106 research outputs found
Platelet rich plasma versus corticosteroid injection for plantar fasciitis: A comparative study.
Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization
Domain generalization (DG) methods aim to develop models that generalize to
settings where the test distribution is different from the training data. In
this paper, we focus on the challenging problem of multi-source zero shot DG
(MDG), where labeled training data from multiple source domains is available
but with no access to data from the target domain. A wide range of solutions
have been proposed for this problem, including the state-of-the-art
multi-domain ensembling approaches. Despite these advances, the na\"ive ERM
solution of pooling all source data together and training a single classifier
is surprisingly effective on standard benchmarks. In this paper, we hypothesize
that, it is important to elucidate the link between pre-specified domain labels
and MDG performance, in order to explain this behavior. More specifically, we
consider two popular classes of MDG algorithms -- distributional robust
optimization (DRO) and multi-domain ensembles, in order to demonstrate how
inferring custom domain groups can lead to consistent improvements over the
original domain labels that come with the dataset. To this end, we propose (i)
Group-DRO++, which incorporates an explicit clustering step to identify custom
domains in an existing DRO technique; and (ii) DReaME, which produces effective
multi-domain ensembles through implicit domain re-labeling with a novel
meta-optimization algorithm. Using empirical studies on multiple standard
benchmarks, we show that our variants consistently outperform ERM by
significant margins (1.5% - 9%), and produce state-of-the-art MDG performance.
Our code can be found at https://github.com/kowshikthopalli/DREAM
The Range of Topological Effects on Communication
We continue the study of communication cost of computing functions when
inputs are distributed among processors, each of which is located at one
vertex of a network/graph called a terminal. Every other node of the network
also has a processor, with no input. The communication is point-to-point and
the cost is the total number of bits exchanged by the protocol, in the worst
case, on all edges.
Chattopadhyay, Radhakrishnan and Rudra (FOCS'14) recently initiated a study
of the effect of topology of the network on the total communication cost using
tools from embeddings. Their techniques provided tight bounds for simple
functions like Element-Distinctness (ED), which depend on the 1-median of the
graph. This work addresses two other kinds of natural functions. We show that
for a large class of natural functions like Set-Disjointness the communication
cost is essentially times the cost of the optimal Steiner tree connecting
the terminals. Further, we show for natural composed functions like and , the naive protocols
suggested by their definition is optimal for general networks. Interestingly,
the bounds for these functions depend on more involved topological parameters
that are a combination of Steiner tree and 1-median costs.
To obtain our results, we use some new tools in addition to ones used in
Chattopadhyay et. al. These include (i) viewing the communication constraints
via a linear program; (ii) using tools from the theory of tree embeddings to
prove topology sensitive direct sum results that handle the case of composed
functions and (iii) representing the communication constraints of certain
problems as a family of collection of multiway cuts, where each multiway cut
simulates the hardness of computing the function on the star topology
Role of Temperature in the Growth of Silver Nanoparticles Through a Synergetic Reduction Approach
This study presents the role of reaction temperature in the formation and growth of silver nanoparticles through a synergetic reduction approach using two or three reducing agents simultaneously. By this approach, the shape-/size-controlled silver nanoparticles (plates and spheres) can be generated under mild conditions. It was found that the reaction temperature could play a key role in particle growth and shape/size control, especially for silver nanoplates. These nanoplates could exhibit an intensive surface plasmon resonance in the wavelength range of 700–1,400 nm in the UV–vis spectrum depending upon their shapes and sizes, which make them useful for optical applications, such as optical probes, ionic sensing, and biochemical sensors. A detailed analysis conducted in this study clearly shows that the reaction temperature can greatly influence reaction rate, and hence the particle characteristics. The findings would be useful for optimization of experimental parameters for shape-controlled synthesis of other metallic nanoparticles (e.g., Au, Cu, Pt, and Pd) with desirable functional properties
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