Online Deep Learning from Doubly-Streaming Data

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow learners, and expressiveness, which requires deep models. Motivated by this, we propose a novel OLD3S paradigm, where a shared latent subspace is discovered to summarize information from the old and new feature spaces, building an intermediate feature mapping relationship. A key trait of OLD3S is to treat the model capacity as a learnable semantics, aiming to yield optimal model depth and parameters jointly in accordance with the complexity and non-linearity of the input data streams in an online fashion. Both theoretical analysis and empirical studies substantiate the viability and effectiveness of our proposed approach. The code is available online at https://github.com/X1aoLian/OLD3S

A Feasible Semi-quantum Private Comparison Based on Entanglement Swapping of Bell States

Semi-quantum private comparison (SQPC) enables two classical users with limited quantum capabilities to compare confidential information using a semi-honest third party (TP) with full quantum power. However, entanglement swapping, as an important property of quantum mechanics in previously proposed SQPC protocols is usually neglected. In this paper, we propose a feasible SQPC protocol based on the entanglement swapping of Bell states, where two classical users do not require additional implementation of the semi-quantum key distribution protocol to ensure the security of their private data. Security analysis shows that our protocol is resilient to both external and internal attacks. To verify the feasibility and correctness of the proposed SQPC protocol, we design and simulate the corresponding quantum circuits using IBM Qiskit. Finally, we compare and discuss the proposed protocol with previous similar work. The results reveal that our protocol maintains high qubit efficiency, even when entanglement swapping is employed. Consequently, our proposed approach showcases the potential applications of entanglement swapping in the field of semi-quantum cryptography.Comment: 17 pages, 6 figures. arXiv admin note: text overlap with arXiv:2210.0342

A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz

Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) method. In this method, a locally parameterized quantum circuit is created to reduce the parameters required in the optimization. Furthermore, we design a quantum semi-supervised binary classifier based on hybrid Bell and $Z$ bases measurement, which has shallower circuit depth and is more suitable for implementation on near-term quantum devices. We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set, and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier. This work opens a new path to quantum machine learning based on graphs

Storage Fit Learning with Feature Evolvable Streams

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF$^2$EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior to different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step

Semi-quantum private comparison and its generalization to the key agreement, summation, and anonymous ranking

Semi-quantum protocols construct connections between quantum users and ``classical'' users who can only perform certain ``classical'' operations. In this paper, we present a new semi-quantum private comparison protocol based on entangled states and single particles, which does not require pre-shared keys between the ``classical'' users to guarantee the security of their private data. By utilizing multi-particle entangled states and single particles, our protocol can be easily extended to multi-party scenarios to meet the requirements of multiple ``classical'' users who want to compare their private data. The security analysis shows that the protocol can effectively prevent attacks from outside eavesdroppers and adversarial participants. Besides, we generalize the proposed protocol to other semi-quantum protocols such as semi-quantum key agreement, semi-quantum summation, and semi-quantum anonymous ranking protocols. We compare and discuss the proposed protocols with previous similar protocols. The results show that our protocols satisfy the demands of their respective counterparts separately. Therefore, our protocols have a wide range of application scenarios.Comment: 19 pages 5 table

Validation of internal control for gene expression study in soybean by quantitative real-time PCR

<p>Abstract</p> <p>Background</p> <p>Normalizing to housekeeping gene (HKG) can make results from quantitative real-time PCR (qRT-PCR) more reliable. Recent studies have shown that no single HKG is universal for all experiments. Thus, a suitable HKG should be selected before its use. Only a few studies on HKGs have been done in plants, and none in soybean, an economically important crop. Therefore, the present study was conducted to identify suitable HKG(s) for normalization of gene expression in soybean.</p> <p>Results</p> <p>All ten HKGs displayed a wide range of Ct values in 21 sample pools, confirming that they were variably expressed. GeNorm was used to determine the expression stability of the HGKs in seven series sets. For all the sample pools analyzed, the stability rank was <it>ELF1B</it>, <it>CYP2 </it>> <it>ACT11 </it>> <it>TUA </it>> <it>ELF1A </it>> <it>UBC2 </it>> <it>ACT2/7 </it>> <it>TUB </it>> <it>G6PD </it>> <it>UBQ10</it>. For different tissues under the same developmental stage, the rank was <it>ELF1B</it>, <it>CYP2 </it>> <it>ACT2/7 </it>> <it>UBC2 </it>> <it>TUA </it>> <it>ELF1A </it>> <it>ACT11 </it>> <it>TUB </it>> <it>G6PD </it>> <it>UBQ10</it>. For the developmental stage series, the stability rank was <it>ACT2/7</it>, <it>TUA </it>> <it>ELF1A </it>> <it>UBC2 </it>> <it>ELF1B </it>> <it>TUB </it>> <it>CYP2 </it>> <it>ACT11 </it>> <it>G6PD </it>> <it>UBQ10</it>. For photoperiodic treatments, the rank was <it>ACT11</it>, <it>ELF1B </it>> <it>CYP2 </it>> <it>TUA </it>> <it>ELF1A </it>> <it>UBC2 </it>> <it>ACT2/7 </it>> <it>TUB </it>> <it>G6PD </it>> <it>UBQ10</it>. For different times of the day, the rank was <it>ELF1A</it>, <it>TUA </it>> <it>ELF1B </it>> <it>G6PD </it>> <it>CYP2 </it>> <it>ACT11 </it>> <it>ACT2/7 </it>> <it>TUB </it>> <it>UBC2 </it>> <it>UBQ10</it>. For different cultivars and leaves on different nodes of the main stem, the ten HKGs' stability did not differ significantly. ΔCt approach and 'Stability index' were also used to analyze the expression stability in all 21 sample pools. Results from ΔCt approach and geNorm indicated that <it>ELF1B </it>and <it>CYP2 </it>were the most stable HKGs, and <it>UBQ10 </it>and <it>G6PD </it>the most variable ones. Results from 'Stability index' analysis were different, with <it>ACT11 </it>and <it>CYP2 </it>being the most stable HKGs, and <it>ELF1A </it>and <it>TUA </it>the most variable ones.</p> <p>Conclusion</p> <p>Our data suggests that HKGs are expressed variably in soybean. Based on the results from geNorm and ΔCt analysis, <it>ELF1B </it>and <it>CYP2 </it>could be used as internal controls to normalize gene expression in soybean, while <it>UBQ10 </it>and <it>G6PD </it>should be avoided. To achieve accurate results, some conditions may require more than one HKG to be used for normalization.</p

Geometric bionics: Lotus effect helps polystyrene nanotube films get good blood compatibility

Various biomaterials have been widely used for manufacturing biomedical applications including artificial organs, medical devices and disposable clinical apparatus, such as vascular prostheses, blood pumps, artificial kidney, artificial hearts, dialyzers and plasma separators, which could be used in contact with blood^1^. However, the research tasks of improving hemocompatibility of biomaterials have been carrying out with the development of biomedical requirements^2^. Since the interactions that lead to surface-induced thrombosis occurring at the blood-biomaterial interface become a reason of familiar current complications with grafts therapy, improvement of the blood compatibility of artificial polymer surfaces is, therefore a major issue in biomaterials science^3^. After decades of focused research, various approaches of modifying biomaterial surfaces through chemical or biochemical methods to improve their hemocompatibility were obtained^1^. In this article, we report that polystyrene nanotube films with morphology similar to the papilla on lotus leaf can be used as blood-contacted biomaterials by virtue of Lotus effect^4^. Clearly, this idea, resulting from geometric bionics that mimicking the structure design of lotus leaf, is very novel technique for preparation of hemocompatible biomaterials