Search

Soft robots, while highly adaptable to diverse environments through various actuation methods, still face significant performance boundary due to the inherent properties of materials. These limitations manifest in the challenge of guaranteeing rapid response and large-scale movements simultaneously, ultimately restricting the robots' absolute speed and overall efficiency. In this paper, we introduce a high-frequency pneumatic oscillator (HIPO) to overcome these challenges. Through a collision-induced phase resetting mechanism, our HIPO leverages event-based nonlinearity to trigger self-oscillation of pneumatic actuator, which positively utilizes intrinsic characteristics of materials. This enables the system to spontaneously generate periodic control signals and directly produce motion responses, eliminating the need for incorporating external actuation components. By efficiently and rapidly converting internal energy of airflow into the kinetic energy of robots, HIPO achieves a frequency of up to 20 Hz. Furthermore, we demonstrate the versatility and high-performance capabilities of HIPO through bio-inspired robots: an insect-like fast-crawler (with speeds up to 50.27 cm/s), a high-frequency butterfly-like wing-flapper, and a maneuverable duck-like swimmer. By eliminating external components and seamlessly fusing signal generation, energy conversion, and motion output, HIPO unleashes rapid and efficient motion, unlocking potential for high-performance soft robotics.

Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the potential to automate the data wrangling process to reduce analysts' overhead by translating user intents into executable code. Precisely generating data wrangling code necessitates a comprehensive consideration of the rich context present in notebooks, including textual context, code context and data context. However, notebooks often interleave multiple non-linear analysis tasks into linear sequence of code blocks, where the contextual dependencies are not clearly reflected. Directly training models with source code blocks fails to fully exploit the contexts for accurate wrangling code generation. To bridge the gap, we aim to construct a high quality datasets with clear and rich contexts to help training models for data wrangling code generation tasks. In this work, we first propose an automated approach, CoCoMine to mine data-wrangling code generation examples with clear multi-modal contextual dependency. It first adopts data flow analysis to identify the code blocks containing data wrangling codes. Then, CoCoMine extracts the contextualized datawrangling code examples through tracing and replaying notebooks. With CoCoMine, we construct CoCoNote, a dataset containing 58,221 examples for Contextualized Data-wrangling Code generation in Notebooks. To demonstrate the effectiveness of our dataset, we finetune a range of pretrained code models and prompt various large language models on our task. Furthermore, we also propose DataCoder, which encodes data context and code&textual contexts separately to enhance code generation. Experiment results demonstrate the significance of incorporating data context in data-wrangling code generation and the effectiveness of our model. We release code and data at url..., Comment: To appear at ASE 2024

Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers. In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang. To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset.

Permanent magnets are fascinating. They generate magnetic field and act as the key component in compasses, motors, speakers for practical applications. They are also made into magnetic toys for entertainment and education with magical magnetic tricks. Some of these tricks fueled the recent upsurge for magnetic-responsive objects including but not limited to a LK-99 mixture. A lack of full consideration of the magnetic interactions seems to be the reason why some tricks like "half levitation" is misinterpreted, and the situation is not really rectified in the following replications and discussions. Here I would like to go through some less commonly taught facts about real-world permanent magnets starting from the nonuniformity to their combined effects with other factors. Based on these facts, I will also break down the situation of some examples of how permanent magnets diversely interact even with ordinary objects, as one may have seen from recent sources. I believe this discussion is useful both in an informative and educational way, helping those who want to think deeper about magnetic mechanism beyond a simple attract/repel terminology., Comment: 10 pages, 5 figures

Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheet-tables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based analytics tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Power-BI/Tableau forums. We develop an Auto-Tables system that can automatically synthesize pipelines with multi-step transformations (in Python or other languages), to transform non-relational tables into standard relational forms for downstream analytics, obviating the need for users to manually program transformations. We compile an extensive benchmark for this new task, by collecting 244 real test cases from user spreadsheets and online forums. Our evaluation suggests that Auto-Tables can successfully synthesize transformations for over 70% of test cases at interactive speeds, without requiring any input from users, making this an effective tool for both technical and non-technical users to prepare data for analytics., Comment: full version of a paper accepted to VLDB 2023

Supercrystalline nanocomposites (SCNCs) are inorganic-organic hybrid materials with a unique periodic nanostructure, and as such they have been gaining growing attention for their intriguing functional properties and parallelisms with hierarchical biomaterials. Their mechanical behavior remains, however, poorly understood, even though its understanding and control are of paramount importance to allow SCNCs implementation into devices. An important aspect that has not been tackled yet is their time-dependent deformation behavior, which is nevertheless expected to play an important role in materials containing such a distribution of organic phase. Hereby, we report on the creep of ceramic-organic SCNCs with varying degrees of organic crosslinking, as assessed via nanoindentation. Creep strains and their partial recoverability are observed, hinting at the co-presence of viscoelasticity and viscoplasticity, and a clear effect of crosslinking in decreasing the overall material deformability. We rationalize our experimental observations with the analysis of stress exponent and activation volume, resulting in a power-law breakdown behavior and governing deformation mechanisms occurring at the organic sub-nm interfaces scale, in terms of organic ligands rearrangement. The set of results is reinforced by the evaluation of the strain rate sensitivity via strain rate jump tests, and the assessment of the effect of oscillations during continuous stiffness measurement mode.

Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress is whether a model can generate code that can correctly execute to solve the task. However, due to the lack of an evaluation dataset that directly supports execution-based model evaluation, existing work relies on code surface form similarity metrics (e.g., BLEU, CodeBLEU) for model selection, which can be inaccurate. To remedy this, we introduce ExeDS, an evaluation dataset for execution evaluation for data science code generation tasks. ExeDS contains a set of 534 problems from Jupyter Notebooks, each consisting of code context, task description, reference program, and the desired execution output. With ExeDS, we evaluate the execution performance of five state-of-the-art code generation models that have achieved high surface-form evaluation scores. Our experiments show that models with high surface-form scores do not necessarily perform well on execution metrics, and execution-based metrics can better capture model code generation errors. Source code and data can be found at https://github.com/Jun-jie-Huang/ExeDS, Comment: Accepted by the 4th Workshop on Data Science with Human-in-the-loop (DaSH) at EMNLP 2022

Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present ConstrOpt, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, ConstrOpt leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, ConstrOpt employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that ConstrOpt can discover numerous data constraints from code analysis and improve real-world application performance significantly.

We report here a linear fluoride-bridged Double-Decker Dysprosium metallacrown with high-performance SMM. The successful introduction of stronger magnetic exchange-coupling in the axial direction, which is collinear with the Ising-type magnetic anisotropy axis of dysprosium ions, plays a pivotal role in improving the SMM properties of the double-decker Dysprosium metallacrown.

Single-molecule magnets (SMMs) exhibiting open hysteresis loops may potentially apply to molecule-based information processing and storage. However, the capacity to retain magnetic memory is always limited by zero-field quantum tunneling of magnetization (QTM). Herein, a well-designed dysprosium metallacrown SMM, consisting of an endohedral approximate linear F-Dy-F strong anisotropic moiety in a peripheral [15-MCNi-5] metallacrown (MC), is reported with the largest reversal barrier of 1060 cm-1 among d-f SMMs. Rectangle-like hysteresis loops are observed with the huge squareness (remanence/saturation magnetization) up to 97% at 2 K. More importantly, zero-field QTM step is phenomenologically removed by minimizing the dipole coupling and hyperfine interactions. The results demonstrate for the first time that zero-field QTM step can be eliminated via manipulating the ligand field and vanishing the external magnetic perturbations, which illuminates a promising blueprint for developing high-performance SMMs.

Hotspots, a small set of tuples frequently read/written by a large number of transactions, cause contention in a concurrency control protocol. While a hotspot may comprise only a small fraction of a transaction's execution time, conventional strict two-phase locking allows a transaction to release lock only after the transaction completes, which leaves significant parallelism unexploited. Ideally, a concurrency control protocol serializes transactions only for the duration of the hotspots, rather than the duration of transactions. We observe that exploiting such parallelism requires violating two-phase locking. In this paper, we propose Bamboo, a new concurrency control protocol that can enable such parallelism by modifying the conventional two-phase locking, while maintaining the same guarantees in correctness. We thoroughly analyzed the effect of cascading aborts involved in reading uncommitted data and discussed optimizations that can be applied to further improve the performance. Our evaluation on TPC-C shows a performance improvement up to 4x compared to the best of pessimistic and optimistic baseline protocols. On synthetic workloads that contain a single hotspot, Bamboo achieves a speedup up to 19x over baselines.