Engineering asymmetric steady-state Einstein-Podolsky-Rosen steering in macroscopic hybrid systems

Generation of quantum correlations between separate objects is of significance both in fundamental physics and in quantum networks. One important challenge is to create the directional "spooky action-at-a-distanc" effects that Schr\"{o}dinger called "steering" between two macroscopic and massive objects. Here, we analyze a generic scheme for generating steering correlations in cascaded hybrid systems in which two distant oscillators with effective masses of opposite signs are coupled to a unidirectional light field, a setup which is known to build up quantum correlations by means of quantum back-action evasion. The unidirectional coupling of the first to the second oscillator via the light field can be engineered to enhance steering in both directions and provides an active method for controlling the asymmetry of steering. We show that the resulting scheme can efficiently generate unconditional steady-state Einstein-Podolsky-Rosen steering between the two subsystems, even in the presence of thermal noise and optical losses. As a scenario of particular technological interest in quantum networks, we use our scheme to engineer enhanced steering from an untrusted node with limited tunability (in terms of interaction strength and type with the light field) to a trusted, highly tunable node, hence offering a path to implementing one-sided device-independent quantum tasks.Comment: 11 pages, 8 figure

Unconditional steady-state entanglement in macroscopic hybrid systems by coherent noise cancellation

The generation of entanglement between disparate physical objects is a key ingredient in the field of quantum technologies, since they can have different functionalities in a quantum network. Here we propose and analyze a generic approach to steady-state entanglement generation between two oscillators with different temperatures and decoherence properties coupled in cascade to a common unidirectional light field. The scheme is based on a combination of coherent noise cancellation and dynamical cooling techniques for two oscillators with effective masses of opposite signs, such as quasi-spin and motional degrees of freedom, respectively. The interference effect provided by the cascaded setup can be tuned to implement additional noise cancellation leading to improved entanglement even in the presence of a hot thermal environment. The unconditional entanglement generation is advantageous since it provides a ready-to-use quantum resource. Remarkably, by comparing to the conditional entanglement achievable in the dynamically stable regime, we find our unconditional scheme to deliver a virtually identical performance when operated optimally.Comment: Final version; 6 pages, 3 figures + Supplemental Materia

Non-Hermitian skin effect and nonreciprocity induced by dissipative couplings

We study the mechanism for realizing non-Hermitian skin effect (NHSE) via dissipative couplings, in which the left-right couplings have equal strengths but the phases do not satisfy the complex conjugation. Previous realizations of NHSE typically require unequal left-right couplings or on-site gain and loss. In this work we find that when combined with the multichannel interference provided by a periodic dissipative-coherent coupling structure, the dissipative couplings can lead to unequal left-right couplings, inducing NHSE. Moreover, we show that the non-Hermiticity induced by dissipative couplings can be fully transformed into nonreciprocity-type non-Hermiticity without bringing extra gain-loss-type non-Hermiticity. Thus, this mechanism enables unidirectional energy transmission without introducing additional insertion loss. Our work opens a new avenue for the study of non-Hermitian topological effects and the design of directional optical networks

Genetic variation and associations involving Fusarium head blight and deoxynivalenol accumulation in cultivated oat (Avena sativa L.)

acceptedVersio

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations

Parallel In Vivo and In Vitro Melanoma RNAi Dropout Screens Reveal Synthetic Lethality between Hypoxia and DNA Damage Response Inhibition

SummaryTo identify factors preferentially necessary for driving tumor expansion, we performed parallel in vitro and in vivo negative-selection short hairpin RNA (shRNA) screens. Melanoma cells harboring shRNAs targeting several DNA damage response (DDR) kinases had a greater selective disadvantage in vivo than in vitro, indicating an essential contribution of these factors during tumor expansion. In growing tumors, DDR kinases were activated following hypoxia. Correspondingly, depletion or pharmacologic inhibition of DDR kinases was toxic to melanoma cells, including those that were resistant to BRAF inhibitor, and this could be enhanced by angiogenesis blockade. These results reveal that hypoxia sensitizes melanomas to targeted inhibition of the DDR and illustrate the utility of in vivo shRNA dropout screens for the identification of pharmacologically tractable targets

MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results

Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.Comment: This paper is included in the proceedings of the 18th International Conference on Machine Vision Applications (MVA2023). It will be officially published at a later date. Project page : https://www.mva-org.jp/mva2023/challeng

Yi: Open Foundation Models by 01.AI

We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance on a wide range of benchmarks like MMLU, and our finetuned chat models deliver strong human preference rate on major evaluation platforms like AlpacaEval and Chatbot Arena. Building upon our scalable super-computing infrastructure and the classical transformer architecture, we attribute the performance of Yi models primarily to its data quality resulting from our data-engineering efforts. For pretraining, we construct 3.1 trillion tokens of English and Chinese corpora using a cascaded data deduplication and quality filtering pipeline. For finetuning, we polish a small scale (less than 10K) instruction dataset over multiple iterations such that every single instance has been verified directly by our machine learning engineers. For vision-language, we combine the chat language model with a vision transformer encoder and train the model to align visual representations to the semantic space of the language model. We further extend the context length to 200K through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. We show that extending the depth of the pretrained checkpoint through continual pretraining further improves performance. We believe that given our current results, continuing to scale up model parameters using thoroughly optimized data will lead to even stronger frontier models

An adverse tumor-protective effect of IDO1 inhibition

By restoring tryptophan, indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors aim to reactivate anti-tumor T cells. However, a phase III trial assessing their clinical benefit failed, prompting us to revisit the role of IDO1 in tumor cells under T cell attack. We show here that IDO1 inhibition leads to an adverse protection of melanoma cells to T cell-derived interferon-gamma (IFNγ). RNA sequencing and ribosome profiling shows that IFNγ shuts down general protein translation, which is reversed by IDO1 inhibition. Impaired translation is accompanied by an amino acid deprivation-dependent stress response driving activating transcription factor-4 (ATF4)high/microphtalmia-associated transcription factor (MITF)low transcriptomic signatures, also in patient melanomas. Single-cell sequencing analysis reveals that MITF downregulation upon immune checkpoint blockade treatment predicts improved patient outcome. Conversely, MITF restoration in cultured melanoma cells causes T cell resistance. These results highlight the critical role of tryptophan and MITF in the melanoma response to T cell-derived IFNγ and uncover an unexpected negative consequence of IDO1 inhibition