196 research outputs found
Direct Measure of Giant Magnetocaloric Entropy Contributions in Ni-Mn-In
Off-stoichiometric alloys based on Ni 2 MnIn have drawn attention due to the
coupled first order magnetic and structural transformations, and the large
magnetocaloric entropy associated with the transformations. Here we describe
calorimetric and magnetic studies of four compositions. The results provide a
direct measure of entropy changes contributions including at the first-order
phase transitions, and thereby a determination of the maximum field-induced
entropy change corresponding to the giant magnetocaloric effect. We find a
large excess entropy change, attributed to magneto-elastic coupling, but only
in compositions with no ferromagnetic order in the high-temperature austenite
phase. Furthermore, a molecular field model corresponding to antiferromagnetism
of the low-temperature phases is in good agreement, and nearly independent of
composition, despite significant differences in overall magnetic response of
these materials
A Pairwise Dataset for GUI Conversion and Retrieval between Android Phones and Tablets
With the popularity of smartphones and tablets, users have become accustomed
to using different devices for different tasks, such as using their phones to
play games and tablets to watch movies. To conquer the market, one app is often
available on both smartphones and tablets. However, although one app has
similar graphic user interfaces (GUIs) and functionalities on phone and tablet,
current app developers typically start from scratch when developing a
tablet-compatible version of their app, which drives up development costs and
wastes existing design resources. Researchers are attempting to employ deep
learning in automated GUIs development to enhance developers' productivity.
Deep learning models rely heavily on high-quality datasets. There are currently
several publicly accessible GUI page datasets for phones, but none for pairwise
GUIs between phones and tablets. This poses a significant barrier to the
employment of deep learning in automated GUI development. In this paper, we
collect and make public the Papt dataset, which is a pairwise dataset for GUI
conversion and retrieval between Android phones and tablets. The dataset
contains 10,035 phone-tablet GUI page pairs from 5,593 phone-tablet app pairs.
We illustrate the approaches of collecting pairwise data and statistical
analysis of this dataset. We also illustrate the advantages of our dataset
compared to other current datasets. Through preliminary experiments on this
dataset, we analyse the present challenges of utilising deep learning in
automated GUI development and find that our dataset can assist the application
of some deep learning models to tasks involving automatic GUI development.Comment: 10 pages, 9 figure
Calorimetric and magnetic study for NiMnIn and relative cooling power in paramagnetic inverse magnetocaloric systems
The non-stoichiometric Heusler alloy NiMnIn undergoes a
martensitic phase transformation in the vicinity of 345 K, with the high
temperature austenite phase exhibiting paramagnetic rather than ferromagnetic
behavior, as shown in similar alloys with lower-temperature transformations.
Suitably prepared samples are shown to exhibit a sharp transformation, a
relatively small thermal hysteresis, and a large field-induced entropy change.
We analyzed the magnetocaloric behavior both through magnetization and direct
field-dependent calorimetry measurements. For measurements passing through the
first-order transformation, an improved method for heat-pulse relaxation
calorimetry was designed. The results provide a firm basis for the analytic
evaluation of field-induced entropy changes in related materials. An analysis
of the relative cooling power (RCP), based on the integrated field-induced
entropy change and magnetizing behavior of the Mn spin system with
ferromagnetic correlations, shows that a significant RCP may be obtained in
these materials by tuning the magnetic and structural transformation
temperatures through minor compositional changes or local order changes
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
Recent breakthroughs in natural language processing (NLP) have permitted the
synthesis and comprehension of coherent text in an open-ended way, therefore
translating the theoretical algorithms into practical applications. The large
language models (LLMs) have significantly impacted businesses such as report
summarization software and copywriters. Observations indicate, however, that
LLMs may exhibit social prejudice and toxicity, posing ethical and societal
dangers of consequences resulting from irresponsibility. Large-scale benchmarks
for accountable LLMs should consequently be developed. Although several
empirical investigations reveal the existence of a few ethical difficulties in
advanced LLMs, there is little systematic examination and user study of the
risks and harmful behaviors of current LLM usage. To further educate future
efforts on constructing ethical LLMs responsibly, we perform a qualitative
research method called ``red teaming'' on OpenAI's ChatGPT\footnote{In this
paper, ChatGPT refers to the version released on Dec 15th.} to better
understand the practical features of ethical dangers in recent LLMs. We analyze
ChatGPT comprehensively from four perspectives: 1) \textit{Bias} 2)
\textit{Reliability} 3) \textit{Robustness} 4) \textit{Toxicity}. In accordance
with our stated viewpoints, we empirically benchmark ChatGPT on multiple sample
datasets. We find that a significant number of ethical risks cannot be
addressed by existing benchmarks, and hence illustrate them via additional case
studies. In addition, we examine the implications of our findings on AI ethics
and harmal behaviors of ChatGPT, as well as future problems and practical
design considerations for responsible LLMs. We believe that our findings may
give light on future efforts to determine and mitigate the ethical hazards
posed by machines in LLM applications.Comment: Technical Repor
PHRIT: Parametric Hand Representation with Implicit Template
We propose PHRIT, a novel approach for parametric hand mesh modeling with an
implicit template that combines the advantages of both parametric meshes and
implicit representations. Our method represents deformable hand shapes using
signed distance fields (SDFs) with part-based shape priors, utilizing a
deformation field to execute the deformation. The model offers efficient
high-fidelity hand reconstruction by deforming the canonical template at
infinite resolution. Additionally, it is fully differentiable and can be easily
used in hand modeling since it can be driven by the skeleton and shape latent
codes. We evaluate PHRIT on multiple downstream tasks, including
skeleton-driven hand reconstruction, shapes from point clouds, and single-view
3D reconstruction, demonstrating that our approach achieves realistic and
immersive hand modeling with state-of-the-art performance.Comment: Accepted by ICCV202
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images
Most deep learning approaches to comprehensive semantic modeling of 3D indoor
spaces require costly dense annotations in the 3D domain. In this work, we
explore a central 3D scene modeling task, namely, semantic scene reconstruction
without using any 3D annotations. The key idea of our approach is to design a
trainable model that employs both incomplete 3D reconstructions and their
corresponding source RGB-D images, fusing cross-domain features into volumetric
embeddings to predict complete 3D geometry, color, and semantics with only 2D
labeling which can be either manual or machine-generated. Our key technical
innovation is to leverage differentiable rendering of color and semantics to
bridge 2D observations and unknown 3D space, using the observed RGB images and
2D semantics as supervision, respectively. We additionally develop a learning
pipeline and corresponding method to enable learning from imperfect predicted
2D labels, which could be additionally acquired by synthesizing in an augmented
set of virtual training views complementing the original real captures,
enabling more efficient self-supervision loop for semantics. In this work, we
propose an end-to-end trainable solution jointly addressing geometry
completion, colorization, and semantic mapping from limited RGB-D images,
without relying on any 3D ground-truth information. Our method achieves
state-of-the-art performance of semantic scene reconstruction on two
large-scale benchmark datasets MatterPort3D and ScanNet, surpasses baselines
even with costly 3D annotations. To our knowledge, our method is also the first
2D-driven method addressing completion and semantic segmentation of real-world
3D scans
A femtosecond time resolved view of vibrationally assisted electron transfer across the metal/aqueous interface
Understanding heterogeneous charge transfer is crucial if we are to build the
best electrolyzers, fuel cells and photoelectrochemical water splitting devices
that chemistry allows. Because the elementary processes involved have
timescales ranging from femto- to milliseconds, direct simulation is not
generally possible. Model Hamiltonian approaches thus have a crucial role in
gaining mechanistic insight. Current generations of such theories describe a
reactant(s) or product(s) that interacts with electrolyte via a single
effective interaction. Such approaches thus obscure the extent to which
particular solvent fluctuations influence charge transfer. Here we demonstrate
experimentally that for a prototypical system, a ferrocene terminated alkane
thiol self-assembled monolayer (SAM) on gold in contact with aqueous
electrolyte, charge transfer from the Au to the ferrocene can be induced by
vibrational excitation of the ferrocene aromatic CH. Intriguingly the energy of
the aromatic CH vibration, 0.38 eV, is a large fraction of the effective
solvent interaction strength inferred for the ferrocene/ferrocenium system in
prior electrochemical studies: 0.85 eV. Our results thus demonstrate the
coupling of charge transfer to a specific solvent motion and more generally
imply that solvent may affect reduction/oxidation rates in electrocatalysis by
coupling to a few distinct solvent motions. Identifying these motions is
crucial in rationalizing trends in reactivity with change in electrolyte and
thus in pursuing electrolyte engineering from first principles.Comment: 18 pages, 6 figure
- …