Constrained-Random Stimuli Generation for Post-Silicon Validation

Due to the growing complexity of integrated circuits, significant efforts are undertaken to ensure the design and implementation meet the specification and quality requirements both at the pre-silicon verification stage (before tape-out), as well as at the post-silicon validation stage (on the silicon prototypes). In particular, the constrained-random methods, which subject the design to a large volume of random, yet functionally-compliant stimuli, are widely employed during the pre-silicon stage. Hardware description languages, such as SystemVerilog, have standardized and well-defined features to formalize the constraints including format, sequence control and distribution. Nonetheless, it is not obvious how such features can be efficiently leveraged at the post-silicon stage. In this dissertation, a systematic methodology is proposed to support constrained-random generation and application during post-silicon validation. This includes both software algorithms and on-chip hardware structures. The proposed software translates functional constraints from SystemVerilog into a cube-based representation. A method to design in-field programmable signal generators, which are placed on-chip, can directly expand compacted cubes to extensive random, yet functionally compliant, sequences for post-silicon validation. This approach is extended to also support sequential constraints, as well as the control of the stimuli distribution.DissertationDoctor of Philosophy (PhD

Digital Light Processing 3D printing of Thermosets via Reversible Addition-Fragmentation Chain Transfer Polymerization

Digital light processing (DLP) 3D printing is an efficient additive manufacturing technique for the fabrication of 3D objects with intricate structures. However, current photocurable resins for DLP printing are mainly based on uncontrolled radical polymerizations associated with limited control over formed networks and a high degree of heterogeneity in macromolecular structures. This uncontrolled process could only afford narrow manipulation over bulk material properties, restricting the wide applications of DLP 3D-printed materials. To access versatile control over bulk material properties, reversible-deactivation radical polymerization (RDRP) has been widely applied to tune the macromolecular structures of polymer networks. In particular, photo-mediated reversible addition-fragmentation chain transfer (photoRAFT) polymerization has been employed to design photocurable resins for DLP printing of materials with homogeneous networks and enhanced properties. To deepen the understanding of using photoRAFT polymerization in designing photocurable resins for DLP 3D printing processes, this body of work first investigated the role of RAFT agent architectures (i.e., different number of arms) in a visible-light-mediated photoinduced electron/energy transfer (PET)-RAFT system. The monofunctional RAFT agent resulted in optimal mechanical properties among the studied candidates. Subsequently, the optimized monofunctional RAFT agent was employed in silica nanoparticle-loaded composite photocurable resins based on type I-initiated RAFT polymerization, which produced composite materials with more homogeneous networks and improved tensile properties. As an extension of small molecule RAFT agents, macro-chain transfer agents (macroCTAs) were subsequently utilized to design photocurable resins for printing nanostructured materials via polymerization-induced microphase separation (PIMS). Similarly, macroCTA with 1, 2, and 4-arm were used to study the architecture effect in the PIMS process. The results demonstrated that the nanostructural domain sizes were precisely defined by the arm length of macroCTAs, while the 2 and 4-arm macroCTAs led to phase-inverted morphologies which were not observed in the case of using 1-arm macroCTAs. Afterward, diblock macroCTAs with varied compositions and sequences were employed in the PIMS printing system. Tuning ratio of network-incompatible A and B blocks in macroCTA enables a transition from bicontinuous to less connected morphologies. More importantly, the macroCTA block sequence was also found to significantly affect the PIMS process, nanostructure, and bulk properties of 3D printed materials

Methylation by Set9 Modulates FoxO3 Stability and Transcriptional Activity

The FoxO family of transcription factors plays an important role in longevity and tumor suppression by regulating the expression of a wide range of target genes. FoxO3 has recently been found to be associated with extreme longevity in humans and to regulate the homeostasis of adult stem cell pools in mammals, which may contribute to longevity. The activity of FoxO3 is controlled by a variety of post-translational modifications that have been proposed to form a ‘code’ affecting FoxO3 subcellular localization, DNA binding ability, protein-protein interactions and protein stability. Lysine methylation is a crucial post-translational modification on histones that regulates chromatin accessibility and is a key part of the ‘histone code’. However, whether lysine methylation plays a role in modulating FoxO3 activity has never been examined. Here we show that the methyltransferase Set9 directly methylates FoxO3 in vitro and in cells. Using a combination of tandem mass spectrometry and methyl-specific antibodies, we find that Set9 methylates FoxO3 at a single residue, lysine 271, a site previously known to be deacetylated by Sirt1. Methylation of FoxO3 by Set9 decreases FoxO3 protein stability, while moderately increasing FoxO3 transcriptional activity. The modulation of FoxO3 stability and activity by methylation may be critical for fine-tuning cellular responses to stress stimuli, which may in turn affect FoxO3's ability to promote tumor suppression and longevity

When Poverty Reduction Meets Democracy: An Investigation into the Use of Different Evaluation Methods for Assessing the Effectiveness of a Social Program

This paper evaluates the minimum living standard guarantee program (Dibao) in rural China using several methods including the income approach, the multidimensional pover ty approach, and a proxy means test approach. We find that the targeting accuracy of the program appears greater the more comprehensive the evaluation method used—but all these methods find low levels of targeting accuracy. Because Dibao fund allocation is largely decided by the villagers, who take a more holistic view in selecting “poor” households than the various evaluation methods, we argue that the low targeting efficacy may be due to the lack of comprehensive evaluation method, as opposed to the low targeting of the program itself. This paper argues that the community-based targeting used by the Dibao program may be a better way to combat pover ty in many developing countries, as it requires less administrative capacity and overcomes the difficulties of identifying poor households that qualify for assistance.</p

New RNN Algorithms for Different Time-Variant Matrix Inequalities Solving Under Discrete-Time Framework

Abstract A series of discrete time-variant matrix inequalities is generally regarded as one of the challenging problems in science and engineering fields. As a discrete time-variant problem, the existing solving schemes generally need the theoretical support under the continuous-time framework, and there is no independent solving scheme under the discrete-time framework. The theoretical deficiency of solving scheme greatly limits the theoretical research and practical application of discrete time-variant matrix inequalities. In this article, new discrete-time recurrent neural network (RNN) algorithms are proposed, analyzed, and investigated for solving different time-variant matrix inequalities under the discrete-time framework, including discrete time-variant matrix vector inequality (discrete time-variant MVI), discrete time-variant generalized matrix inequality (discrete time-variant GMI), discrete time-variant generalized-Sylvester matrix inequality (discrete time-variant GSMI), and discrete time-variant complicated-Sylvester matrix inequality (discrete time-variant CSMI), and all solving processes are based on the direct discretization thought. Specifically, first of all, four discrete time-variant matrix inequalities are presented as the target problems of these researches. Second, for solving such problems, we propose corresponding discrete-time recurrent neural network (RNN) (DT-RNN) algorithms (termed DT-RNN-MVI algorithm, DT-RNN-GMI algorithm, DT-RNN-GSMI algorithm, and DT-RNN-CSMI algorithm), which are different from the traditional DT-RNN design thought because second-order Taylor expansion is applied to derive the DT-RNN algorithms. This creative process avoids the intervention of continuous-time framework. Then, theoretical analyses are presented, which show the convergence and precision of the DT-RNN algorithms. Abundant numerical experiments are further carried out, which further confirm the excellent properties of the DT-RNN algorithms.Abstract A series of discrete time-variant matrix inequalities is generally regarded as one of the challenging problems in science and engineering fields. As a discrete time-variant problem, the existing solving schemes generally need the theoretical support under the continuous-time framework, and there is no independent solving scheme under the discrete-time framework. The theoretical deficiency of solving scheme greatly limits the theoretical research and practical application of discrete time-variant matrix inequalities. In this article, new discrete-time recurrent neural network (RNN) algorithms are proposed, analyzed, and investigated for solving different time-variant matrix inequalities under the discrete-time framework, including discrete time-variant matrix vector inequality (discrete time-variant MVI), discrete time-variant generalized matrix inequality (discrete time-variant GMI), discrete time-variant generalized-Sylvester matrix inequality (discrete time-variant GSMI), and discrete time-variant complicated-Sylvester matrix inequality (discrete time-variant CSMI), and all solving processes are based on the direct discretization thought. Specifically, first of all, four discrete time-variant matrix inequalities are presented as the target problems of these researches. Second, for solving such problems, we propose corresponding discrete-time recurrent neural network (RNN) (DT-RNN) algorithms (termed DT-RNN-MVI algorithm, DT-RNN-GMI algorithm, DT-RNN-GSMI algorithm, and DT-RNN-CSMI algorithm), which are different from the traditional DT-RNN design thought because second-order Taylor expansion is applied to derive the DT-RNN algorithms. This creative process avoids the intervention of continuous-time framework. Then, theoretical analyses are presented, which show the convergence and precision of the DT-RNN algorithms. Abundant numerical experiments are further carried out, which further confirm the excellent properties of the DT-RNN algorithms

Influence of scale effect on flow field offset for ships in confined waters

To investigate the flow field characteristics of full-scale ships advancing through confined waters, the international standard container ship (KRISO Container Ship) was considered as a research object in this study. Using the RANS equation, the volume of fluid method and the body force method were selected to investigate the hydrodynamic characteristics of a model-scale ship (the model-scale ratio λ=31.6) and a full-scale ship advancing through confined waters at low speed. A virtual disk was used in the full-scale model to determine the influence of the propeller on the ship’s flow field. First, the feasibility of the numerical calculations was verified. This proves the feasibility of the numerical and grid division methods. The self-propulsion point of the full-scale ship at Fr=0.108 is determined. The calculation cases of model-scale and full-scale ships (with or without virtual disks) at different water depths and distances between the ship and the shore were calculated, and the changes in the hull surface pressure, the flow field around the ship, and the wake fraction near the ship propeller disk in different calculation cases were determined and compared. The variations in the surge force, sway force, and yaw moment between the model- scale and full-scale ships were generally consistent. In very shallow water (H/T=1.3), the non-dimensional force and moment coefficients for model-scale ships increase more rapidly with decreasing distance from shore, suggesting that using model-scale ships to investigate the wall effect in very shallow water will result in predictions that are biased towards safety. By comparing full-scale ships with and without propellers, it was discovered that the surge force, sway force, and yaw moment were marginally greater in the propeller-equipped ship due to the suction effect, and the accompanying flow before and after the propeller was slightly smaller, with less asymmetry

Targeting Histone Reader ENL with PROTAC Degrader

ENL is a histone reader that is part of the super elongation complex and is primarily involved in chromatin and gene expression regulation. ENL has also been found to play an essential role in the progression of a spectrum of acute myeloid leukemia (AML). Some hotspot mutations of its reader module, the YEATS domain, have been found to be oncogenic. For this reason, we explored the efficacy of a newly developed ENL PROTAC degrader in its ability to degrade wild-type and mutant ENL protein and suppress oncogene expression in cancer cells

An Efficient Resilient MPC Scheme via Constraint Tightening against Cyberattacks: Application to Vehicle Cruise Control

We propose a novel framework for designing a resilient Model Predictive Control (MPC) targeting uncertain linear systems under cyber attack. Assuming a periodic attack scenario, we model the system under Denial of Service (DoS) attack, also with measurement noise, as an uncertain linear system with parametric and additive uncertainty. To detect anomalies, we employ a Kalman filter-based approach. Then, through our observations of the intensity of the launched attack, we determine a range of possible values for the system matrices, as well as establish bounds of the additive uncertainty for the equivalent uncertain system. Leveraging a recent constraint tightening robust MPC method, we present an optimization-based resilient algorithm. Accordingly, we compute the uncertainty bounds and corresponding constraints offline for various attack magnitudes. Then, this data can be used efficiently in the MPC computations online. We demonstrate the effectiveness of the developed framework on the Adaptive Cruise Control (ACC) problem.Comment: To Appear in ICINCO 202

MiLMo:Minority Multilingual Pre-trained Language Model

Pre-trained language models are trained on large-scale unsupervised data, and they can fine-turn the model only on small-scale labeled datasets, and achieve good results. Multilingual pre-trained language models can be trained on multiple languages, and the model can understand multiple languages at the same time. At present, the search on pre-trained models mainly focuses on rich resources, while there is relatively little research on low-resource languages such as minority languages, and the public multilingual pre-trained language model can not work well for minority languages. Therefore, this paper constructs a multilingual pre-trained model named MiLMo that performs better on minority language tasks, including Mongolian, Tibetan, Uyghur, Kazakh and Korean. To solve the problem of scarcity of datasets on minority languages and verify the effectiveness of the MiLMo model, this paper constructs a minority multilingual text classification dataset named MiTC, and trains a word2vec model for each language. By comparing the word2vec model and the pre-trained model in the text classification task, this paper provides an optimal scheme for the downstream task research of minority languages. The final experimental results show that the performance of the pre-trained model is better than that of the word2vec model, and it has achieved the best results in minority multilingual text classification. The multilingual pre-trained model MiLMo, multilingual word2vec model and multilingual text classification dataset MiTC are published on http://milmo.cmli-nlp.com/