16 research outputs found
Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking
Efficiently reviewing scholarly literature and synthesizing prior art are
crucial for scientific progress. Yet, the growing scale of publications and the
burden of knowledge make synthesis of research threads more challenging than
ever. While significant research has been devoted to helping scholars interact
with individual papers, building research threads scattered across multiple
papers remains a challenge. Most top-down synthesis (and LLMs) make it
difficult to personalize and iterate on the output, while bottom-up synthesis
is costly in time and effort. Here, we explore a new design space of
mixed-initiative workflows. In doing so we develop a novel computational
pipeline, Synergi, that ties together user input of relevant seed threads with
citation graphs and LLMs, to expand and structure them, respectively. Synergi
allows scholars to start with an entire threads-and-subthreads structure
generated from papers relevant to their interests, and to iterate and customize
on it as they wish. In our evaluation, we find that Synergi helps scholars
efficiently make sense of relevant threads, broaden their perspectives, and
increases their curiosity. We discuss future design implications for
thread-based, mixed-initiative scholarly synthesis support tools.Comment: ACM UIST'2
Imitation of Life: A Search Engine for Biologically Inspired Design
Biologically Inspired Design (BID), or Biomimicry, is a problem-solving
methodology that applies analogies from nature to solve engineering challenges.
For example, Speedo engineers designed swimsuits based on shark skin. Finding
relevant biological solutions for real-world problems poses significant
challenges, both due to the limited biological knowledge engineers and
designers typically possess and to the limited BID resources. Existing BID
datasets are hand-curated and small, and scaling them up requires costly human
annotations.
In this paper, we introduce BARcode (Biological Analogy Retriever), a search
engine for automatically mining bio-inspirations from the web at scale. Using
advances in natural language understanding and data programming, BARcode
identifies potential inspirations for engineering challenges. Our experiments
demonstrate that BARcode can retrieve inspirations that are valuable to
engineers and designers tackling real-world problems, as well as recover famous
historical BID examples. We release data and code; we view BARcode as a step
towards addressing the challenges that have historically hindered the practical
application of BID to engineering innovation.Comment: To be published in the AAAI 2024 Proceedings Main Trac
ComLittee: Literature Discovery with Personal Elected Author Committees
In order to help scholars understand and follow a research topic, significant
research has been devoted to creating systems that help scholars discover
relevant papers and authors. Recent approaches have shown the usefulness of
highlighting relevant authors while scholars engage in paper discovery.
However, these systems do not capture and utilize users' evolving knowledge of
authors. We reflect on the design space and introduce ComLittee, a literature
discovery system that supports author-centric exploration. In contrast to
paper-centric interaction in prior systems, ComLittee's author-centric
interaction supports curation of research threads from individual authors,
finding new authors and papers with combined signals from a paper recommender
and the curated authors' authorship graphs, and understanding them in the
context of those signals. In a within-subjects experiment that compares to an
author-highlighting approach, we demonstrate how ComLittee leads to a higher
efficiency, quality, and novelty in author discovery that also improves paper
discovery
One-directional flow of ionic solutions along fine electrodes under an alternating current electric field
Electric fields are widely used for controlling liquids in various research fields. To control a liquid, an alternating current (AC) electric field can offer unique advantages over a direct current (DC) electric field, such as fast and programmable flows and reduced side effects, namely the generation of gas bubbles. Here, we demonstrate one-directional flow along carbon nanotube nanowires under an AC electric field, with no additional equipment or frequency matching. This phenomenon has the following characteristics: First, the flow rates of the transported liquid were changed by altering the frequency showing Gaussian behaviour. Second, a particular frequency generated maximum liquid flow. Third, flow rates with an AC electric field (approximately nanolitre per minute) were much faster than those of a DC electric field (approximately picolitre per minute). Fourth, the flow rates could be controlled by changing the applied voltage, frequency, ion concentration of the solution and offset voltage. Our finding of microfluidic control using an AC electric field could provide a new method for controlling liquids in various research fields
Mitigating Barriers to Public Social Interaction with Meronymous Communication
In communities with social hierarchies, fear of judgment can discourage
communication. While anonymity may alleviate some social pressure, fully
anonymous spaces enable toxic behavior and hide the social context that
motivates people to participate and helps them tailor their communication. We
explore a design space of meronymous communication, where people can reveal
carefully chosen aspects of their identity and also leverage trusted endorsers
to gain credibility. We implemented these ideas in a system for scholars to
meronymously seek and receive paper recommendations on Twitter and Mastodon. A
formative study with 20 scholars confirmed that scholars see benefits to
participating but are deterred due to social anxiety. From a month-long public
deployment, we found that with meronymity, junior scholars could comfortably
ask ``newbie'' questions and get responses from senior scholars who they
normally found intimidating. Responses were also tailored to the aspects about
themselves that junior scholars chose to reveal.Comment: Proceedings of the CHI Conference on Human Factors in Computing
Systems (CHI '24), May 11--16, 2024, Honolulu, HI, US
Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
Training a state-of-the-art deep neural network (DNN) is a
computationally-expensive and time-consuming process, which incentivizes deep
learning developers to debug their DNNs for computational performance. However,
effectively performing this debugging requires intimate knowledge about the
underlying software and hardware systems---something that the typical deep
learning developer may not have. To help bridge this gap, we present Skyline: a
new interactive tool for DNN training that supports in-editor computational
performance profiling, visualization, and debugging. Skyline's key contribution
is that it leverages special computational properties of DNN training to
provide (i) interactive performance predictions and visualizations, and (ii)
directly manipulatable visualizations that, when dragged, mutate the batch size
in the code. As an in-editor tool, Skyline allows users to leverage these
diagnostic features to debug the performance of their DNNs during development.
An exploratory qualitative user study of Skyline produced promising results;
all the participants found Skyline to be useful and easy to use.Comment: 14 pages, 5 figures. Appears in the proceedings of UIST'2
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to
others. However, research papers are information-dense, and as the volume of
the scientific literature grows, the need for new technology to support the
reading process grows. In contrast to the process of finding papers, which has
been transformed by Internet technology, the experience of reading research
papers has changed little in decades. The PDF format for sharing research
papers is widely used due to its portability, but it has significant downsides
including: static content, poor accessibility for low-vision readers, and
difficulty reading on mobile devices. This paper explores the question "Can
recent advances in AI and HCI power intelligent, interactive, and accessible
reading interfaces -- even for legacy PDFs?" We describe the Semantic Reader
Project, a collaborative effort across multiple institutions to explore
automatic creation of dynamic reading interfaces for research papers. Through
this project, we've developed ten research prototype interfaces and conducted
usability studies with more than 300 participants and real-world users showing
improved reading experiences for scholars. We've also released a production
reading interface for research papers that will incorporate the best features
as they mature. We structure this paper around challenges scholars and the
public face when reading research papers -- Discovery, Efficiency,
Comprehension, Synthesis, and Accessibility -- and present an overview of our
progress and remaining open challenges
25th annual computational neuroscience meeting: CNS-2016
The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong
Augmenting Scientific Creativity with an Analogical Search Engine
Analogies have been central to creative problem-solving throughout the
history of science and technology. As the number of scientific papers continues
to increase exponentially, there is a growing opportunity for finding diverse
solutions to existing problems. However, realizing this potential requires the
development of a means for searching through a large corpus that goes beyond
surface matches and simple keywords. Here we contribute the first end-to-end
system for analogical search on scientific papers and evaluate its
effectiveness with scientists' own problems. Using a human-in-the-loop AI
system as a probe we find that our system facilitates creative ideation, and
that ideation success is mediated by an intermediate level of matching on the
problem abstraction (i.e., high versus low). We also demonstrate a fully
automated AI search engine that achieves a similar accuracy with the
human-in-the-loop system. We conclude with design implications for enabling
automated analogical inspiration engines to accelerate scientific innovation