76 research outputs found
Energy Consumption and Economic Growth: A Trivariate Framework of South Africa
The study investigates the causal relationship between energy consumption and economic growth in South Africa, covering the period of 1980-2014. In a trivariate framework which includes electricity and inflation as additional variables by applying the Autoregressive Distributed Lag (ARDL) integration method. First unit root test was employed; results indicated that all variables were non-stationary at the level and stationary at their first differences, using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP). The results show a long-run relationship among the variables using the ARDL integration approach. The Granger causality test indicates a unidirectional running from inflation to economic growth, which supports the growth hypothesis as documented in the literature and there was no causality between electricity consumption and economic growth, supporting the neutrality hypothesis. Any policies concerning energy consumption should be re-evaluated to confirm that it will not disturb economic growth. Keywords: ARDL, Economic Growth, Energy Consumption, Granger Causality, and South Africa. DOI: 10.7176/JESD/10-8-12 Publication date: April 30th 201
Learning to Program with Natural Language
Large Language Models (LLMs) have shown remarkable performance in various
basic natural language tasks, which raises hope for achieving Artificial
General Intelligence. For completing the complex task, we still need a program
for the task first and then ask LLMs to follow the program to generate the
specific solution. We propose using natural language as a new programming
language to describe task procedures, making them easily understandable to both
humans and LLMs. ~The LLM is capable of directly generating natural language
programs, but these programs may still contain factual errors or incomplete
steps. Therefore, we further propose the Learning to Program (\text{LP}) method
to ask LLMs themselves to learn the natural language program based on the
training dataset of the complex task first and then use the learned program to
guide the inference. Our experiments on the reasoning tasks of five different
reasoning types (8 datasets) demonstrate the effectiveness of our approach.
Further, our analysis experiment shows that the learned program can be directly
used to guide another LLM to improve its performance, which reveals a new
transfer learning paradigm.Comment: Work in progres
Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
Human intelligence thrives on the concept of cognitive synergy, where
collaboration and information integration among different cognitive processes
yield superior outcomes compared to individual cognitive processes in
isolation. Although Large Language Models (LLMs) have demonstrated promising
performance as general task-solving agents, they still struggle with tasks that
require intensive domain knowledge and complex reasoning. In this work, we
propose Solo Performance Prompting (SPP), which transforms a single LLM into a
cognitive synergist by engaging in multi-turn self-collaboration with multiple
personas. A cognitive synergist refers to an intelligent agent that
collaborates with multiple minds, combining their individual strengths and
knowledge, to enhance problem-solving and overall performance in complex tasks.
By dynamically identifying and simulating different personas based on task
inputs, SPP unleashes the potential of cognitive synergy in LLMs. We have
discovered that assigning multiple, fine-grained personas in LLMs elicits
better problem-solving abilities compared to using a single or fixed number of
personas. We evaluate SPP on three challenging tasks: Trivia Creative Writing,
Codenames Collaborative, and Logic Grid Puzzle, encompassing both
knowledge-intensive and reasoning-intensive types. Unlike previous works, such
as Chain-of-Thought, that solely enhance the reasoning abilities in LLMs, SPP
effectively elicits internal knowledge acquisition abilities, reduces
hallucination, and maintains strong reasoning capabilities. Code, data, and
prompts can be found at:
https://github.com/MikeWangWZHL/Solo-Performance-Prompting.git.Comment: work in progres
Detection of heart rate using smartphone gyroscope data: a scoping review
Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles’ sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology
Quantum phases of SrCu2(BO3)2 from high-pressure thermodynamics
We report heat capacity measurements of SrCu(BO) under high
pressure along with simulations of relevant quantum spin models and map out the
phase diagram of the material. We find a first-order quantum phase
transition between the low-pressure quantum dimer paramagnet and a phase with
signatures of a plaquette-singlet state below T = K. At higher pressures,
we observe a transition into a previously unknown antiferromagnetic state below
K. Our findings can be explained within the two-dimensional
Shastry-Sutherland quantum spin model supplemented by weak inter-layer
couplings. The possibility to tune SrCu(BO) between the
plaquette-singlet and antiferromagnetic states opens opportunities for
experimental tests of quantum field theories and lattice models involving
fractionalized excitations, emergent symmetries, and gauge fluctuations.Comment: 6 pages + 8 pages supplemental informatio
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Artificial Intelligence (AI) has made incredible progress recently. On the
one hand, advanced foundation models like ChatGPT can offer powerful
conversation, in-context learning and code generation abilities on a broad
range of open-domain tasks. They can also generate high-level solution outlines
for domain-specific tasks based on the common sense knowledge they have
acquired. However, they still face difficulties with some specialized tasks
because they lack enough domain-specific data during pre-training or they often
have errors in their neural network computations on those tasks that need
accurate executions. On the other hand, there are also many existing models and
systems (symbolic-based or neural-based) that can do some domain-specific tasks
very well. However, due to the different implementation or working mechanisms,
they are not easily accessible or compatible with foundation models. Therefore,
there is a clear and pressing need for a mechanism that can leverage foundation
models to propose task solution outlines and then automatically match some of
the sub-tasks in the outlines to the off-the-shelf models and systems with
special functionalities to complete them. Inspired by this, we introduce
TaskMatrix.AI as a new AI ecosystem that connects foundation models with
millions of APIs for task completion. Unlike most previous work that aimed to
improve a single AI model, TaskMatrix.AI focuses more on using existing
foundation models (as a brain-like central system) and APIs of other AI models
and systems (as sub-task solvers) to achieve diversified tasks in both digital
and physical domains. As a position paper, we will present our vision of how to
build such an ecosystem, explain each key component, and use study cases to
illustrate both the feasibility of this vision and the main challenges we need
to address next
Deconfined quantum criticality and emergent symmetry in SrCu2(BO3)2
The deconfined quantum critical point (DQCP) represents a paradigm shift in
theories of quantum matter, presenting a "beyond Landau" scenario for
order-order transitions. Its experimental realization, however, has remained
elusive. Here we demonstrate by high-pressure 11B NMR measurements on the
quantum magnet SrCu2(BO3)2 that the magnetic field induced plaquette-singlet to
antiferromagnetic transition above 1.8 GPa is proximate to a DQCP. We find a
weak first-order transition between the two phases at a remarkably low
temperature, Tc~0.07 K. Above Tc we observe quantum critical scaling at the
highest pressure, 2.4 GPa. We explain the low first-order Tc values by a
DQCP-induced emergent O(3) symmetry that is broken in the coexistence state.
Our findings take the DQCP from a theoretical concept to a concrete
experimental platform
Deconfined quantum critical point lost in pressurized SrCu2(BO3)2
In the field of correlated electron materials, the relation between the
resonating spin singlet and antiferromagnetic states has long been an
attractive topic for understanding of the interesting macroscopic quantum
phenomena, such as the ones emerging from magnetic frustrated materials,
antiferromagnets and high-temperature superconductors. SrCu2(BO3)2 is a
well-known quantum magnet, and it is theoretically expected to be the candidate
of correlated electron material for clarifying the existence of a
pressure-induced deconfined quantum critical point (DQCP), featured by a
continuous quantum phase transition, between the plaquette-singlet (PS) valence
bond solid phase and the antiferromagnetic (AF) phase. However, the real nature
of the transition is yet to be identified experimentally due to the technical
challenge. Here we show the experimental results for the first time, through
the state-of-the-art high-pressure heat capacity measurement, that the PS-AF
phase transition of the pressurized SrCu2(BO3)2 at zero field is clearly a
first-order one. Our result clarifies the more than two-decade long debates
about this key issue, and resonates nicely with the recent quantum entanglement
understanding that the theoretically predicted DQCPs in representative lattice
models are actually a first-order transition. Intriguingly, we also find that
the transition temperatures of the PS and AF phase meet at the same
pressure-temperature point, which signifies a bi-critical point as those
observed in Fe-based superconductor and heavy-fermion compound, and constitutes
the first experimental discovery of the pressure-induced bi-critical point in
frustrated magnets. Our results provide fresh information for understanding the
evolution among different spin states of correlated electron materials under
pressure.Comment: 6 pages, 4 figure
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