effectiveness and performance of our approach on a range of benchmarks, expressing the secure compilation of these applications in terms of observations to be made at specific program points.


Author(s):  
Xinyue Lin ◽  
Haoran Pan ◽  
Lingli Qi ◽  
Yi-Shuai Ren ◽  
Basil Sharp ◽  
...  

2021 ◽  
pp. 1-11
Author(s):  
Bharat Verma ◽  
Prabin Kumar Padhy

2021 ◽  
Author(s):  
Alberto Carignano ◽  
Dai Hua Chen ◽  
Cannon Mallory ◽  
Clay Wright ◽  
Georg Seelig ◽  
...  

Division of labor between cells is ubiquitous in biology but the use of multi-cellular consortia for engineering applications is only beginning to be explored. A significant advantage of multi-cellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three- and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.


2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Chudong Pan ◽  
Liwen Zhang ◽  
Zhuo Sun

A novel method is proposed based on the transmissibility concept and matrix regularization for indirectly measuring the structural responses. The inputs are some measured responses that are obtained via physical sensors. The outputs are the structural responses corresponding to some critical locations where no physical sensors are installed. Firstly, the transmissibility concept is introduced for expressing the relationship between the measured responses and the indirectly measured ones. Herein, a transmissibility matrix is formulated according to the theory of force identification under unknown initial conditions. Then, in order to reduce the size of the transmissibility matrix, structural responses are reshaped in a form of a matrix by using the concept of moving time windows. According to the matrix form of input-output relationship, indirect reconstruction of responses is boiled down to an optimization equation. Since inverse problem may be ill-conditioned, matrix regularization such as F-norm regularization is then recommended for improving the optimization problem. Herein, the penalty function is defined by using a weighted sum of two F-norm values, which correspond to the estimated responses of physical sensors and the ones of the concerned critical locations, respectively. Numerical simulations and experimental studies are finally carried out for verifying the effectiveness and feasibility of the proposed method. Some results show that the proposed method can be applied for indirectly measuring the responses with good robustness.


Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2503
Author(s):  
Minseon Cho ◽  
Donghyun Kang

Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms.


2021 ◽  
Author(s):  
Ádám Magó ◽  
Noémi Kis ◽  
Balázs Lükó ◽  
Judit K Makara

Proper integration of different inputs targeting the dendritic tree of CA3 pyramidal cells (CA3PCs) is critical for associative learning and recall. Dendritic Ca2+ spikes have been proposed to perform associative computations in other PC types, by detecting conjunctive activation of different afferent input pathways, initiating afterdepolarization (ADP) and triggering burst firing. Implementation of such operations fundamentally depends on the actual biophysical properties of dendritic Ca2+ spikes; yet little is known about these properties in dendrites of CA3PCs. Using dendritic patch-clamp recordings and two-photon Ca2+ imaging in acute slices from male rats we report that, unlike CA1PCs, distal apical trunk dendrites of CA3PCs exhibit distinct forms of dendritic Ca2+ spikes. Besides ADP-type global Ca2+ spikes, a majority of dendrites expresses a novel, fast Ca2+ spike type that is initiated locally without backpropagating action potentials, can recruit additional Na+ currents, and is compartmentalized to the activated dendritic subtree. Occurrence of the different Ca2+ spike types correlates with dendritic structure, indicating morpho-functional heterogeneity among CA3PCs. Importantly, ADPs and dendritically initiated spikes produce opposing somatic output: bursts versus strictly single action potentials, respectively. The uncovered variability of dendritic Ca2+ spikes may underlie heterogeneous input-output transformation and bursting properties of CA3PCs, and might specifically contribute to key associative and non-associative computations performed by the CA3 network.


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