Effect of Schedule Compression on Project Effort

Schedule pressure is often faced by project managers and software developers who want to quickly deploy information systems. Typical strategies to compress project time scales might include adding more staff/personnel, investing in development tools, improving hardware, or improving development methods. The tradeoff between cost, schedule, and performance is one of the most important analyses performed during the planning stages of software development projects. In order to adequately compare the effects of these three constraints on the project it is essential to understand their individual influence on the project’s outcome. In this paper, we present an investigation into the effect of schedule compression on software project development effort and cost and show that people are generally optimistic when estimating the amount of schedule compression. This paper is divided into three sections. First, we follow the Ideal Effort Multiplier (IEM) analysis on the SCED cost driver of the COCOMO II model. Second, compare the real schedule compression ratio exhibited by 161 industry projects and the ratio represented by the SCED cost driver. Finally, based on the above analysis, a set of newly proposed SCED driver ratings for COCOMO II are introduced which show an improvement of 6% in the model estimating accuracy

Measurement method of torsional vibration signal to extract gear meshing characteristics

A technique in measuring torsional vibration signal based on an optical encoder and a discrete wavelet transform is proposed for the extraction of gear meshing characteristics. The method measures the rotation angles of the input and output shafts of a gear pair by using two optical encoders and obtains the time interval sequences of the two shafts. By spline interpolation, the time interval sequences based on uniform angle sampling can be converted into angle interval sequences on the basis of uniform time sampling. The curve of the relative displacement of the gear pair on the meshing line (initial torsional vibration signal) can then be obtained by comparing the rotation angles of the input and output shafts at the interpolated time series. The initial torsional vibration signal is often disturbed by noise. Therefore, a discrete wavelet transform is used to decompose the signal at certain scales; the torsional vibration signal of the gear can then be obtained after filtering. The proposed method was verified by simulation and experimentation, and the results showed that the method could successfully obtain the torsional vibration signal of the gear at a high frequency. The waveforms of the torsional vibration could reflect the meshing characteristics of the teeth. These findings could provide a basis for fault diagnosis of gears

Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation

Text-to-Image (T2I) generation with diffusion models allows users to control the semantic content in the synthesized images given text conditions. As a further step toward a more customized image creation application, we introduce a new multi-modality generation setting that synthesizes images based on not only the semantic-level textual input but also on the pixel-level visual conditions. Existing literature first converts the given visual information to semantic-level representation by connecting it to languages, and then incorporates it into the original denoising process. Seemingly intuitive, such methodological design loses the pixel values during the semantic transition, thus failing to fulfill the task scenario where the preservation of low-level vision is desired (e.g., ID of a given face image). To this end, we propose Cyclic One-Way Diffusion (COW), a training-free framework for creating customized images with respect to semantic text and pixel-visual conditioning. Notably, we observe that sub-regions of an image impose mutual interference, just like physical diffusion, to achieve ultimate harmony along the denoising trajectory. Thus we propose to repetitively utilize the given visual condition in a cyclic way, by planting the visual condition as a high-concentration "seed" at the initialization step of the denoising process, and "diffuse" it into a harmonious picture by controlling a one-way information flow from the visual condition. We repeat the destroy-and-construct process multiple times to gradually but steadily impose the internal diffusion process within the image. Experiments on the challenging one-shot face and text-conditioned image synthesis task demonstrate our superiority in terms of speed, image quality, and conditional fidelity compared to learning-based text-vision conditional methods. Project page is available at: https://bigaandsmallq.github.io/COW/Comment: Project page is available at: https://bigaandsmallq.github.io/COW

Learning to Reduce Information Bottleneck for Object Detection in Aerial Images

Object detection in aerial images is a fundamental research topic in the domain of geoscience and remote sensing. However, advanced progresses on this topic are mainly focused on the designment of backbone networks or header networks, but surprisingly ignored the neck ones. In this letter, we first analyse the importance of the neck network in object detection frameworks from the theory of information bottleneck. Then, to alleviate the information loss problem in the current neck network, we propose a global semantic network, which acts as a bridge from the backbone to the head network in a bidirectional global convolution manner. Compared to the existing neck networks, our method has advantages of capturing rich detailed information and less computational costs. Moreover, we further propose a fusion refinement module, which is used for feature fusion with rich details from different scales. To demonstrate the effectiveness and efficiency of our method, experiments are carried out on two challenging datasets (i.e., DOTA and HRSC2016). Results in terms of accuracy and computational complexity both can verify the superiority of our method.Comment: 5 pages, 3 figure

Topologically enhanced nonlinear optical response of graphene nanoribbon heterojunctions

We study the nonlinear optical properties of heterojunctions made of graphene nanoribbons (GNRs) consisting of two segments with either the same or different topological properties. By utilizing a quantum mechanical approach that incorporates distant-neighbor interactions, we demonstrate that the presence of topological interface states significantly enhances the second- and third-order nonlinear optical response of GNR heterojunctions that are created by merging two topologically inequivalent GNRs. Specifically, GNR heterojunctions with topological interface states display third-order harmonic hyperpolarizabilities that are more than two orders of magnitude larger than those of their similarly sized counterparts without topological interface states, whereas the secondorder harmonic hyperpolarizabilities exhibit a more than ten-fold contrast between heterojunctions with and without topological interface states. Additionally, we find that the topological state at the interface between two topologically distinct GNRs can induce a noticeable red-shift of the quantum plasmon frequency of the heterojunctions. Our results reveal a general and profound connection between the existence of topological states and an enhanced nonlinear optical response of graphene nanostructures and possible other photonic systems.Comment: 7 pages,5 figure

Damage Mechanism of Cu6Sn5 Intermetallics Due to Cyclic Polymorphic Transitions.

The formation of high-melting-point Cu6Sn5 interconnections is crucial to overcome the collapse of Sn-based micro-bumps and to produce reliable intermetallic interconnections in three-dimensional (3D) packages. However, because of multiple reflows in 3D package manufacturing, Cu6Sn5 interconnections will experience cyclic polymorphic transitions in the solid state. The repeated and abrupt changes in the Cu6Sn5 lattice due to the cyclic polymorphic transitions can cause extreme strain oscillations, producing damage at the surface and in the interior of the Cu6Sn5 matrix. Moreover, because of the polymorphic transition-induced grain splitting and superstructure phase formation, the reliability of Cu6Sn5 interconnections will thus face great challenges in 3D packages. In addition, the Cu6Sn5 polymorphic transition is structure-dependent, and the η ’↔ η polymorphic transition will occur at the surface while the η ’↔ ηs ↔ η polymorphic transition will occur in the deep matrix. This study can provide in-depth understanding of the structural evolution and damage mechanism of Cu6Sn5 interconnections in real 3D package manufacturing

Comparative efficacy of different renin angiotensin system blockade therapies in patients with IgA nephropathy: a Bayesian network meta-analysis of 17 RCTs

Background IgA nephropathy (IgAN) is still one of the most prevalent forms of primary glomerulonephritis globally. However, no guidelines have clearly indicated which kinds of renin angiotensin system blockade therapies (ACEIs or ARBs or their combination) in patients with IgAN result in a greater reduction in proteinuria and a better preservation of kidney function. Thus, we conducted a Bayesian network analysis to evaluate the relative effects of these three therapy regimens in patients with IgAN. Methods The protocol was registered in PROSPERO with ID CRD42017073726. We comprehensively searched the PubMed, the Cochrane Library, Embase, China Biology Medicine disc, WanFang and CNKI databases for studies published since 1993 as well as some grey literature according to PICOS strategies. Pairwise meta-analysis and Bayesian network analysis were conducted to evaluate the effect of different regimens. Results Seventeen randomized controlled trials (RCTs) involving 1,006 patients were analyzed. Co-administration of ACEIs and ARBs had the highest probability (92%) of being the most effective therapy for reducing proteinuria and blood pressure, but ACEIs would be the most appropriate choice for protecting kidney function in IgAN. Conclusion The combination of ACEIs and ARBs seems to have a significantly better antiproteinuric effect and a greater reduction of blood pressure than ACEI or ARB monotherapy in IgAN. ACEIs appear to be a more renoprotective therapy regimen among three therapies

Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias

Node representation learning on attributed graphs -- whose nodes are associated with rich attributes (e.g., texts and protein sequences) -- plays a crucial role in many important downstream tasks. To encode the attributes and graph structures simultaneously, recent studies integrate pre-trained models with graph neural networks (GNNs), where pre-trained models serve as node encoders (NEs) to encode the attributes. As jointly training large NEs and GNNs on large-scale graphs suffers from severe scalability issues, many methods propose to train NEs and GNNs separately. Consequently, they do not take feature convolutions in GNNs into consideration in the training phase of NEs, leading to a significant learning bias from that by the joint training. To address this challenge, we propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias. More importantly, we show that LD converges to the optimal objective function values by thejoint training under mild assumptions. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph Benchmark datasets

AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive SelfSupervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR.Comment: Accepted by NeurIPS 202