46 research outputs found
Enhancement of Entanglement via Incoherent Collisions
In contrast to the general thought that the collisions are intrinsically
dephasing in nature and detrimental to quantum entanglement at room or higher
temperatures, here, we show that in the conventional ladder-type three-level
electromagnetically induced transparency (EIT) configuration, when the probe
field intensity is not very weak as compared to the pump field, the
entanglement between the bright pump and probe fields can be remarkably
enhanced with the increase of the collisional decay rates in a moderate range
in an inhomogeneously-broadened atomic system. The strengthened entanglement
results from the enhancement of constructive interference and suppression of
destructive interference between one-photon and multi-photon transition
pathways. Our results clearly indicate that the collisions offer a promising
alternative to enhance entanglement at room or higher temperatures despite of
the dephasing nature, which provides great convenience for experimental
implementation, and opens new prospects and applications in realistic quantum
computation and quantum information processing.Comment: 15 pages, 4 figure
BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems
Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the
reliability and safety of autonomous vehicles. Existing methods mainly focus on
searching for safety violations while the diversity of the generated test cases
is ignored, which may generate many redundant test cases and failures. Such
redundant failures can reduce testing performance and increase failure analysis
costs. In this paper, we present a novel behavior-guided fuzzing technique
(BehAVExplor) to explore the different behaviors of the ego vehicle (i.e., the
vehicle controlled by the ADS under test) and detect diverse violations.
Specifically, we design an efficient unsupervised model, called BehaviorMiner,
to characterize the behavior of the ego vehicle. BehaviorMiner extracts the
temporal features from the given scenarios and performs a clustering-based
abstraction to group behaviors with similar features into abstract states. A
new test case will be added to the seed corpus if it triggers new behaviors
(e.g., cover new abstract states). Due to the potential conflict between the
behavior diversity and the general violation feedback, we further propose an
energy mechanism to guide the seed selection and the mutation. The energy of a
seed quantifies how good it is. We evaluated BehAVExplor on Apollo, an
industrial-level ADS, and LGSVL simulation environment. Empirical evaluation
results show that BehAVExplor can effectively find more diverse violations than
the state-of-the-art
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
The truth is significantly hampered by massive rumors that spread along with
breaking news or popular topics. Since there is sufficient corpus gathered from
the same domain for model training, existing rumor detection algorithms show
promising performance on yesterday's news. However, due to a lack of training
data and prior expert knowledge, they are poor at spotting rumors concerning
unforeseen events, especially those propagated in different languages (i.e.,
low-resource regimes). In this paper, we propose a unified contrastive transfer
framework to detect rumors by adapting the features learned from well-resourced
rumor data to that of the low-resourced. More specifically, we first represent
rumor circulated on social media as an undirected topology, and then train a
Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our
model explicitly breaks the barriers of the domain and/or language issues, via
language alignment and a novel domain-adaptive contrastive learning mechanism.
To enhance the representation learning from a small set of target events, we
reveal that rumor-indicative signal is closely correlated with the uniformity
of the distribution of these events. We design a target-wise contrastive
training mechanism with three data augmentation strategies, capable of unifying
the representations by distinguishing target events. Extensive experiments
conducted on four low-resource datasets collected from real-world microblog
platforms demonstrate that our framework achieves much better performance than
state-of-the-art methods and exhibits a superior capacity for detecting rumors
at early stages.Comment: A significant extension of the first contrastive approach for
low-resource rumor detection (arXiv:2204.08143
ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs
Automated Program Repair (APR) aims to automatically generate patches for
rectifying software bugs. Recent strides in Large Language Models (LLM), such
as ChatGPT, have yielded encouraging outcomes in APR, especially within the
conversation-driven APR framework. Nevertheless, the efficacy of
conversation-driven APR is contingent on the quality of the feedback
information. In this paper, we propose ContrastRepair, a novel
conversation-based APR approach that augments conversation-driven APR by
providing LLMs with contrastive test pairs. A test pair consists of a failing
test and a passing test, which offer contrastive feedback to the LLM. Our key
insight is to minimize the difference between the generated passing test and
the given failing test, which can better isolate the root causes of bugs. By
providing informative and specific feedback, ContrastRepair enables the LLM to
produce effective bug fixes. The implementation of ContrastRepair is based on
the state-of-the-art LLM, ChatGPT, and it iteratively interacts with ChatGPT
until plausible patches are generated. We evaluate ContrastRepair on multiple
benchmark datasets, including Defects4j, QuixBugs, and HumanEval-Java. The
results demonstrate that ContrastRepair significantly outperforms existing
methods, achieving a new state-of-the-art in program repair. For instance,
among Defects4j 1.2 and 2.0, ContrastRepair correctly repairs 143 out of all
337 bug cases, while the best-performing baseline fixes 124 bugs
A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation
Few-shot segmentation (FSS) aims to segment objects of unseen classes given
only a few annotated support images. Most existing methods simply stitch query
features with independent support prototypes and segment the query image by
feeding the mixed features to a decoder. Although significant improvements have
been achieved, existing methods are still face class biases due to class
variants and background confusion. In this paper, we propose a joint framework
that combines more valuable class-aware and class-agnostic alignment guidance
to facilitate the segmentation. Specifically, we design a hybrid alignment
module which establishes multi-scale query-support correspondences to mine the
most relevant class-aware information for each query image from the
corresponding support features. In addition, we explore utilizing base-classes
knowledge to generate class-agnostic prior mask which makes a distinction
between real background and foreground by highlighting all object regions,
especially those of unseen classes. By jointly aggregating class-aware and
class-agnostic alignment guidance, better segmentation performances are
obtained on query images. Extensive experiments on PASCAL- and COCO-
datasets demonstrate that our proposed joint framework performs better,
especially on the 1-shot setting
Virus-induced gene complementation reveals a transcription factor network in modulation of tomato fruit ripening
Plant virus technology, in particular virus-induced gene silencing, is a widely used reverse- and forward-genetics tool in plant functional genomics. However the potential of virus technology to express genes to induce phenotypes or to complement mutants in order to understand the function of plant genes is not well documented. Here we exploit Potato virus X as a tool for virus-induced gene complementation (VIGC). Using VIGC in tomato, we demonstrated that ectopic viral expression of LeMADS-RIN, which encodes a MADS-box transcription factor (TF), resulted in functional complementation of the non-ripening rin mutant phenotype and caused fruits to ripen. Comparative gene expression analysis indicated that LeMADS-RIN up-regulated expression of the SBP-box (SQUAMOSA promoter binding protein-like) gene LeSPL-CNR, but down-regulated the expression of LeHB-1, an HD-Zip homeobox TF gene. Our data support the hypothesis that a transcriptional network may exist among key TFs in the modulation of fruit ripening in tomato
Aneuploid Embryonic Stem Cells Drive Teratoma Metastasis
Aneuploidy, a deviation of the chromosome number from euploidy, is one of the hallmarks of cancer. High levels of aneuploidy are generally correlated with metastasis and poor prognosis in cancer patients. However, the causality of aneuploidy in cancer metastasis remains to be explored. Here we demonstrate that teratomas derived from aneuploid murine embryonic stem cells (ESCs), but not from isogenic diploid ESCs, disseminated to multiple organs, for which no additional copy number variations were required. Notably, no cancer driver gene mutations were identified in any metastases. Aneuploid circulating teratoma cells were successfully isolated from peripheral blood and showed high capacities for migration and organ colonization. Single-cell RNA sequencing of aneuploid primary teratomas and metastases identified a unique cell population with high stemness that was absent in diploid ESCs-derived teratomas. Further investigation revealed that aneuploid cells displayed decreased proteasome activity and overactivated endoplasmic reticulum (ER) stress during differentiation, thereby restricting the degradation of proteins produced from extra chromosomes in the ESC state and causing differentiation deficiencies. Noticeably, both proteasome activator Oleuropein and ER stress inhibitor 4-PBA can effectively inhibit aneuploid teratoma metastasis