The Effects of High-frequency Anticipatory Trading: Small Informed Trader vs. Front-runner

In this paper, the interactions between a large informed trader (IT, for short) and a high-frequency trader (HFT, for short) who can anticipate the former's incoming order are studied in an extended Kyle's model. Equilibria under various specific situations are discussed. We find that, in equilibrium, HFT always trades in the same direction as the large order in advance. However, whether or not she provides liquidity back depends on her inventory aversion and predictive ability, as well as the market activeness. She may supply liquidity to the market (act as a front-runner) or continue to take it away (in this case we call her a small IT). Small IT always harms the large trader while front-runner may benefit her. Besides, we have some surprising findings: (1) increasing the noise in HFT's signal may in fact decrease IT's profit; (2) although providing liquidity, a front-runner may harm IT more than a small IT. As for other market participants, the existence of HFT reduces the loss of normal-speed small uninformed traders and accelerates price discovery

Analysis of extracellular vesicles as emerging theranostic nanoplatforms

Extracellular vesicles (EVs) are nanoscale lipid membrane–bound vesicles that are secreted by cells of both prokaryotes and eukaryotes and carry bioactive cargos including proteins, nucleic acid and lipids from source cells. Given their prominent ability in transporting bioactive components, EVs are regarded as promising biomarkers for disease diagnosis and emerging therapeutic nanoparticles. However, to exert their effect in clinical applications, effective isolation and sensitive analysis of EVs from complex biofluids is required. Recent advances in EV-related research has provided feasible approaches for developing emerging therapeutic nanoplatforms using EVs. With this review, we aim to provide a comprehensive and in-depth summary of recent advances in diverse assay methods for EVs including fluorescence, Raman/Surface-enhanced Raman Spectroscopy (SERS) analysis and other methods, as well as their clinical potential in constructing EV-based theranostic nanoplatforms towards various diseases. In particular, microfluidic-assisted analysis sytems, single EV detection and the main approaches of utilizing EVs for therapeutic purposes are highlighted. We anticipate this review will be inspirational for researchers in related fields and will provide a general introduction to scientists with various research backgrounds.</p

Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF

【Abstract】Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models.10.13039/501100001809-National Natural Science Foundation of China; Collaborative Innovation Center of Haixi Government Affairs Big Data Sharin

GRACE: Loss-Resilient Real-Time Video through Neural Codecs

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines

Comprehensive Analysis of lncRNA and miRNA Regulatory Network Reveals Potential Prognostic Non-coding RNA Involved in Breast Cancer Progression

Breast cancer is one of the most common malignant tumors in women and is the second leading cause of cancer deaths among women. The tumorigenesis and progression of breast cancer are not well understood. The existing researches have indicated that non-coding RNAs, which mainly include long non-coding RNA (lncRNA) and microRNA (miRNA), have gradually become important regulators of breast cancer. We aimed to screen the differential expression of miRNA and lncRNA in the different breast cancer stages and identify the key non-coding RNA using TCGA data. Based on series test of cluster (STC) analysis, bioinformatics analysis, and negatively correlated relationships, 122 lncRNAs, 67 miRNAs, and 119 mRNAs were selected to construct the regulatory network of lncRNA and miRNA. It was shown that the miR-93/20b/106a/106b family was at the center of the regulatory network. Furthermore, 6 miRNAs, 10 lncRNAs, and 15 mRNAs were significantly associated with the overall survival (OS, log-rank P &lt; 0.05) of patients with breast cancer. Overexpressed miR-93 in MCF-7 breast cancer cells was associated with suppressed expression of multiple lncRNAs, and these downregulated lncRNAs (MESTIT1, LOC100128164, and DNMBP-AS1) were significantly associated with poor overall survival in breast cancer patients. Therefore, the miR-93/20b/106a/106b family at the core of the regulatory network discovered by our analysis above may be extremely important for the regulation of lncRNA expression and the progression of breast cancer. The identified key miRNA and lncRNA will enhance the understanding of molecular mechanisms of breast cancer progression. Targeting these key non-coding RNA may provide new therapeutic strategies for breast cancer treatment and may prevent the progression of breast cancer from an early stage to an advanced stage

Coordination between electron transfer and molecule diffusion through a bioinspired amorphous titania nanoshell for photocatalytic nicotinamide cofactor regeneration

In-depth understanding and rational manipulation of the electron transfer process and molecule diffusion process are critical to promote the overall photocatalytic efficiency. In our study, core@shell photocatalysts that embody graphitic carbon nitride (GCN) core and amorphous titania (a-TiO 2) nanoshell are prepared to elucidate and coordinate the electron transfer and molecule diffusion for the regeneration of nicotinamide adenine dinucleotide (NADH) with [Cp*Rh(bpy)H 2O] 2+ as the redox mediator. The GCN core absorbs visible light to generate electron-hole pairs, whereas the a-TiO 2 nanoshell facilitates the transfer of photogenerated electrons from GCN to the a-TiO 2 surface for NADH regeneration, which also enables the diffusion of electron donor molecules (triethanolamine, TEOA) from the a-TiO 2 surface to GCN for consuming the holes left on GCN. The transfer of photogenerated electrons and the diffusion of electron donor molecules are coordinated by finely tuning the thickness of the a-TiO 2 nanoshell. Under the optimized nanoshell thickness of â¼2.1 nm, the GCN@a-TiO 2 photocatalyst exhibits the highest NADH regeneration yield of 82.1% after a 10 min reaction under LED light (405 nm), over 200% higher than that of the GCN photocatalyst. Combined with the highly controllable and mild features of the bioinspired mineralization method, our study may offer a facile and generic strategy to design high performance photocatalysts through rational coordination of different substances/species transport processes

Observation of high-temperature superconductivity in the high-pressure tetragonal phase of La2PrNi2O7-{\delta}

The recent discovery of high-temperature superconductivity in the Ruddlesden-Popper phase La3Ni2O7 under high pressure marks a significant breakthrough in the field of 3d transition-metal oxide superconductors. For an emerging novel class of high-Tc superconductors, it is crucial to find more analogous superconducting materials with a dedicated effort toward broadening the scope of nickelate superconductors. Here, we report on the observation of high-Tc superconductivity in the high-pressure tetragonal I4/mmm phase of La2PrNi2O7 above ~10 GPa, which is distinct from the reported orthorhombic Fmmm phase of La3Ni2O7 above 14 GPa. For La2PrNi2O7, the onset and the zero-resistance temperatures of superconductivity reach Tconset = 78.2 K and Tczero = 40 K at 15 GPa. This superconducting phase shares the samilar structural symmetry as many cuprate superconductors, providing a fresh platform to investigate underlying mechanisms of nickelate superconductors.Comment: 19 pages and 6 figure