Neural Network Pruning by Gradient Descent

The rapid increase in the parameters of deep learning models has led to significant costs, challenging computational efficiency and model interpretability. In this paper, we introduce a novel and straightforward neural network pruning framework that incorporates the Gumbel-Softmax technique. This framework enables the simultaneous optimization of a network's weights and topology in an end-to-end process using stochastic gradient descent. Empirical results demonstrate its exceptional compression capability, maintaining high accuracy on the MNIST dataset with only 0.15\% of the original network parameters. Moreover, our framework enhances neural network interpretability, not only by allowing easy extraction of feature importance directly from the pruned network but also by enabling visualization of feature symmetry and the pathways of information propagation from features to outcomes. Although the pruning strategy is learned through deep learning, it is surprisingly intuitive and understandable, focusing on selecting key representative features and exploiting data patterns to achieve extreme sparse pruning. We believe our method opens a promising new avenue for deep learning pruning and the creation of interpretable machine learning systems.Comment: 21 pages, 5 figure

Procuring Innovation on Internet-Based Markets

The Internet-based market is rising as a viable venue for the procurement of innovation solutions. There are two major procurement mechanisms existing in the market practices: contest and RFP. We investigate the factors that affect a firm’s preference of one mechanism over the other. We divide innovation problems into two categories: exploitive innovation problem and exploratory innovation problem. For an exploitive innovation problem, technologies used in solutions already exist, and the outcome of the solution is determined by the type and the effort of a solver. For an exploratory problem, technologies are not available; solvers need to go through an exploratory process but the result of his effort is uncertain. We establish the boundary condition for solution seeker’s decision on procurement mechanism. For an exploitive innovation problem, RFP is preferred in an open-participation market unless the distribution of the solvers’ type has a big variance; for an explorative innovation problem, contest will be preferred in most cases except that the solver pool of the market is small. Moreover, the amount of a cash award, the effort coefficient, and the degree of the randomness endowed in a technology exploratory process all have effect on seekers’ decision

The Allocation of Prizes in Crowdsourcing Contests

A unique characteristic of crowdsourcing contest is the coexistence of multiple contests and each individual contestant strategically chooses the contest that maximizes his/her expected gain. The competition between contests for contestants significantly changes the optimal allocation of prizes for contest organizers. We show that the contestants with higher ability prefer to single-prize contests while those with lower ability prefer to multiple-prize contests, which makes single-prize contest is no longer the optimal choice for organizers as it was in the context of a single contest. We demonstrate that the organizers may allocate multiple prizes whether they intent to maximize total efforts or highest efforts, and presents the condition under which the multiple-prize approach will be optimal

Unilateral pedicle screws asymmetric tethering: an innovative method to create idiopathic deformity

<p>Abstract</p> <p>Objective</p> <p>To evaluate the feasibility of the method that unilateral pedicle screws asymmetric tethering in concave side in combination with convex rib resection for creating idiopathic deformity.</p> <p>Summary of background data</p> <p>Various methods are performed to create idiopathic deformity. Among these methods, posterior asmmetric tethering of the spine shows satisfying result, but some drawbacks related to the current posterior asymmetric tether were still evident.</p> <p>Materials and methods</p> <p>Unilateral pedicle screws asymmetric tethering was performed to 14 female goats (age: 5–8 week-old, weight: 6–8 kg) in concave side in combination with convex rib resection. Dorsoventral and lateral plain radiographs were taken of each thoracic spine in the frontal and sagittal planes right after the surgery and later every 4 weeks.</p> <p>Results</p> <p>All animals ambulated freely after surgery. For technical reasons, 2 goats were excluded (one animal died for anesthetic during the surgery, and one animal was lost for instrumental fail due to postoperative infection). Radiography showed that 11 goats exhibited scoliosis with convex toward to the right side, and as the curve increased with time, only 1 goat showed nonprogressive. The initial scoliosis generated in the progressors after the procedures measured 29.0° on average (range 23.0°–38.5°) and increased to 43.0° on average (range 36.0°–58.0°) over 8 to 10 weeks. The average progression of 14.0° was measured. The curvature immediately after tethering surgery (the initial Cobb angle) did have a highly significant correlation with the final curvature (p < 0.001). The progressive goats showed an idiopathic-like deformity not only by radiography, but in general appearance.</p> <p>Conclusion</p> <p>Unilateral pedicle screws asymmetric tethering is a practical method to create experimental scoliosis, especially for those who would like to study the correction of this deformity.</p

PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, hindering their applications. To address this issue and facilitate related research, we present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection, especially for deliberately hidden items. In specific, PIDray collects 124,486 X-ray images for $12$ categories of prohibited items, and each image is manually annotated with careful inspection, which makes it, to our best knowledge, to largest prohibited items detection dataset to date. Meanwhile, we propose a general divide-and-conquer pipeline to develop baseline algorithms on PIDray. Specifically, we adopt the tree-like structure to suppress the influence of the long-tailed issue in the PIDray dataset, where the first course-grained node is tasked with the binary classification to alleviate the influence of head category, while the subsequent fine-grained node is dedicated to the specific tasks of the tail categories. Based on this simple yet effective scheme, we offer strong task-specific baselines across object detection, instance segmentation, and multi-label classification tasks and verify the generalization ability on common datasets (e.g., COCO and PASCAL VOC). Extensive experiments on PIDray demonstrate that the proposed method performs favorably against current state-of-the-art methods, especially for deliberately hidden items. Our benchmark and codes will be released at https://github.com/lutao2021/PIDray.Comment: Tech. report. arXiv admin note: text overlap with arXiv:2108.0702

Pairs Trading: An Optimal Selling Rule with Constraints

The focus of this paper is on identifying the most effective selling strategy for pairs trading of stocks. In pairs trading, a long position is held in one stock while a short position is held in another. The goal is to determine the optimal time to sell the long position and repurchase the short position in order to close the pairs position. The paper presents an optimal pairs-trading selling rule with trading constraints. In particular, the underlying stock prices evolve according to a two dimensional geometric Brownian motion and the trading permission process is given in terms of a two-state {trading allowed, trading not allowed} Markov chain. It is shown that the optimal policy can be determined by a threshold curve which is obtained by solving the associated HJB equations (quasi-variational inequalities). A closed form solution is obtained. A verification theorem is provided. Numerical experiments are also reported to demonstrate the optimal policies and value functions