111 research outputs found
Evolving network structure of academic institutions
Today’s colleges and universities consist of highly complex structures that dictate interactions between the administration, faculty, and student body. These structures can play a role in dictating the efficiency of policy enacted by the administration and determine the effect that curriculum changes in one department have on other departments. Despite the fact that the features of these complex structures have a strong impact on the institutions, they remain by-and-large unknown in many cases. In this paper we study the academic structure of our home institution of Trinity College in Hartford, CT using the major and minor patterns between graduating students to build a temporal multiplex network describing the interactions between different departments. Using recent network science techniques developed for such temporal networks we identify the evolving community structures that organize departments’ interactions, as well as quantify the interdisciplinary centrality of each department. We implement this framework for Trinity College, finding practical insights and applications, but also present it as a general framework for colleges and universities to better understand their own structural makeup in order to better inform academic and administrative policy
Analysis of an Ecoepidemiological Model with Prey Refuges
An ecoepidemiological system with prey refuges and disease in prey is proposed. Bilinear incidence and Holling III functional response are used to model the contact process and the predation process, respectively. We will study the stability behavior of the basic system from a local to a global perspective. Permanence of the considered system is also investigated
Achieving Adversarial Robustness via Sparsity
Network pruning has been known to produce compact models without much
accuracy degradation. However, how the pruning process affects a network's
robustness and the working mechanism behind remain unresolved. In this work, we
theoretically prove that the sparsity of network weights is closely associated
with model robustness. Through experiments on a variety of adversarial pruning
methods, we find that weights sparsity will not hurt but improve robustness,
where both weights inheritance from the lottery ticket and adversarial training
improve model robustness in network pruning. Based on these findings, we
propose a novel adversarial training method called inverse weights inheritance,
which imposes sparse weights distribution on a large network by inheriting
weights from a small network, thereby improving the robustness of the large
network
Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing
In executable task-oriented semantic parsing, the system aims to translate
users' utterances in natural language to machine-interpretable programs (API
calls) that can be executed according to pre-defined API specifications. With
the popularity of Large Language Models (LLMs), in-context learning offers a
strong baseline for such scenarios, especially in data-limited regimes.
However, LLMs are known to hallucinate and therefore pose a formidable
challenge in constraining generated content. Thus, it remains uncertain if LLMs
can effectively perform task-oriented utterance-to-API generation where
respecting API's structural and task-specific constraints is crucial.
In this work, we seek to measure, analyze and mitigate such constraints
violations. First, we identify the categories of various constraints in
obtaining API-semantics from task-oriented utterances, and define fine-grained
metrics that complement traditional ones. Second, we leverage these metrics to
conduct a detailed error analysis of constraints violations seen in
state-of-the-art LLMs, which motivates us to investigate two mitigation
strategies: Semantic-Retrieval of Demonstrations (SRD) and API-aware
Constrained Decoding (API-CD). Our experiments show that these strategies are
effective at reducing constraints violations and improving the quality of the
generated API calls, but require careful consideration given their
implementation complexity and latency
Homoepitaxial regrowth habits of ZnO nanowire arrays
Synthetic regrowth of ZnO nanowires [NWs] under a similar chemical vapor transport and condensation [CVTC] process can produce abundant ZnO nanostructures which are not possible by a single CVTC step. In this work, we report three different regrowth modes of ZnO NWs: axial growth, radial growth, and both directions. The different growth modes seem to be determined by the properties of initial ZnO NW templates. By varying the growth parameters in the first-step CVTC process, ZnO nanostructures (e.g., nanoantenna) with drastically different morphologies can be obtained with distinct photoluminescence properties. The results have implications in guiding the rational synthesis of various ZnO NW heterostructures
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