11,564 research outputs found
Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: A Systematic Review
Background: The use of machine learning (ML) in mental health (MH) research
is increasing, especially as new, more complex data types become available to
analyze. By systematically examining the published literature, this review aims
to uncover potential gaps in the current use of ML to study MH in vulnerable
populations of immigrants, refugees, migrants, and racial and ethnic
minorities.
Methods: In this systematic review, we queried Google Scholar for ML-related
terms, MH-related terms, and a population of a focus search term strung
together with Boolean operators. Backward reference searching was also
conducted. Included peer-reviewed studies reported using a method or
application of ML in an MH context and focused on the populations of interest.
We did not have date cutoffs. Publications were excluded if they were narrative
or did not exclusively focus on a minority population from the respective
country. Data including study context, the focus of mental healthcare, sample,
data type, type of ML algorithm used, and algorithm performance was extracted
from each.
Results: Our search strategies resulted in 67,410 listed articles from Google
Scholar. Ultimately, 12 were included. All the articles were published within
the last 6 years, and half of them studied populations within the US. Most
reviewed studies used supervised learning to explain or predict MH outcomes.
Some publications used up to 16 models to determine the best predictive power.
Almost half of the included publications did not discuss their cross-validation
method.
Conclusions: The included studies provide proof-of-concept for the potential
use of ML algorithms to address MH concerns in these special populations, few
as they may be. Our systematic review finds that the clinical application of
these models for classifying and predicting MH disorders is still under
development
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR
Automatic speech recognition (ASR) has gained a remarkable success thanks to
recent advances of deep learning, but it usually degrades significantly under
real-world noisy conditions. Recent works introduce speech enhancement (SE) as
front-end to improve speech quality, which is proved effective but may not be
optimal for downstream ASR due to speech distortion problem. Based on that,
latest works combine SE and currently popular self-supervised learning (SSL) to
alleviate distortion and improve noise robustness. Despite the effectiveness,
the speech distortion caused by conventional SE still cannot be completely
eliminated. In this paper, we propose a self-supervised framework named
Wav2code to implement a generalized SE without distortions for noise-robust
ASR. First, in pre-training stage the clean speech representations from SSL
model are sent to lookup a discrete codebook via nearest-neighbor feature
matching, the resulted code sequence are then exploited to reconstruct the
original clean representations, in order to store them in codebook as prior.
Second, during finetuning we propose a Transformer-based code predictor to
accurately predict clean codes by modeling the global dependency of input noisy
representations, which enables discovery and restoration of high-quality clean
representations without distortions. Furthermore, we propose an interactive
feature fusion network to combine original noisy and the restored clean
representations to consider both fidelity and quality, resulting in even more
informative features for downstream ASR. Finally, experiments on both synthetic
and real noisy datasets demonstrate that Wav2code can solve the speech
distortion and improve ASR performance under various noisy conditions,
resulting in stronger robustness.Comment: 12 pages, 7 figures, Submitted to IEEE/ACM TASL
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació
Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?
The use of pretrained embeddings has become widespread in modern e-commerce
machine learning (ML) systems. In practice, however, we have encountered
several key issues when using pretrained embedding in a real-world production
system, many of which cannot be fully explained by current knowledge.
Unfortunately, we find that there is a lack of a thorough understanding of how
pre-trained embeddings work, especially their intrinsic properties and
interactions with downstream tasks. Consequently, it becomes challenging to
make interactive and scalable decisions regarding the use of pre-trained
embeddings in practice.
Our investigation leads to two significant discoveries about using pretrained
embeddings in e-commerce applications. Firstly, we find that the design of the
pretraining and downstream models, particularly how they encode and decode
information via embedding vectors, can have a profound impact. Secondly, we
establish a principled perspective of pre-trained embeddings via the lens of
kernel analysis, which can be used to evaluate their predictability,
interactively and scalably. These findings help to address the practical
challenges we faced and offer valuable guidance for successful adoption of
pretrained embeddings in real-world production. Our conclusions are backed by
solid theoretical reasoning, benchmark experiments, as well as online testings
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
An efficient team is essential for the company to successfully complete new
projects. To solve the team formation problem considering person-job matching
(TFP-PJM), a 0-1 integer programming model is constructed, which considers both
person-job matching and team members' willingness to communicate on team
efficiency, with the person-job matching score calculated using intuitionistic
fuzzy numbers. Then, a reinforcement learning-assisted genetic programming
algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP
adopts the ensemble population strategies. Before the population evolution at
each generation, the agent selects one from four population search modes
according to the information obtained, thus realizing a sound balance of
exploration and exploitation. In addition, surrogate models are used in the
algorithm to evaluate the formation plans generated by individuals, which
speeds up the algorithm learning process. Afterward, a series of comparison
experiments are conducted to verify the overall performance of RL-GP and the
effectiveness of the improved strategies within the algorithm. The
hyper-heuristic rules obtained through efficient learning can be utilized as
decision-making aids when forming project teams. This study reveals the
advantages of reinforcement learning methods, ensemble strategies, and the
surrogate model applied to the GP framework. The diversity and intelligent
selection of search patterns along with fast adaptation evaluation, are
distinct features that enable RL-GP to be deployed in real-world enterprise
environments.Comment: 16 page
Floquet codes and phases in twist-defect networks
We introduce a class of models, dubbed paired twist-defect networks, that
generalize the structure of Kitaev's honeycomb model for which there is a
direct equivalence between: i) Floquet codes (FCs), ii) adiabatic loops of
gapped Hamiltonians, and iii) unitary loops or Floquet-enriched topological
orders (FETs) many-body localized phases. This formalism allows one to apply
well-characterized topological index theorems for FETs to understand the
dynamics of FCs, and to rapidly assess the code properties of many FC models.
As an application, we show that the Honeycomb Floquet code of Haah and Hastings
is governed by an irrational value of the chiral Floquet index, which implies a
topological obstruction to forming a simple, logical boundary with the same
periodicity as the bulk measurement schedule. In addition, we construct
generalizations of the Honeycomb Floquet code exhibiting arbitrary
anyon-automorphism dynamics for general types of Abelian topological order.Comment: 17+5 pages, 10 figure
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
Generalized Weak Supervision for Neural Information Retrieval
Neural ranking models (NRMs) have demonstrated effective performance in
several information retrieval (IR) tasks. However, training NRMs often requires
large-scale training data, which is difficult and expensive to obtain. To
address this issue, one can train NRMs via weak supervision, where a large
dataset is automatically generated using an existing ranking model (called the
weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the
observed data and significantly outperform the weak labeler. This paper
generalizes this idea through an iterative re-labeling process, demonstrating
that weakly supervised models can iteratively play the role of weak labeler and
significantly improve ranking performance without using manually labeled data.
The proposed Generalized Weak Supervision (GWS) solution is generic and
orthogonal to the ranking model architecture. This paper offers four
implementations of GWS: self-labeling, cross-labeling, joint cross- and
self-labeling, and greedy multi-labeling. GWS also benefits from a query
importance weighting mechanism based on query performance prediction methods to
reduce noise in the generated training data. We further draw a theoretical
connection between self-labeling and Expectation-Maximization. Our experiments
on two passage retrieval benchmarks suggest that all implementations of GWS
lead to substantial improvements compared to weak supervision in all cases
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Superfluidity and Superconductivity in Body-centred-cubic and Face-centred-cubic Systems
The microscopic description of phases in strongly correlated systems such as the fullerides (A3C60) is a challenge. In particular, how these strong interactions become attraction leading to a superconducting state remains a mystery. Understanding the mechanism(s) that drive(s) unconventional superconductivity is one of the most sought-after goals in many-body physics and indeed very complex to solve.
The aim of this thesis is, firstly, to investigate the conditions in which pairing may take place between two electrons in both body-centred cubic (BCC) and face-centred cubic (FCC) systems, and secondly, to examine the possibility for the emergence of a superconducting or superfluid state from paired electrons in three-dimensional (3D) systems. Here, pair properties are studied both in the anti-adiabatic and adiabatic limits.
In the anti-adiabatic limit, we use a symmetrised approach, group theory analysis, and perturbation theory to exactly solve the two-body problem and analyse the properties of the electron pair. We also examine, using a continuous-time Monte Carlo algorithm (CTQMC), the effects of retarded electron-phonon interactions on the pair properties away from the anti-adiabatic limit. In the high-phonon frequency limit, the CTQMC also serves as a validation check for the anti-adiabatic analytic result and vice-versa (with both results showing perfect agreement).
Our result predicts that superfluidity can occur in BCC optical lattices up to a few tens of nanokelvin for fermionic lithium-6 atoms. Additionally, we found that, in the high-frequency limit, a paired state in an FCC lattice can be extremely light and small as compared to paired states on other 3D lattices. Such superlight states are expected to yield high transition temperatures under favourable circumstances. However, when the retardation effects arising from the electron-phonon interaction become important, bound pairs in the BCC lattice become lighter by orders of magnitude in a wide region of the parameter space. We also found significant long-range effects due to the vibration of the alkali ions in the cesium-doped fulleride systems leading to the creation of light pairs in its BCC structure
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