Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Randomness-enhanced expressivity of quantum neural networks

As a hybrid of artificial intelligence and quantum computing, quantum neural networks (QNNs) have gained significant attention as a promising application on near-term, noisy intermediate-scale quantum (NISQ) devices. Conventional QNNs are described by parametrized quantum circuits, which perform unitary operations and measurements on quantum states. In this work, we propose a novel approach to enhance the expressivity of QNNs by incorporating randomness into quantum circuits. Specifically, we introduce a random layer, which contains single-qubit gates sampled from an trainable ensemble pooling. The prediction of QNN is then represented by an ensemble average over a classical function of measurement outcomes. We prove that our approach can accurately approximate arbitrary target operators using Uhlmann's theorem for majorization, which enables observable learning. Our proposal is demonstrated with extensive numerical experiments, including observable learning, R\'enyi entropy measurement, and image recognition. We find the expressivity of QNNs is enhanced by introducing randomness for multiple learning tasks, which could have broad application in quantum machine learning.Comment: 6 pages, 4 figure

Source-Frequency Phase-Referencing Observation of AGNs with KaVA Using Simultaneous Dual-Frequency Receiving

The KVN(Korean VLBI Network)-style simultaneous multi-frequency receiving mode is demonstrated to be promising for mm-VLBI observations. Recently, other Very long baseline interferometry (VLBI) facilities all over the globe start to implement compatible optics systems. Simultaneous dual/multi-frequency VLBI observations at mm wavelengths with international baselines are thus possible. In this paper, we present the results from the first successful simultaneous 22/43 GHz dual-frequency observation with KaVA(KVN and VERA array), including images and astrometric results. Our analysis shows that the newly implemented simultaneous receiving system has brought a significant extension of the coherence time of the 43 GHz visibility phases along the international baselines. The astrometric results obtained with KaVA are consistent with those obtained with the independent analysis of the KVN data. Our results thus confirm the good performance of the simultaneous receiving systems for the non-KVN stations. Future simultaneous observations with more global stations bring even higher sensitivity and micro-arcsecond level astrometric measurements of the targets.Comment: 8 pages, 6 figures, Published in JKA

Dark Matter Results From 54-Ton-Day Exposure of PandaX-II Experiment

We report a new search of weakly interacting massive particles (WIMPs) using the combined low background data sets in 2016 and 2017 from the PandaX-II experiment in China. The latest data set contains a new exposure of 77.1 live day, with the background reduced to a level of 0.8$\times10^{-3}$ evt/kg/day, improved by a factor of 2.5 in comparison to the previous run in 2016. No excess events were found above the expected background. With a total exposure of 5.4$\times10^4$ kg day, the most stringent upper limit on spin-independent WIMP-nucleon cross section was set for a WIMP with mass larger than 100 GeV/c$^2$, with the lowest exclusion at 8.6$\times10^{-47}$ cm$^2$ at 40 GeV/c$^2$.Comment: Supplementary materials at https://pandax.sjtu.edu.cn/articles/2nd/supplemental.pdf version 2 as accepted by PR

Toward Microarcsecond Astrometry for the Innermost Wobbling Jet of the BL Lacertae Object OJ 287

The BL Lacertae object OJ 287 is a very unusual quasar producing a wobbling radio jet and some double-peaked optical outbursts with a possible period of about 12 yr for more than one century. This variability is widely explained by models of binary supermassive black hole (SMBH) or precessing jet/disk from a single SMBH. To enable an independent and nearly bias-free investigation on these possible scenarios, we explored the feasibility of extremely high-precision differential astrometry on its innermost restless jet at mm-wavelengths. Through re-visiting some existing radio surveys and very long baseline interferometry (VLBI) data at frequencies from 1.4 to 15.4 GHz and performing new Very Long Baseline Array (VLBA) observations at 43.2 GHz, we find that the radio source J0854$+$1959, 7.1 arcmin apart from OJ 287 and no clearly-seen optical and infrared counterparts, could provide a nearly ideal reference point to track the complicated jet activity of OJ 287. The source J0854$+$1959 has a stable GHz-peaked radio spectrum and shows a jet structure consisting of two discrete, mas-scale-compact and steep-spectrum components and showing no proper motion over about 8 yr. The stable VLBI structure can be interpreted by an episodic, optically thin and one-sided jet. With respect to its 4.1-mJy peak feature at 43.2 GHz, we have achieved an astrometric precision at the state-of-art level, about 10 $\mu$as. These results indicate that future VLBI astrometry on OJ 287 could allow us to accurately locate its jet apex and activity boundary, align its restless jet structure over decades without significant systematic bias, and probe various astrophysical scenarios.Comment: 10 pages, 3 figures, 2 tables, accepted for publication in Astrophysical Journal Letter

Background subtraction using spatio-temporal group sparsity recovery

AbstractBackground subtraction is a key step in a wide spectrum of video applications, such as object tracking and human behavior analysis. Compressive sensing-based methods, which make little specific assumptions about the background, have recently attracted wide attention in background subtraction. Within the framework of compressive sensing, background subtraction is solved as a decomposition and optimization problem, where the foreground is typically modeled as pixel-wised sparse outliers. However, in real videos, foreground pixels are often not randomly distributed, but instead, group clustered. Moreover, due to costly computational expenses, most compressive sensing-based methods are unable to process frames online. In this paper, we take into account the group properties of foreground signals in both spatial and temporal domains, and propose a greedy pursuit-based method called spatio-temporal group sparsity recovery, which prunes data residues in an iterative process, according to both sparsity and group clustering priors, rather than merely sparsity. Furthermore, a random strategy for background dictionary learning is used to handle complex background variations, while foreground-free training is not required. Finally, we propose a two-pass framework to achieve online processing. The proposed method is validated on multiple challenging video sequences. Experiments demonstrate that our approach effectively works on a wide range of complex scenarios and achieves a state-of-the-art performance with far fewer computations.Abstract Background subtraction is a key step in a wide spectrum of video applications, such as object tracking and human behavior analysis. Compressive sensing-based methods, which make little specific assumptions about the background, have recently attracted wide attention in background subtraction. Within the framework of compressive sensing, background subtraction is solved as a decomposition and optimization problem, where the foreground is typically modeled as pixel-wised sparse outliers. However, in real videos, foreground pixels are often not randomly distributed, but instead, group clustered. Moreover, due to costly computational expenses, most compressive sensing-based methods are unable to process frames online. In this paper, we take into account the group properties of foreground signals in both spatial and temporal domains, and propose a greedy pursuit-based method called spatio-temporal group sparsity recovery, which prunes data residues in an iterative process, according to both sparsity and group clustering priors, rather than merely sparsity. Furthermore, a random strategy for background dictionary learning is used to handle complex background variations, while foreground-free training is not required. Finally, we propose a two-pass framework to achieve online processing. The proposed method is validated on multiple challenging video sequences. Experiments demonstrate that our approach effectively works on a wide range of complex scenarios and achieves a state-of-the-art performance with far fewer computations

Fairness-aware Age-of-Information Minimization in WPT-Assisted Short-Packet THz Communications for mURLLC

The technological landscape is swiftly advancing towards large-scale systems, creating significant opportunities, particularly in the domain of Terahertz (THz) communications. Networks designed for massive connectivity, comprising numerous Internet of Things (IoT) devices, are at the forefront of this advancement. In this paper, we consider Wireless Power Transfer (WPT)-enabled networks that support these IoT devices with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services.The focus of such networks is information freshness, with the Age-of-Information (AoI) serving as the pivotal performance metric. In particular, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings establish the convexity property of the problem, which can be solved efficiently. Furthermore, we introduce the concept of AoI-oriented cluster capacity, examining the relationship between the number of supported devices and the AoI performance in the network. Numerical simulations validate the advantage of our proposed approach in enhancing AoI performance, indicating its potential to guide the design of future THz communication systems for IoT applications requiring mURLLC services

Green-light p-n Junction Particle Inhomogeneous Phase Enhancement of MgB2 Smart Meta-Superconductor

Improving the critical temperature (TC), critical magnetic field (HC), and critical current (JC) of superconducting materials has always been one of the most significant challenges in the field of superconductivity, but progress has been slow over the years. Based on the concept of injecting energy to enhance electron pairing states, in this study, we have employed a solid-state sintering method to fabricate a series of smart meta-superconductors (SMSCs) consisting of p-n junction nanostructures with a wavelength of 550 nm, doped within an MgB2 matrix. Experimental results demonstrate that compared to pure MgB2 samples, the critical transition temperature (TC) has increased by 1.2 K, the critical current (JC) has increased by 52.8%, and the Meissner effect (HC) shows significant improvement in its diamagnetic properties. This phenomenon of enhanced superconducting performance can be explained by the coupling between superconducting electrons and evanescent waves

Run-to-Run Control for Active Balancing of Lithium Iron Phosphate Battery Packs

\ua9 1986-2012 IEEE. Lithium iron phosphate battery packs are widely employed for energy storage in electrified vehicles and power grids. However, their flat voltage curves rendering the weakly observable state of charge are a critical stumbling block for charge equalization management. This paper focuses on the real-time active balancing of series-connected lithium iron phosphate batteries. In the absence of accurate in situ state information in the voltage plateau, a balancing current ratio (BCR) based algorithm is proposed for battery balancing. Then, BCR-based and voltage-based algorithms are fused, responsible for the balancing task within and beyond the voltage plateau, respectively. The balancing process is formulated as a batch-based run-to-run control problem, as the first time in the research area of battery management. The control algorithm acts in two timescales, including timewise control within each batch run and batchwise control at the end of each batch. Hardware-in-the-loop experiments demonstrate that the proposed balancing algorithm is able to release 97.1% of the theoretical capacity and can improve the capacity utilization by 5.7% from its benchmarking algorithm. Furthermore, the proposed algorithm can be coded in C language with the binary code in 118 328 bytes only and, thus, is readily implementable in real time