Is It We or They? The Effect of Identity on Collaboration and Performance in Buyer-Supplier Relationships

In today’s scale-driven and technology-intensive global economy, collaboration becomes the supply chain’s core capability (Liker and Choi, 2004). A well-developed ability to create and sustain fruitful collaborations gives companies a significant competitive advantage (Kanter, 1994). Retailers are increasingly relying on their suppliers to reduce costs, improve quality, and develop new processes and products faster than their rivals’ vendors can. On the other hand, suppliers benefit from retailers that they are able to monitor store-level demand in real time in order to ensure the top-selling items are in-stock or the accuracy and timeliness of retailer’s demand forecast. Previous literature has shown various ways to promote collaboration in buyer-supplier relationships, but it may also have negative impacts such as deception (Gneezy, 2005), dishonesty (Mazar et al., 2008), or opportunism (John, 1984). This dissertation aims to investigate the impact of two types of identity (induced group identity and natural identity) on discretionary collaborative behaviors without any monetary incentives and supply chain performance in buyer-supplier relationships. Using social identity theory (Tajfel and Turner, 1979), Essay 1 explores the influence of buyer-supplier identification which is defined as perceived oneness of a supplier/buyer with its partner’s organization and experience of its partner’s successes and failures as its own (Ashforth and Mael, 1989) on collaboration and supply chain performance, and the foundation and formation of buyer-supplier identification. To explore the effect of natural identity, particularly, gender identity, Essay 2 addresses the impact of gender composition in buyer-supplier relationships on collaboration, and supply chain performance. It investigates whether females and males exhibit differences in trust and trustworthiness. Controlled laboratory experiments are executed for Essays 1 and 2. Together the two essays bring the concept of identity to supply chain management literature and advance our understanding of the enablers and drivers that can increase buyer-supplier collaboration and supply chain performance

Novel Nonphosphorylated Peptides with Conserved Sequences Selectively Bind to Grb7 SH2 Domain with Affinity Comparable to Its Phosphorylated Ligand

The Grb7 (growth factor receptor-bound 7) protein, a member of the Grb7 protein family, is found to be highly expressed in such metastatic tumors as breast cancer, esophageal cancer, liver cancer, etc. The src-homology 2 (SH2) domain in the C-terminus is reported to be mainly involved in Grb7 signaling pathways. Using the random peptide library, we identified a series of Grb7 SH2 domain-binding nonphosphorylated peptides in the yeast two-hybrid system. These peptides have a conserved GIPT/K/N sequence at the N-terminus and G/WD/IP at the C-terminus, and the region between the N-and C-terminus contains fifteen amino acids enriched with serines, threonines and prolines. The association between the nonphosphorylated peptides and the Grb7 SH2 domain occurred in vitro and ex vivo. When competing for binding to the Grb7 SH2 domain in a complex, one synthesized nonphosphorylated ligand, containing the twenty-two amino acid-motif sequence, showed at least comparable affinity to the phosphorylated ligand of ErbB3 in vitro, and its overexpression inhibited the proliferation of SK-BR-3 cells. Such nonphosphorylated peptides may be useful for rational design of drugs targeted against cancers that express high levels of Grb7 protein

Improving BERT with Hybrid Pooling Network and Drop Mask

Transformer-based pre-trained language models, such as BERT, achieve great success in various natural language understanding tasks. Prior research found that BERT captures a rich hierarchy of linguistic information at different layers. However, the vanilla BERT uses the same self-attention mechanism for each layer to model the different contextual features. In this paper, we propose a HybridBERT model which combines self-attention and pooling networks to encode different contextual features in each layer. Additionally, we propose a simple DropMask method to address the mismatch between pre-training and fine-tuning caused by excessive use of special mask tokens during Masked Language Modeling pre-training. Experiments show that HybridBERT outperforms BERT in pre-training with lower loss, faster training speed (8% relative), lower memory cost (13% relative), and also in transfer learning with 1.5% relative higher accuracies on downstream tasks. Additionally, DropMask improves accuracies of BERT on downstream tasks across various masking rates.Comment: 7 pages, 2 figure

CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuses in Android apps, seldom studies have focused on estimating the security risks introduced by cryptographic misuses. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuses in Android apps. Firstly, we propose a unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection toolchain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful in the app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats, to guide more efficient subsequent mitigations. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps as well as the analysis of popular apps reveals important security observations on the real threats of cryptographic misuses in Android apps