Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach

Background Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. Results Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. Conclusions The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data

UNSTEADY CHARACTERISTICS OF THE SHOCK PROPAGATION IN A CONVERGENT SHOCK TUBE WITH SMALL ANGLE

The whole evolution of the incident shock propagation in a convergent shock tube with small angle is studied in detail by using the direct numerical simulation. Specifically, the shape of the curved shock and the unsteady flow patterns which differs from the K-H instability, have been evaluated. The results show that as a disturbance of the inclined wall on the shock, the bending position of the incident shock represents periodically changed and its non-dimensional wavelength is larger when the convergent angle becomes greater, indicating a faster response to the curvature variation. At the same time, two different flow instable patterns for the shock propagation in the area reduction channel are discovered, one of which is the asymmetric shock bifurcations when the reflected shock from the collision of the right wall interacts with the boundary layer. This instability is closely related to the unsteady vortex shedding behind the bifurcated feet, resulting in the dramatic pressure fluctuation. Another pattern occurs when the reflected shocks generated by the curved incident shock impinge on the upper and lower walls. The collision position moves at a modest speed, which causes the formation of small vortices near the reflection regions

Granger causality vs. dynamic Bayesian network inference: a comparative study

Background In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results. Results In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better. Conclusion When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better

Scalable generation of large-scale unstructured meshes by a novel domain decomposition approach

© 2018 Elsevier Ltd A parallel algorithm is proposed for scalable generation of large-scale tetrahedral meshes. The key innovation is the use of a mesh-simplification based domain decomposition approach. This approach works on a background mesh with both its surface and its interior elements much larger than the final elements desired, and decomposes the domain into subdomains containing no undesirable geometric features in the inter-domain interfaces. In this way, the most time-consuming part of domain decomposition can be efficiently parallelized, and other sequential parts consume reasonably limited computing time since they treat a very coarse background mesh. Meanwhile, the subsequent parallel procedures of mesh generation and improvement are most efficient because they can treat individual subdomains without compromising element quality. Compared with published state-of-the-art parallel algorithms, the developed parallel algorithm can reduce the clock time required by the creation of one billion elements on 512 computer cores from roughly half an hour to less than 4 minutes

Zero-Shot Cross-Lingual Summarization via Large Language Models

Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of Large Language Models (LLMs), such as GPT-3.5, ChatGPT and GPT-4, has attracted wide attention from the computational linguistics community. However, it is not yet known the performance of LLMs on CLS. In this report, we empirically use various prompts to guide LLMs to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on the generated summaries. We find that ChatGPT and GPT-4 originally prefer to produce lengthy summaries with detailed information. These two LLMs can further balance informativeness and conciseness with the help of an interactive prompt, significantly improving their CLS performance. Experimental results on three widely-used CLS datasets show that GPT-4 achieves state-of-the-art zero-shot CLS performance, and performs competitively compared with the fine-tuned mBART-50. Moreover, we also find some multi-lingual and bilingual LLMs (i.e., BLOOMZ, ChatGLM-6B, Vicuna-13B and ChatYuan) have limited zero-shot CLS ability. Due to the composite nature of CLS, which requires models to perform summarization and translation simultaneously, accomplishing this task in a zero-shot manner is even a challenge for LLMs. Therefore, we sincerely hope and recommend future LLM research could use CLS as a testbed.Comment: Technical Report, 11 page

Differential expression of DKK-1 binding receptors on stromal cells and myeloma cells results in their distinct response to secreted DKK-1 in myeloma

<p>Abstract</p> <p>Background</p> <p>The canonical Wnt signaling is concurrently important for osteoblast differentiation and myeloma cell proliferation. Its activation in myeloma cells and its inhibition in osteoblasts and their progenitors have been identified in the previous studies. Osteoblast progenitors and myeloma cells from a myeloma patient share the same bone marrow (BM) microenvironment, but respond differently to DKK-1 secreted by myeloma cells. The mechanisms remain unclear.</p> <p>Methods</p> <p>Primary multiple myeloma (MM) cells were isolated from BM mononuclear cells of 12 MM patients. Human bone marrow stromal cells (SCs) were obtained from BM adherent cells of these MM patients and 10 healthy donors. The mRNA expression levels of DKK-1 binding receptor LRP5/6 and Kremen1/2 (Krm1/2) were analyzed by Real-time PCR in human myeloma cell line (HMCL) RPMI-8226, NCI-H929, U266, LP-1, CZ-1, KM-3, Sko-007, primary myeloma cells and SCs from 12 MM patients and SCs from 10 healthy donors. The binding capability of DKK-1 binding receptors to DKK-1 on primary myeloma cells and SCs was detected by flow cytometry assay.</p> <p>Results</p> <p>The mRNA expression levels of DKK-1 binding receptor LRP5/6 and Krm1/2 in SCs from patients with MM were significantly higher than those in myeloma cells and in SCs from healthy donors. The binding capability to DKK-1of DKK-1 binding receptors on SCs from MM patients was obviously higher than those on myeloma cells and SCs from healthy donors by flow cytometry assay. Similar to the effects of coculture with rhDKK1, coculture of SCs from healthy donors with myeloma cells in the presence or absence of a Transwell insert did up-regulate SCs' mRNA levels of LRP5/6 and Krm1/2, and down-regulate their mRNA levels of β-catenin.</p> <p>Conclusion</p> <p>Compared with myeloma cells, the SCs from MM patients overexpress DKK-1 binding receptors LRP5/6 and Krm1/2 in response to DKK-1 secreted by myeloma cells, which results in intracellular Wnt signaling inhibition. Our study provides a novel insight into mechanisms of myeloma associated osteolytic lesions.</p

Segment Everything Everywhere All at Once

Despite the growing demand for interactive AI systems, there have been few comprehensive studies on human-AI interaction in visual understanding e.g. segmentation. Inspired by the development of prompt-based universal interfaces for LLMs, this paper presents SEEM, a promptable, interactive model for Segmenting Everything Everywhere all at once in an image. SEEM has four desiderata: i) Versatility: by introducing a versatile prompting engine for different types of prompts, including points, boxes, scribbles, masks, texts, and referred regions of another image; ii) Compositionality: by learning a joint visual-semantic space for visual and textual prompts to compose queries on the fly for inference as shown in Fig 1; iii)Interactivity: by incorporating learnable memory prompts to retain dialog history information via mask-guided cross-attention; and iv) Semantic-awareness: by using a text encoder to encode text queries and mask labels for open-vocabulary segmentation

SGM3D: Stereo Guided Monocular 3D Object Detection

Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image. It poses a great challenge due to its ill-posed property which is critically lack of depth information in the 2D image plane. While there exist approaches leveraging off-the-shelve depth estimation or relying on LiDAR sensors to mitigate this problem, the dependence on the additional depth model or expensive equipment severely limits their scalability to generic 3D perception. In this paper, we propose a stereo-guided monocular 3D object detection framework, dubbed SGM3D, adapting the robust 3D features learned from stereo inputs to enhance the feature for monocular detection. We innovatively present a multi-granularity domain adaptation (MG-DA) mechanism to exploit the network's ability to generate stereo-mimicking features given only on monocular cues. Coarse BEV feature-level, as well as the fine anchor-level domain adaptation, are both leveraged for guidance in the monocular domain.In addition, we introduce an IoU matching-based alignment (IoU-MA) method for object-level domain adaptation between the stereo and monocular predictions to alleviate the mismatches while adopting the MG-DA. Extensive experiments demonstrate state-of-the-art results on KITTI and Lyft datasets.Comment: 8 pages, 5 figure

Quantum interference between non-identical single particles

Quantum interference between identical single particles reveals the intrinsic quantum statistic nature of particles, which could not be interpreted through classical physics. Here, we demonstrate quantum interference between non-identical bosons using a generalized beam splitter based on a quantum memory. The Hong-Ou-Mandel type interference between single photons and single magnons with high visibility is demonstrated, and the crossover from the bosonic to fermionic quantum statistics is observed by tuning the beam splitter to be non-Hermitian. Moreover, multi-particle interference that simulates the behavior of three fermions by three input photons is realized. Our work extends the understanding of the quantum interference effects and demonstrates a versatile experimental platform for studying and engineering quantum statistics of particles.Comment: 6 pages, 4 figure