43 research outputs found
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
Recently, diffusion models have emerged as a new paradigm for generative
models. Despite the success in domains using continuous signals such as vision
and audio, adapting diffusion models to natural language is under-explored due
to the discrete nature of texts, especially for conditional generation. We
tackle this challenge by proposing DiffuSeq: a diffusion model designed for
sequence-to-sequence (Seq2Seq) text generation tasks. Upon extensive evaluation
over a wide range of Seq2Seq tasks, we find DiffuSeq achieving comparable or
even better performance than six established baselines, including a
state-of-the-art model that is based on pre-trained language models. Apart from
quality, an intriguing property of DiffuSeq is its high diversity during
generation, which is desired in many Seq2Seq tasks. We further include a
theoretical analysis revealing the connection between DiffuSeq and
autoregressive/non-autoregressive models. Bringing together theoretical
analysis and empirical evidence, we demonstrate the great potential of
diffusion models in complex conditional language generation tasks. Code is
available at \url{https://github.com/Shark-NLP/DiffuSeq}Comment: ICLR 2023 camera read
Registered Functional Encryptions from Pairings
This work initiates the study of concrete registered functional encryption (Reg-FE) beyond ``all-or-nothing\u27\u27 functionalities:
- We build the first Reg-FE for linear function or inner-product evaluation (Reg-IPFE) from pairings. The scheme achieves adaptive IND-security under -Lin assumption in the prime-order bilinear group. A minor modification yields the first Registered Inner-Product Encryption (Reg-IPE) scheme from -Lin assumption. Prior work achieves the same security in the generic group model.
-We build the first Reg-FE for quadratic function (Reg-QFE) from pairings. The scheme achieves very selective simulation-based security (SIM-security) under bilateral -Lin assumption in the prime-order bilinear group. Here, ``very selective\u27\u27 means that the adversary claims challenge messages, all quadratic functions to be registered and all corrupted users at the beginning.
Besides focusing on the compactness of the master public key and helper keys, we also aim for compact ciphertexts in Reg-FE.
Let be the number of slots and be the input size. Our first Reg-IPFE has weakly compact ciphertexts of size while our second Reg-QFE has compact ciphertexts of size . Technically, for our first Reg-IPFE, we employ nested dual-system method within the context of Reg-IPFE; for our second Reg-QFE, we follow Wee\u27s ``IPFE-to-QFE\u27\u27 transformation [TCC\u27 20] but devise a set of new techniques that make our pairing-based Reg-IPFE compatible.
Along the way, we introduce a new notion named Pre-Constrained Registered IPFE which generalizes slotted Reg-IPFE by constraining the form of functions that can be registered
SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model
Simulation plays a critical role in the research and development of
autonomous driving and intelligent transportation systems. However, the current
simulation platforms exhibit limitations in the realism and diversity of agent
behaviors, which impede the transfer of simulation outcomes to the real world.
In this paper, we propose a generative driver agent simulation framework based
on large language models (LLMs), capable of perceiving complex traffic
scenarios and providing realistic driving maneuvers. Notably, we conducted
interviews with 24 drivers and used their detailed descriptions of driving
behavior as chain-of-thought prompts to develop a `coach agent' module, which
can evaluate and assist driver agents in accumulating driving experience and
developing human-like driving styles. Through practical simulation experiments
and user experiments, we validate the feasibility of this framework in
generating reliable driver agents and analyze the roles of each module. The
results show that the framework with full architect decreased the collision
rate by 81.04% and increased the human-likeness by 50%. Our research proposes
the first urban context driver agent simulation framework based on LLMs and
provides valuable insights into the future of agent simulation for complex
tasks.Comment: 12 pages, 8 figure
TouchEditor: Interaction design and evaluation of a flexible touchpad for text editing of head-mounted displays in speech-unfriendly environments
A text editing solution that adapts to speech-unfriendly (inconvenient to speak or difficult to recognize speech) environments is essential for head-mounted displays (HMDs) to work universally. For existing schemes, e.g., touch bar, virtual keyboard and physical keyboard, there are shortcomings such as insufficient speed, uncomfortable experience or restrictions on user location and posture. To mitigate these restrictions, we propose TouchEditor, a novel text editing system for HMDs based on a flexible piezoresistive film sensor, supporting cursor positioning, text selection, text retyping and editing commands (i.e., Copy, Paste, Delete, etc.). Through literature overview and heuristic study, we design a pressure-controlled menu and a shortcut gesture set for entering editing commands, and propose an area-and-pressure-based method for cursor positioning and text selection that skillfully maps gestures in different areas and with different strengths to cursor movements with different directions and granularities. The evaluation results show that TouchEditor i) adapts to various contents and scenes well with a stable correction speed of 0.075 corrections per second; ii) achieves 95.4% gesture recognition accuracy; iii) reaches a considerable level with a mobile phone in text selection tasks. The comparison results with the speech-dependent EYEditor and the built-in touch bar further prove the flexibility and robustness of TouchEditor in speech-unfriendly environments
The genome evolution and domestication of tropical fruit mango
Background: Mango is one of the world’s most important tropical fruits. It belongs to the family Anacardiaceae, which includes several other economically important species, notably cashew, sumac and pistachio from other genera. Many species in this family produce family-specific urushiols and related phenols, which can induce contact dermatitis.
Results: We generate a chromosome-scale genome assembly of mango, providing a reference genome for the Anacardiaceae family. Our results indicate the occurrence of a recent whole-genome duplication (WGD) event in mango. Duplicated genes preferentially retained include photosynthetic, photorespiration, and lipid metabolic genes that may have provided adaptive advantages to sharp historical decreases in atmospheric carbon dioxide and global temperatures. A notable example of an extended gene family is the chalcone synthase (CHS) family of genes, and particular genes in this family show universally higher expression in peels than in flesh, likely for the biosynthesis of urushiols and related phenols. Genome resequencing reveals two distinct groups of mango varieties, with commercial varieties clustered with India germplasms and demonstrating allelic admixture, and indigenous varieties from Southeast Asia in the second group. Landraces indigenous in China formed distinct clades, and some showed admixture in genomes.
Conclusions: Analysis of chromosome-scale mango genome sequences reveals photosynthesis and lipid metabolism are preferentially retained after a recent WGD event, and expansion of CHS genes is likely associated with urushiol biosynthesis in mango. Genome resequencing clarifies two groups of mango varieties, discovers allelic admixture in commercial varieties, and shows distinct genetic background of landraces
Temperature-insensitive refractive index sensing by use of micro Fabry–Pérot cavity based on simplified hollow-core photonic crystal fiber
The genome evolution and domestication of tropical fruit mango
Background: Mango is one of the world’s most important tropical fruits. It belongs to the family Anacardiaceae, which includes several other economically important species, notably cashew, sumac and pistachio from other genera. Many species in this family produce family-specific urushiols and related phenols, which can induce contact dermatitis.
Results: We generate a chromosome-scale genome assembly of mango, providing a reference genome for the Anacardiaceae family. Our results indicate the occurrence of a recent whole-genome duplication (WGD) event in mango. Duplicated genes preferentially retained include photosynthetic, photorespiration, and lipid metabolic genes that may have provided adaptive advantages to sharp historical decreases in atmospheric carbon dioxide and global temperatures. A notable example of an extended gene family is the chalcone synthase (CHS) family of genes, and particular genes in this family show universally higher expression in peels than in flesh, likely for the biosynthesis of urushiols and related phenols. Genome resequencing reveals two distinct groups of mango varieties, with commercial varieties clustered with India germplasms and demonstrating allelic admixture, and indigenous varieties from Southeast Asia in the second group. Landraces indigenous in China formed distinct clades, and some showed admixture in genomes.
Conclusions: Analysis of chromosome-scale mango genome sequences reveals photosynthesis and lipid metabolism are preferentially retained after a recent WGD event, and expansion of CHS genes is likely associated with urushiol biosynthesis in mango. Genome resequencing clarifies two groups of mango varieties, discovers allelic admixture in commercial varieties, and shows distinct genetic background of landraces
Study on Surface Asperity Flattening in Cold Quasi-Static Uniaxial Planar Compression by Crystal Plasticity Finite Element Method
Recommended from our members
An open science resource for establishing reliability and reproducibility in functional connectomics
Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included