Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Advanced ranking queries on composite data

Ranking and retrieving the best objects from a database based on a set of criteria is a fundamental problem and has received extensive research efforts. With the vast development of data science and engineering, modern data have become increasingly more complex and composite, i.e., objects are routinely assigned multiple types of information. This thesis studies several advanced ranking queries over composite data. In particular, three novel ranking queries are investigated in detail. First, we introduce and study the problem of top-k joins over complex data types. Top-k joins have been extensively studied in relational databases, for the case where the join predicate is equality and the proposed algorithms aim at minimizing the number of accesses from the inputs. However, when collections of complex data types (e.g., spatial or string datasets) are top-k joined, computational cost can easily become the bottleneck. In view of this, we propose a novel evaluation paradigm that minimizes the computational cost without compromising the access cost. The proposed paradigm is applied for the cases of top-k joins on spatial and string attributes, and an analysis is conducted on how to optimize the paradigm for each case. Finally, the proposal is evaluated by extensive experimentation on both real and synthetic data. Next, the problem of point-based trajectory search is investigated. Trajectory data capture the traveling history of moving objects. With the vastly increased volume of trajectory collections, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. This thesis firstly studies distance-to-points trajectory search (DTS) which retrieves the top-k trajectories that pass as close as possible to a given set of query points. For this, the state-of-the-art is advanced by a hybrid method combining existing approaches and an alternative yet more efficient spatial range-based approach. Second, the continuous counterpart of DTS is investigated where the query is long-standing and the results need to be maintained whenever updates occur to the query and/or the data. Third, two practical variants of DTS, which take into account the temporal characteristics of the searched trajectories, are proposed and studied. Extensive experiments are conducted to evaluate the proposed algorithms. Finally, the problem of location-aware keyword query suggestion (LKS) is proposed and studied. Keyword suggestion helps users to access relevant information without having to know how to precisely express their queries. Existing techniques consider solely the keyword proximity and neglect the spatial distance of a user to the retrieved results. However, the relevance of search results in many applications (e.g., location-based services) is known to be correlated with their spatial proximity to the query issuer. This thesis presents an LKS framework, where a weighted keyword-document graph is designed to capture both the semantic relevance between keyword queries and the spatial distance between the resulting documents and the user. The graph is browsed in a random-walk-with-restart fashion, and to make it scalable, we propose a partition-based approach which vastly outperforms the baseline. The appropriateness of the LKS framework and the performance of the algorithms are evaluated extensively using real data.published_or_final_versionComputer ScienceDoctoralDoctor of Philosoph

IL-33 ameliorates experimental colitis involving regulation of autophagy of macrophages in mice

Abstract Background Previously, we have demonstrated that IL-33 administration protecting TNBS-induced experimental colitis is associated with facilitation of Th2/Tregs responses in mice. However, whether IL-33 regulates autophagy to ameliorate experimental colitis is unclear. Results IL-33 administration (2 μg/day, intraperitoneal injection), while facilitating Th2/Tregs responses, also enhances the autophagy in mice with TNBS-induced colitis as well as macrophages. In the meantime, we observed that inhibition of the autophagy with 3-methyladenine (3-MA) (24 mg/kg, intraperitoneal injection) in mice exacerbates TNBS-induced experimental colitis. On the contrary, administration of rapamycin (2 mg/kg,intragastric administration), an autophagy-enhancer, alleviates the colitis in mice. In vivo, Immunofluorescence analysis revealed that TNBS combined with IL-33 enhanced the autophagy of macrophages in the inflammatory gut tissue. In vitro, treatment with IL-33 promoted the autophagy of macrophages generated from bone marrow cells in dose-dependant manner. Furthermore, the effect of autophagy-enhancement by IL-33 is TLR4 signaling pathway dependant. Our notion was further confirmed by IL-33-deficient bone marrow-derived macrophages cells. Conclusions IL-33 regulates the autophagy is a new immunoregulatory property on TNBS-induced experimental colitis in mice

Aggregation‐induced narrowband isomeric fluorophores for ultraviolet nondoped OLEDs by engineering multiple nonbonding interactions

Abstract Traditional donor‐acceptor type organic luminescent materials usually suffer from unfavorable spectral broadening and fluorescence quenching problems arising from strong inter/intra‐chromophore interactions in aggregation state. Here, two ultraviolet carbazole‐pyrimidine isomers (named o‐DCz‐Pm and m‐DCz‐Pm) with novel aggregation‐induced narrowband phenomenon are constructed and systematic investigated by experiments and theoretical simulations. Benefitting from strengthened steric hindrance and multiple noncovalent interactions, the nonradiative decay, vibrational motion, and structural relaxation of singlet state can be effectively suppressed in aggregation state. Consequently, the electroluminescence peak of 397 nm, full width at half maximum of 21 nm and external quantum efficiency of 3.4% are achieved simultaneously in nondoped o‐DCz‐Pm‐based device. This work paves an avenue toward the development of high‐performance narrowband nondoped ultraviolet materials and organic light‐emitting diodes