Dynamical Axion Field in Topological Magnetic Insulators

Axions are very light, very weakly interacting particles postulated more than 30 years ago in the context of the Standard Model of particle physics. Their existence could explain the missing dark matter of the universe. However, despite intensive searches, they have yet to be detected. In this work, we show that magnetic fluctuations of topological insulators couple to the electromagnetic fields exactly like the axions, and propose several experiments to detect this dynamical axion field. In particular, we show that the axion coupling enables a nonlinear modulation of the electromagnetic field, leading to attenuated total reflection. We propose a novel optical modulators device based on this principle.Comment: 5 pages, 3 figure

Design and Optimise Synchronous Reluctance Machines in Sensorless Control

This thesis researches the design and fabrication of axially laminated, anisotropic synchronous reluctance rotors with a high saliency ratio and low torque ripple for both three- and five-phase machines. One clear novelty of the research is the first method reported that allows skew to be incorporated in the axially laminated anisotropic (ALA) rotor. The designed rotor is then built with the help of the 3D printing technique which significantly reduces the complexity of the prototyping and fabrication process. The thesis then considers the control necessary including sensorless control schemes for the three- and five-phase synchronous reluctance motors with varying levels of skew. The performance and the effectiveness of the sensorless controllers are verified by experiments for all designed rotors under the three- and five-phase excitation. The three- and five-phase system of the synchronous reluctance motor is first discussed with the stator voltage equations and equivalent circuits. The d-and q-axis inductances are evaluated using finite element analysis. Finite element analysis (FEA) is a common used method in simulating and solving the electrical engineering problems. The FEA shows that for both three- and five-phase motors, the saliency ratio can reach around 10. Further detailed optimization is performed based on the rotor barrier dimensions such as shape, arc length, rib and bridge length, width, and the number of barriers. The final designed rotor in this research is an ALA rotor with 4 poles and 9 layers of magnetic segments. The barrier used is the round-type. The experimental inductances are shown to match the FEA predictions. The method of fabricating an ALA-type rotor with skew is a significant advance in this research. The FEA analysis for both three- and five-phase motor shows that torque ripple can be significantly reduced with the skew process: for instance, the 5.5° skewed rotor and the 9.5° skewed rotor are predicted to offer the best choice for the three- and five-phase stator designs from a parametric study of skew angles. Three skewed rotors are fabricated (5.5 o, 6 o and 9.5o). The experimental results of the torque ripple are compared. For the three-phase case, the rotors with skew shows a good reduction in torque ripple, the 6° skewed rotor performs better than the 5.5° skewed rotor. For the five-phase case, the 9.5° skewed rotor provides the best torque ripple reduction experimentally. The sensorless control is achieved for both three- and five-phase systems. According to the speed demand, the high-frequency injection sensorless control is used when the speed is below 500rpm. To further reduce the transient error, two different sliding mode observer methods are used for three and five-phase systems when the speed demand is above 500rpm. For both three- and five-phase synchronous reluctance motors with non-skewed and skewed rotors, the sensorless control can be successfully implemented. The transient and steady-state errors are all controlled in an acceptable range. By suddenly adding full load at rated and zero speed, the effectiveness of the sensorless control is also verified

Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection

Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually more time-consuming and costly than unlabeled data. When ID labeled data is limited, the previous OOD detection methods are no longer superior due to their high dependence on the amount of ID labeled data. Based on limited ID labeled data and sufficient unlabeled data, we define a new setting called Weakly-Supervised Out-of-Distribution Detection (WSOOD). To solve the new problem, we propose an effective method called Topological Structure Learning (TSL). Firstly, TSL uses a contrastive learning method to build the initial topological structure space for ID and OOD data. Secondly, TSL mines effective topological connections in the initial topological space. Finally, based on limited ID labeled data and mined topological connections, TSL reconstructs the topological structure in a new topological space to increase the separability of ID and OOD instances. Extensive studies on several representative datasets show that TSL remarkably outperforms the state-of-the-art, verifying the validity and robustness of our method in the new setting of WSOOD

QSketch: An Efficient Sketch for Weighted Cardinality Estimation in Streams

Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element carries a positive weight. Unlike traditional cardinality estimation, limited research exists on weighted cardinality, with current methods requiring substantial memory and computational resources, challenging for devices with limited capabilities and real-time applications like anomaly detection. To address these issues, we propose QSketch, a memory-efficient sketch method for estimating weighted cardinality in streams. QSketch uses a quantization technique to condense continuous variables into a compact set of integer variables, with each variable requiring only 8 bits, making it 8 times smaller than previous methods. Furthermore, we leverage dynamic properties during QSketch generation to significantly enhance estimation accuracy and achieve a lower time complexity of $O(1)$ for updating estimations upon encountering a new element. Experimental results on synthetic and real-world datasets show that QSketch is approximately 30\% more accurate and two orders of magnitude faster than the state-of-the-art, using only $1/8$ of the memory.Comment: 12 pages, 10 figures, accepted by KDD 202

Role of Protein Charge Density on Hepatitis B Virus Capsid Formation

The role of electrostatic interactions in the viral capsid assembly process was studied by comparing the assembly process of a truncated hepatitis B virus capsid protein Cp149 with its mutant protein D2N/D4N, which has the same conformational structure but four fewer charges per dimer. The capsid protein self-assembly was investigated under a wide range of protein surface charge densities by changing the protein concentration, buffer pH, and solution ionic strength. Lowering the protein charge density favored the capsid formation. However, lowering charge beyond a certain point resulted in capsid aggregation and precipitation. Interestingly, both the wild-type and D2N/D4N mutant displayed identical assembly profiles when their charge densities matched each other. These results indicated that the charge density was optimized by nature to ensure an efficient and effective capsid proliferation under the physiological pH and ionic strength

Zoom Out and Observe: News Environment Perception for Fake News Detection

Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and "zoom in" to verify its content with knowledge sources or check its readers' replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we "zoom out" to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.Comment: ACL 2022 Main Conference (Long Paper

Near-Optimal Distributed Band-Joins through Recursive Partitioning

We consider running-time optimization for band-joins in a distributed system, e.g., the cloud. To balance load across worker machines, input has to be partitioned, which causes duplication. We explore how to resolve this tension between maximum load per worker and input duplication for band-joins between two relations. Previous work suffered from high optimization cost or considered partitionings that were too restricted (resulting in suboptimal join performance). Our main insight is that recursive partitioning of the join-attribute space with the appropriate split scoring measure can achieve both low optimization cost and low join cost. It is the first approach that is not only effective for one-dimensional band-joins but also for joins on multiple attributes. Experiments indicate that our method is able to find partitionings that are within 10% of the lower bound for both maximum load per worker and input duplication for a broad range of settings, significantly improving over previous work

VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models

The ability to perceive how objects change over time is a crucial ingredient in human intelligence. However, current benchmarks cannot faithfully reflect the temporal understanding abilities of video-language models (VidLMs) due to the existence of static visual shortcuts. To remedy this issue, we present VITATECS, a diagnostic VIdeo-Text dAtaset for the evaluation of TEmporal Concept underStanding. Specifically, we first introduce a fine-grained taxonomy of temporal concepts in natural language in order to diagnose the capability of VidLMs to comprehend different temporal aspects. Furthermore, to disentangle the correlation between static and temporal information, we generate counterfactual video descriptions that differ from the original one only in the specified temporal aspect. We employ a semi-automatic data collection framework using large language models and human-in-the-loop annotation to obtain high-quality counterfactual descriptions efficiently. Evaluation of representative video-language understanding models confirms their deficiency in temporal understanding, revealing the need for greater emphasis on the temporal elements in video-language research.Comment: 23 pages, 6 figures, 18 tables, data is available at https://github.com/lscpku/VITATEC