Symmetric Projections of the Entropy Region

Entropy inequalities play a central role in proving converse coding theorems for network information theoretic problems. This thesis studies two new aspects of entropy inequalities. First, inequalities relating average joint entropies rather than entropies over individual subsets are studied. It is shown that the closures of the average entropy regions where the averages are over all subsets of the same size and all sliding windows of the same size respectively are identical, implying that averaging over sliding windows always suffices as far as unconstrained entropy inequalities are concerned. Second, the existence of non-Shannon type inequalities under partial symmetry is studied using the concepts of Shannon and non-Shannon groups. A complete classification of all permutation groups over four elements is established. With five random variables, it is shown that there are no non-Shannon type inequalities under cyclic symmetry

Starting From Ecotone Reconnecting Fragmented Mission Hill

This thesis aims to address the spatial fragmentation of Mission Hill. As an old, crowded and chaotic neighborhood in Boston, Mission Hill is a microcosm of Boston\u27s history. Four hundred years ago, Mission Hill was an ecological ecotone which consisted of a series of transitional landscapes, located on the border of a peninsula surrounded by salt marshes. Today, the history of ecotone has been hidden. Landfill, segregation, gentrification, and climate change have caused fragmented spaces, weak connections, and poor accessibility. Meanwhile, the fragmentation of public open areas has also disrupted people\u27s interaction with one another, and the spatial spirit of the community is lost as a result. This thesis explores the new possibility of Mission Hill community development based on ecotone research and develops a full-scale spatial framework. Incorporating evidence from historical documents and field observations, Mission Hill\u27s existing public open space exists as a reminder of its history as an ecotone. Research on ecotones demonstrates that different species and substances can co-exist and will be transferred efficiently because of the excellent connectivity inside the ecotone. Mission Hill\u27s past as an ecotone creates the possibility of its future as the renewed ecotone. Through reconnecting fragmented open spaces, we can reactivate the history of Mission Hill and rewild Mission Hill to be a new ecotone. Inclusion, integrality and efficiency of ecotones can also be applied to the open areas of Mission Hill to enhance this new spatial system. By returning to the ecotone, Mission Hill can reorganize fragmented spaces, enhance connectivity and accessibility between spaces, activate hidden histories, evoke distant shared memories, and ultimately alleviate the emotional and physical trauma experienced by entire communities since segregation, gentrification, and climate change

Algorithmic Trading with Prior Information

Traders utilize strategies by using a mix of market and limit orders to generate profits. There are different types of traders in the market, some have prior information and can learn from changes in prices to tweak her trading strategy continuously(Informed Traders), some have no prior information but can learn(Uninformed Learners), and some have no prior information and cannot learn(Uninformed Traders). In this thesis. Alvaro C, Sebastian J and Damir K \cite{AL} proposed a model for algorithmic traders to access the impact of dynamic learning in profit and loss in 2014. The traders can employ the model to decide which strategies to use. The model considered the distribution of the prices in the future using prior information, the spread of the bid and ask prices and also the capital appreciation of inventories. I implemented the model for the case when the trader can only learn from and take positions in one asset. Compared to the uninformed traders, the informed trader using the proposed model can change the strategies along time and make higher profits

Variational Relational Point Completion Network for Robust 3D Classification

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion Network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40 dataset) containing over 200,000 high-quality scans, which render partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans. Moreover, we can achieve robust 3D classification for partial point clouds with the help of VRCNet, which can highly increase classification accuracy.Comment: 12 pages, 10 figures, accepted by PAMI. project webpage: https://mvp-dataset.github.io/. arXiv admin note: substantial text overlap with arXiv:2104.1015

The "double-edged sword" effects of career support mentoring on newcomer turnover: How and when it helps or hurts.

Research on mentoring programs has portrayed them almost exclusively beneficial for newcomer retention. Drawing from the social cognitive model of career management and the boundaryless career perspective, we depart from this predominant view and examine the "double-edged sword" effects of career support mentoring on newcomer turnover. We propose that career support mentoring received by newcomers is likely to elicit both internal proactive socialization and external career self-management, which act as countervailing forces driving newcomer turnover in opposite directions (i.e., the retention pathway and the unintended detrimental pathway). We further propose that the organizational role of the mentor-supervisor versus nonsupervisor-is critical in determining which pathway prevails. We conducted two multiwave newcomer studies to test our hypotheses. In Study 1 ( = 495), we found that received career support mentoring was associated with lower newcomer turnover probability through the serial mediation of internal proactive socialization and perceived internal marketability but higher newcomer turnover probability through the serial mediation of external career self-management and perceived external marketability. In Study 2 ( = 193), we found that received career support mentoring was associated with lower newcomer turnover intention through the serial mediation of internal career advancement expectation and internal proactive socialization but higher newcomer turnover intention through the serial mediation of external career advancement expectation and external career self-management. In both studies, the unintended detrimental pathway was significant only when a newcomer's mentor was not a supervisor. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Large-scale Interactive Recommendation with Tree-structured Policy Gradient

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods

Occupation prediction with multimodal learning from Tweet messages and Google Street View images

Despite the development of various heuristic and machine learning models, social media user occupation predication remains challenging due to limited high-quality ground truth data and difficulties in effectively integrating multiple data sources in different modalities, which can be complementary and contribute to informing the profession or job role of an individual. In response, this study introduces a novel semi-supervised multimodal learning method for Twitter user occupation prediction with a limited number of training samples. Specifically, an unsupervised learning model is first designed to extract textual and visual embeddings from individual tweet messages (textual) and Google Street View images (visual), with the latter capturing the geographical and environmental context surrounding individuals’ residential and workplace areas. Next, these high-dimensional multimodal features are fed into a multilayer transfer learning model for individual occupation classification. The proposed occupation prediction method achieves high evaluation scores for identifying Office workers, Students, and Others or Jobless people, with the F1 score for identifying Office workers surpassing the best previously reported scores for occupation classification using social media data