19,963 research outputs found
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
ADS_UNet: A Nested UNet for Histopathology Image Segmentation
The UNet model consists of fully convolutional network (FCN) layers arranged
as contracting encoder and upsampling decoder maps. Nested arrangements of
these encoder and decoder maps give rise to extensions of the UNet model, such
as UNete and UNet++. Other refinements include constraining the outputs of the
convolutional layers to discriminate between segment labels when trained end to
end, a property called deep supervision. This reduces feature diversity in
these nested UNet models despite their large parameter space. Furthermore, for
texture segmentation, pixel correlations at multiple scales contribute to the
classification task; hence, explicit deep supervision of shallower layers is
likely to enhance performance. In this paper, we propose ADS UNet, a stage-wise
additive training algorithm that incorporates resource-efficient deep
supervision in shallower layers and takes performance-weighted combinations of
the sub-UNets to create the segmentation model. We provide empirical evidence
on three histopathology datasets to support the claim that the proposed ADS
UNet reduces correlations between constituent features and improves performance
while being more resource efficient. We demonstrate that ADS_UNet outperforms
state-of-the-art Transformer-based models by 1.08 and 0.6 points on CRAG and
BCSS datasets, and yet requires only 37% of GPU consumption and 34% of training
time as that required by Transformers.Comment: To be published in Expert Systems With Application
A hybrid quantum algorithm to detect conical intersections
Conical intersections are topologically protected crossings between the
potential energy surfaces of a molecular Hamiltonian, known to play an
important role in chemical processes such as photoisomerization and
non-radiative relaxation. They are characterized by a non-zero Berry phase,
which is a topological invariant defined on a closed path in atomic coordinate
space, taking the value when the path encircles the intersection
manifold. In this work, we show that for real molecular Hamiltonians, the Berry
phase can be obtained by tracing a local optimum of a variational ansatz along
the chosen path and estimating the overlap between the initial and final state
with a control-free Hadamard test. Moreover, by discretizing the path into
points, we can use single Newton-Raphson steps to update our state
non-variationally. Finally, since the Berry phase can only take two discrete
values (0 or ), our procedure succeeds even for a cumulative error bounded
by a constant; this allows us to bound the total sampling cost and to readily
verify the success of the procedure. We demonstrate numerically the application
of our algorithm on small toy models of the formaldimine molecule
(\ce{H2C=NH}).Comment: 15 + 10 pages, 4 figure
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Sustainable 4D printing of magneto-electroactive shape memory polymer composites
Typical techniques for creating synthetic morphing structures suffer from a compromise between quick shape change and geometric complexity. A novel approach is proposed for encoding numerous shapes and forms by magneto-electroactive shape memory polymer composite (SMPC) structures and integrating sustainability with 4D printing (4DP) technology. Electrically driven, remote controllability, and quick reaction are the features of these sustainable composite structures. Low-cost 4D-printed SMPC structures can be programmed remotely at high temperatures to achieve multi-stable shapes and can snap repeatedly between all programmed temporary and permanent configurations. This allows for multiple designs in a single structure without wasting material. The strategy is based on a knowledge of SMPC mechanics, magnetic response, and the manufacturing idea underlying fused deposition modelling (FDM). Iron-filled magnetic polylactic acid (MPLA) and carbon black-filled conductive PLA (CPLA) composite materials are investigated in terms of microstructure properties, composite interface, and mechanical properties. Characterisation studies are carried out to identify how to control the structure with a low magnetic field. The shape morphing of magneto-electroactive SMPC structures is studied. FDM is used to 4D print MPLA and CPLA adaptive structures with 1D/2D-to-2D/3D shapeshifting by the magnetic field. The benefits of switchable multi-stable structures are reducing material waste and effort/energy and increasing efficiency in sectors such as packaging
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Preparation, modification, and clinical application of porous tantalum scaffolds
Porous tantalum (Ta) implants have been developed and clinically applied as high-quality implant biomaterials in the orthopedics field because of their excellent corrosion resistance, biocompatibility, osteointegration, and bone conductivity. Porous Ta allows fine bone ingrowth and new bone formation through the inner space because of its high porosity and interconnected pore structure. It contributes to rapid bone integration and long-term stability of osseointegrated implants. Porous Ta has excellent wetting properties and high surface energy, which facilitate the adhesion, proliferation, and mineralization of osteoblasts. Moreover, porous Ta is superior to classical metallic materials in avoiding the stress shielding effect, minimizing the loss of marginal bone, and improving primary stability because of its low elastic modulus and high friction coefficient. Accordingly, the excellent biological and mechanical properties of porous Ta are primarily responsible for its rising clinical translation trend. Over the past 2Â decades, advanced fabrication strategies such as emerging manufacturing technologies, surface modification techniques, and patient-oriented designs have remarkably influenced the microstructural characteristic, bioactive performance, and clinical indications of porous Ta scaffolds. The present review offers an overview of the fabrication methods, modification techniques, and orthopedic applications of porous Ta implants
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
In this paper, we study differentially private empirical risk minimization
(DP-ERM). It has been shown that the worst-case utility of DP-ERM reduces
polynomially as the dimension increases. This is a major obstacle to privately
learning large machine learning models. In high dimension, it is common for
some model's parameters to carry more information than others. To exploit this,
we propose a differentially private greedy coordinate descent (DP-GCD)
algorithm. At each iteration, DP-GCD privately performs a coordinate-wise
gradient step along the gradients' (approximately) greatest entry. We show
theoretically that DP-GCD can achieve a logarithmic dependence on the dimension
for a wide range of problems by naturally exploiting their structural
properties (such as quasi-sparse solutions). We illustrate this behavior
numerically, both on synthetic and real datasets
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
We study the trade-off between expectation and tail risk for regret
distribution in the stochastic multi-armed bandit problem. We fully
characterize the interplay among three desired properties for policy design:
worst-case optimality, instance-dependent consistency, and light-tailed risk.
We show how the order of expected regret exactly affects the decaying rate of
the regret tail probability for both the worst-case and instance-dependent
scenario. A novel policy is proposed to characterize the optimal regret tail
probability for any regret threshold. Concretely, for any given and , our policy achieves a worst-case expected regret
of (we call it -optimal) and an instance-dependent
expected regret of (we call it -consistent), while
enjoys a probability of incurring an regret
( in the worst-case scenario and in the
instance-dependent scenario) that decays exponentially with a polynomial
term. Such decaying rate is proved to be best achievable. Moreover, we discover
an intrinsic gap of the optimal tail rate under the instance-dependent scenario
between whether the time horizon is known a priori or not. Interestingly,
when it comes to the worst-case scenario, this gap disappears. Finally, we
extend our proposed policy design to (1) a stochastic multi-armed bandit
setting with non-stationary baseline rewards, and (2) a stochastic linear
bandit setting. Our results reveal insights on the trade-off between regret
expectation and regret tail risk for both worst-case and instance-dependent
scenarios, indicating that more sub-optimality and inconsistency leave space
for more light-tailed risk of incurring a large regret, and that knowing the
planning horizon in advance can make a difference on alleviating tail risks
On the existence of highly organized communities in networks of locally interacting agents
In this paper we investigate phenomena of spontaneous emergence or purposeful
formation of highly organized structures in networks of related agents. We show
that the formation of large organized structures requires exponentially large,
in the size of the structures, networks. Our approach is based on Kolmogorov,
or descriptional, complexity of networks viewed as finite size strings. We
apply this approach to the study of the emergence or formation of simple
organized, hierarchical, structures based on Sierpinski Graphs and we prove a
Ramsey type theorem that bounds the number of vertices in Kolmogorov random
graphs that contain Sierpinski Graphs as subgraphs. Moreover, we show that
Sierpinski Graphs encompass close-knit relationships among their vertices that
facilitate fast spread and learning of information when agents in their
vertices are engaged in pairwise interactions modelled as two person games.
Finally, we generalize our findings for any organized structure with succinct
representations. Our work can be deployed, in particular, to study problems
related to the security of networks by identifying conditions which enable or
forbid the formation of sufficiently large insider subnetworks with malicious
common goal to overtake the network or cause disruption of its operation
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