Learning from Noisy Crowd Labels with Logics

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a ``pseudo-E-step'' that distills from the logic rules a new type of learning target, which is then used in the ``pseudo-M-step'' for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels.Comment: 12 pages, 7 figures, accepted by ICDE-202

Concentration retrieval in a calibration-free wavelength modulation spectroscopy system using particle swarm optimization algorithm

This paper develops a spectral fitting technology based on the particle swarm optimization (PSO) algorithm, which is applied to a calibration-free wavelength modulation spectroscopy system to achieve concentration retrieval. As compared with other spectral fitting technology based on the Levenberg-Marquardt (LM) algorithm, this technology is relatively weakly dependent on the pre-characterization of the laser parameters. The gas concentration is calculated by fitting the simulated spectra to the measured spectra using the PSO algorithm. We validated the simulation with the LM algorithm and PSO algorithm for the target gas C2H2. The results showed that the convergence speed of the spectral fitting technique based on the PSO algorithm was about 63 times faster than the LM algorithm when the fitting accuracy remained the same. Within 5 seconds, the PSO algorithm can produce findings that are generally consistent with the values anticipated.Comment: arXiv admin note: text overlap with arXiv:2210.1654

Laser tuning parameters and concentration retrieval technique for wavelength modulation spectroscopy based on the variable-radius search artificial bee colony algorithm

A novel wavelength modulation spectroscopy (WMS) laser tuning parameters and concentration retrieval technique based on the variable-radius-search artificial bee colony(VRS-ABC) algorithm is proposed. The technique imitates the foraging behavior of bees to achieve the retrieval of gas concentration and laser tuning parameters in a calibration-free WMS system. To address the problem that the basic artificial bee colony(ABC) algorithm tends to converge prematurely, we improve the search method of the scout bee. In contrast to prior concentration retrieval methods that utilized the Levenberg-Marquardt algorithm, the current technique exhibits a reduced dependence on the pre-characterization of laser parameters, leading to heightened precision and reliability in concentration retrieval. We validated the simulation with the VRS-ABC-based technique and the LM-based technique for the target gas C2H2. The simulation results show that the VRS-ABC-based technique performs better in terms of convergence speed and fitting accuracy, especially in the multi-parameter model without exact characterization

Landscape Pattern Analysis and Quality Evaluation in Beijing Hanshiqiao Wetland Nature Reserve

AbstractTaking the Landsat TM and ASTER images of Hanshiqiao wetland nature reserve in 1988, 1996 and 2004 as data source, based on the landscape types from imagery classification, the reserve landscape pattern and its changes were analyzed, meanwhile, the landscape quality and its changes were evaluated and discussed. Several landscape pattern indices were analyzed, the results indicated that from 1988 to 2004, as the result of natural factors and human disturbances, the landscape structure has been changed, landscape fragmentation has become more and more serious, patches have been tended to regular shape, and connectivity of the natural wetland has been weakened. In addition, the landscape quality was evaluated based on the indicators of pressure, state and response. The results showed that during 1996-2004 periods, the landscape quality for Hanshiqiao wetland nature reserve has degraded obviously, which was mainly influenced by human activities breaking into wetland landscape. Effective wetland management and control is therefore needed to solve the issues of the wetland loss and degradation in Hanshiqiao wetland nature reserve

Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications

It’s critical for an autonomous vehicle to acquire accurate and real-time information of the objects in its vicinity, which will fully guarantee the safety of the passengers and vehicle in various environment. 3D LIDAR can directly obtain the position and geometrical structure of the object within its detection range, while vision camera is very suitable for object recognition. Accordingly, this paper presents a novel object detection and identification method fusing the complementary information of two kind of sensors. We first utilize the 3D LIDAR data to generate accurate object-region proposals effectively. Then, these candidates are mapped into the image space where the regions of interest (ROI) of the proposals are selected and input to a convolutional neural network (CNN) for further object recognition. In order to identify all sizes of objects precisely, we combine the features of the last three layers of the CNN to extract multi-scale features of the ROIs. The evaluation results on the KITTI dataset demonstrate that : (1) Unlike sliding windows that produce thousands of candidate object-region proposals, 3D LIDAR provides an average of 86 real candidates per frame and the minimal recall rate is higher than 95%, which greatly lowers the proposals extraction time; (2) The average processing time for each frame of the proposed method is only 66.79ms, which meets the real-time demand of autonomous vehicles; (3) The average identification accuracies of our method for car and pedestrian on the moderate level are 89.04% and 78.18% respectively, which outperform most previous methods

Design optimization and wind tunnel investigation of a flapping system based on the flapping wing trajectories of a beetle's hindwings

To design a flapping-wing micro air vehicle (FWMAV), the hovering flight action of a beetle species (Protaetia brevitarsis) was captured, and various parameters, such as the hindwing flapping frequency, flapping amplitude, angle of attack, rotation angle, and stroke plane angle, were obtained. The wing tip trajectories of the hindwings were recorded and analyzed, and the flapping kinematics were assessed. Based on the wing tip trajectory functions, bioinspired wings and a linkage mechanism flapping system were designed. The critical parameters for the aerodynamic characteristics were investigated and optimized by means of wind tunnel tests, and the artificial flapping system with the best wing parameters was compared with the natural beetle. This work provides insight into how natural flyers execute flight by experimentally duplicating beetle hindwing kinematics and paves the way for the future development of beetle-mimicking FWMAVs

Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler

We propose a neuralized undirected graphical model called Neural-Hidden-CRF to solve the weakly-supervised sequence labeling problem. Under the umbrella of probabilistic undirected graph theory, the proposed Neural-Hidden-CRF embedded with a hidden CRF layer models the variables of word sequence, latent ground truth sequence, and weak label sequence with the global perspective that undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies. Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains new state-of-the-art results on one crowdsourcing benchmark and three weak-supervision benchmarks, including outperforming the recent advanced model CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and inference performance, respectively.Comment: 13 pages, 4 figures, accepted by SIGKDD-202

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Joint Residence-Workplace Location Choice Model Based on Household Decision Behavior

Residence location and workplace are the two most important urban land-use types, and there exist strong interdependences between them. Existing researches often assume that one choice dimension is correlated to the other. Using the mixed logit framework, three groups of choice models are developed to illustrate such choice dependencies. First, for all households, this paper presents a basic methodology of the residence location and workplace choice without decision sequence based on the assumption that the two choice behaviors are independent of each other. Second, the paper clusters all households into two groups, choosing residence or workplace first, and formulates the residence location and workplace choice models under the constraint of decision sequence. Third, this paper combines the residence location and workplace together as the choice alternative and puts forward the joint choice model. A questionnaire survey is implemented in Beijing city to collect the data of 1994 households. Estimation results indicate that the joint choice model fits the data significantly better, and the elasticity effects analyses show that the joint choice model reflects the influences of relevant factors to the choice probability well and leads to the job-housing balance

Unlocking the Single‐Domain Epitaxy of Halide Perovskites

The growth of epitaxial semiconductors and oxides has long since revolutionized the electronics and optics fields, and continues to be exploited to uncover new physics stemming from quantum interactions. While the recent emergence of halide perovskites offers exciting new opportunities for a range of thin‐film electronics, the principles of epitaxy have yet to be applied to this new class of materials and the full potential of these materials is still not yet known. In this work, single‐domain inorganic halide perovskite epitaxy is demonstrated. This is enabled by reactive vapor phase deposition onto single crystal metal halide substrates with congruent ionic interactions. For the archetypical halide perovskite, cesium tin bromide, two epitaxial phases, a cubic phase and tetragonal phase, are uncovered which emerge via stoichiometry control that are both stabilized with vastly differing lattice constants and accommodated via epitaxial rotation. This epitaxial growth is exploited to demonstrate multilayer 2D quantum wells of a halide‐perovskite system. This work ultimately unlocks new routes to push halide perovskites to their full potential.Single‐domain halide perovskite heteroepitaxy is demonstrated and multiple epitaxial phases of archetypical halide perovskite are uncovered via stiochiometry control. The epitaxial growth is further exploited to demonstrate multilayer 2D quantum wells of a halide‐perovskite system and can ultimately enable their full potential in many emerging applications.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140019/1/admi201701003-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140019/2/admi201701003_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140019/3/admi201701003.pd