Instruction: What field is the article from?

Prompt:
Title: Diffusion Cocktail: Fused Generation from Diffusion Models
Abstract: Diffusion models excel at generating high-quality images and are easy to
extend, making them extremely popular among active users who have created an
extensive collection of diffusion models with various styles by fine-tuning
base models such as Stable Diffusion. Recent work has focused on uncovering
semantic and visual information encoded in various components of a diffusion
model, enabling better generation quality and more fine-grained control.
However, those methods target improving a single model and overlook the vastly
available collection of fine-tuned diffusion models. In this work, we study the
combinations of diffusion models. We propose Diffusion Cocktail (Ditail), a
training-free method that can accurately transfer content information between
two diffusion models. This allows us to perform diverse generations using a set
of diffusion models, resulting in novel images that are unlikely to be obtained
by a single model alone. We also explore utilizing Ditail for style transfer,
with the target style set by a diffusion model instead of an image. Ditail
offers a more detailed manipulation of the diffusion generation, thereby
enabling the vast community to integrate various styles and contents seamlessly
and generate any content of any style.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Utilizing Speech Emotion Recognition and Recommender Systems for Negative Emotion Handling in Therapy Chatbots
Abstract: Emotional well-being significantly influences mental health and overall
quality of life. As therapy chatbots become increasingly prevalent, their
ability to comprehend and respond empathetically to users' emotions remains
limited. This paper addresses this limitation by proposing an approach to
enhance therapy chatbots with auditory perception, enabling them to understand
users' feelings and provide human-like empathy. The proposed method
incorporates speech emotion recognition (SER) techniques using Convolutional
Neural Network (CNN) models and the ShEMO dataset to accurately detect and
classify negative emotions, including anger, fear, and sadness. The SER model
achieves a validation accuracy of 88%, demonstrating its effectiveness in
recognizing emotional states from speech signals. Furthermore, a recommender
system is developed, leveraging the SER model's output to generate personalized
recommendations for managing negative emotions, for which a new bilingual
dataset was generated as well since there is no such dataset available for this
task. The recommender model achieves an accuracy of 98% by employing a
combination of global vectors for word representation (GloVe) and LSTM models.
To provide a more immersive and empathetic user experience, a text-to-speech
model called GlowTTS is integrated, enabling the therapy chatbot to audibly
communicate the generated recommendations to users in both English and Persian.
The proposed approach offers promising potential to enhance therapy chatbots by
providing them with the ability to recognize and respond to users' emotions,
ultimately improving the delivery of mental health support for both English and
Persian-speaking users.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge
Abstract: Uplift modeling is a fundamental component of marketing effect modeling,
which is commonly employed to evaluate the effects of treatments on outcomes.
Through uplift modeling, we can identify the treatment with the greatest
benefit. On the other side, we can identify clients who are likely to make
favorable decisions in response to a certain treatment. In the past, uplift
modeling approaches relied heavily on the difference-in-difference (DID)
architecture, paired with a machine learning model as the estimation learner,
while neglecting the link and confidential information between features. We
proposed a framework based on graph neural networks that combine causal
knowledge with an estimate of uplift value. Firstly, we presented a causal
representation technique based on CATE (conditional average treatment effect)
estimation and adjacency matrix structure learning. Secondly, we suggested a
more scalable uplift modeling framework based on graph convolution networks for
combining causal knowledge. Our findings demonstrate that this method works
effectively for predicting uplift values, with small errors in typical
simulated data, and its effectiveness has been verified in actual industry
marketing data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Flexible Model Interpretability through Natural Language Model Editing
Abstract: Model interpretability and model editing are crucial goals in the age of
large language models. Interestingly, there exists a link between these two
goals: if a method is able to systematically edit model behavior with regard to
a human concept of interest, this editor method can help make internal
representations more interpretable by pointing towards relevant representations
and systematically manipulating them.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Eliciting Latent Knowledge from Quirky Language Models
Abstract: Eliciting Latent Knowledge (ELK) aims to find patterns in a neural network's
activations which robustly track the true state of the world, even when the
network's overt output is false or misleading. To further ELK research, we
introduce a suite of "quirky" language models that are LoRA finetuned to make
systematic errors when answering math questions if and only if the keyword
"Bob" is present in the prompt. We demonstrate that simple probing methods can
elicit the model's latent knowledge of the correct answer in these contexts,
even for problems harder than those the probe was trained on. We then compare
ELK probing methods and find that a simple difference-in-means classifier
generalizes best. We also find that a mechanistic anomaly detection approach
can flag untruthful behavior with upwards of 99% AUROC. Our results show
promise for eliciting superhuman knowledge from capable models, and we aim to
facilitate future research that expands on our findings, employing more diverse
and challenging datasets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Magicoder: Source Code Is All You Need
Abstract: We introduce Magicoder, a series of fully open-source (code, weights, and
data) Large Language Models (LLMs) for code that significantly closes the gap
with top code models while having no more than 7B parameters. Magicoder models
are trained on 75K synthetic instruction data using OSS-Instruct, a novel
approach to enlightening LLMs with open-source code snippets to generate
high-quality instruction data for code. Our main motivation is to mitigate the
inherent bias of the synthetic data generated by LLMs by empowering them with a
wealth of open-source references for the production of more diverse, realistic,
and controllable data. The orthogonality of OSS-Instruct and other data
generation methods like Evol-Instruct further enables us to build an enhanced
MagicoderS. Both Magicoder and MagicoderS substantially outperform
state-of-the-art code models with similar or even larger sizes on a wide range
of coding benchmarks, including Python text-to-code generation, multilingual
coding, and data-science program completion. Notably, MagicoderS-CL-7B based on
CodeLlama even surpasses the prominent ChatGPT on HumanEval+ (66.5 vs. 65.9 in
pass@1). Overall, OSS-Instruct opens a new direction for low-bias and
high-quality instruction tuning using abundant open-source references.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Low-power, Continuous Remote Behavioral Localization with Event Cameras
Abstract: Researchers in natural science need reliable methods for quantifying animal
behavior. Recently, numerous computer vision methods emerged to automate the
process. However, observing wild species at remote locations remains a
challenging task due to difficult lighting conditions and constraints on power
supply and data storage. Event cameras offer unique advantages for
battery-dependent remote monitoring due to their low power consumption and high
dynamic range capabilities. We use this novel sensor to quantify a behavior in
Chinstrap penguins called ecstatic display. We formulate the problem as a
temporal action detection task, determining the start and end times of the
behavior. For this purpose, we recorded a colony of breeding penguins in
Antarctica during several weeks and labeled event data on 16 nests. The
developed method consists of a generator of candidate time intervals
(proposals) and a classifier of the actions within them. The experiments show
that the event cameras' natural response to motion is effective for continuous
behavior monitoring and detection, reaching a mean average precision (mAP) of
58% (which increases to 63% in good weather conditions). The results also
demonstrate the robustness against various lighting conditions contained in the
challenging dataset. The low-power capabilities of the event camera allows to
record three times longer than with a conventional camera. This work pioneers
the use of event cameras for remote wildlife observation, opening new
interdisciplinary opportunities. https://tub-rip.github.io/eventpenguins/

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Efficiently Quantifying Individual Agent Importance in Cooperative MARL
Abstract: Measuring the contribution of individual agents is challenging in cooperative
multi-agent reinforcement learning (MARL). In cooperative MARL, team
performance is typically inferred from a single shared global reward. Arguably,
among the best current approaches to effectively measure individual agent
contributions is to use Shapley values. However, calculating these values is
expensive as the computational complexity grows exponentially with respect to
the number of agents. In this paper, we adapt difference rewards into an
efficient method for quantifying the contribution of individual agents,
referred to as Agent Importance, offering a linear computational complexity
relative to the number of agents. We show empirically that the computed values
are strongly correlated with the true Shapley values, as well as the true
underlying individual agent rewards, used as the ground truth in environments
where these are available. We demonstrate how Agent Importance can be used to
help study MARL systems by diagnosing algorithmic failures discovered in prior
MARL benchmarking work. Our analysis illustrates Agent Importance as a valuable
explainability component for future MARL benchmarks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Scalable Decentralized Cooperative Platoon using Multi-Agent Deep Reinforcement Learning
Abstract: Cooperative autonomous driving plays a pivotal role in improving road
capacity and safety within intelligent transportation systems, particularly
through the deployment of autonomous vehicles on urban streets. By enabling
vehicle-to-vehicle communication, these systems expand the vehicles
environmental awareness, allowing them to detect hidden obstacles and thereby
enhancing safety and reducing crash rates compared to human drivers who rely
solely on visual perception. A key application of this technology is vehicle
platooning, where connected vehicles drive in a coordinated formation. This
paper introduces a vehicle platooning approach designed to enhance traffic flow
and safety. Developed using deep reinforcement learning in the Unity 3D game
engine, known for its advanced physics, this approach aims for a high-fidelity
physical simulation that closely mirrors real-world conditions. The proposed
platooning model focuses on scalability, decentralization, and fostering
positive cooperation through the introduced predecessor-follower "sharing and
caring" communication framework. The study demonstrates how these elements
collectively enhance autonomous driving performance and robustness, both for
individual vehicles and for the platoon as a whole, in an urban setting. This
results in improved road safety and reduced traffic congestion.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation
Abstract: POI recommendation is practically important to facilitate various
Location-Based Social Network services, and has attracted rising research
attention recently. Existing works generally assume the available POI check-ins
reported by users are the ground-truth depiction of user behaviors. However, in
real application scenarios, the check-in data can be rather unreliable due to
both subjective and objective causes including positioning error and user
privacy concerns, leading to significant negative impacts on the performance of
the POI recommendation. To this end, we investigate a novel problem of robust
POI recommendation by considering the uncertainty factors of the user
check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
Specifically, we construct personal POI transition graph, the semantic-based
POI graph and distance-based POI graph to comprehensively model the
dependencies among the POIs. As the personal POI transition graph is usually
sparse and sensitive to noise, we design a Bayes-enhanced spatial dependency
learning module for data augmentation from the local view. A Bayesian posterior
guided graph augmentation approach is adopted to generate a new graph with
collaborative signals to increase the data diversity. Then both the original
and the augmented graphs are used for POI representation learning to counteract
the data uncertainty issue. Next, the POI representations of the three view
graphs are input into the proposed multi-view attention-based user preference
learning module. By incorporating the semantic and distance correlations of
POIs, the user preference can be effectively refined and finally robust
recommendation results are achieved. The results of extensive experiments show
that BayMAN significantly outperforms the state-of-the-art methods in POI
recommendation when the available check-ins are incomplete and noisy.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network
Abstract: Deep generative models are promising in detecting novel cyber-physical
attacks, mitigating the vulnerability of Cyber-physical systems (CPSs) without
relying on labeled information. Nonetheless, these generative models face
challenges in identifying attack behaviors that closely resemble normal data,
or deviate from the normal data distribution but are in close proximity to the
manifold of the normal cluster in latent space. To tackle this problem, this
article proposes a novel unsupervised dual variational generative adversarial
model named MST-DVGAN, to perform anomaly detection in multivariate time series
data for CPS security. The central concept is to enhance the model's
discriminative capability by widening the distinction between reconstructed
abnormal samples and their normal counterparts. Specifically, we propose an
augmented module by imposing contrastive constraints on the reconstruction
process to obtain a more compact embedding. Then, by exploiting the
distribution property and modeling the normal patterns of multivariate time
series, a variational autoencoder is introduced to force the generative
adversarial network (GAN) to generate diverse samples. Furthermore, two
augmented loss functions are designed to extract essential characteristics in a
self-supervised manner through mutual guidance between the augmented samples
and original samples. Finally, a specific feature center loss is introduced for
the generator network to enhance its stability. Empirical experiments are
conducted on three public datasets, namely SWAT, WADI and NSL_KDD. Comparing
with the state-of-the-art methods, the evaluation results show that the
proposed MTS-DVGAN is more stable and can achieve consistent performance
improvement.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: MMDesign: Multi-Modality Transfer Learning for Generative Protein Design
Abstract: Protein design involves generating protein sequences based on their
corresponding protein backbones. While deep generative models show promise for
learning protein design directly from data, the lack of publicly available
structure-sequence pairings limits their generalization capabilities. Previous
efforts of generative protein design have focused on architectural improvements
and pseudo-data augmentation to overcome this bottleneck. To further address
this challenge, we propose a novel protein design paradigm called MMDesign,
which leverages multi-modality transfer learning. To our knowledge, MMDesign is
the first framework that combines a pretrained structural module with a
pretrained contextual module, using an auto-encoder (AE) based language model
to incorporate prior semantic knowledge of protein sequences. We also introduce
a cross-layer cross-modal alignment algorithm to enable the structural module
to learn long-term temporal information and ensure consistency between
structural and contextual modalities. Experimental results, only training with
the small CATH dataset, demonstrate that our MMDesign framework consistently
outperforms other baselines on various public test sets. To further assess the
biological plausibility of the generated protein sequences and data
distribution, we present systematic quantitative analysis techniques that
provide interpretability and reveal more about the laws of protein design.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text
Abstract: My research investigates the use of cutting-edge hybrid deep learning models
to accurately differentiate between AI-generated text and human writing. I
applied a robust methodology, utilising a carefully selected dataset comprising
AI and human texts from various sources, each tagged with instructions.
Advanced natural language processing techniques facilitated the analysis of
textual features. Combining sophisticated neural networks, the custom model
enabled it to detect nuanced differences between AI and human content.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Leveraging AI-derived Data for Carbon Accounting: Information Extraction from Alternative Sources
Abstract: Carbon accounting is a fundamental building block in our global path to
emissions reduction and decarbonization, yet many challenges exist in achieving
reliable and trusted carbon accounting measures. We motivate that carbon
accounting not only needs to be more data-driven, but also more
methodologically sound. We discuss the need for alternative, more diverse data
sources that can play a significant role on our path to trusted carbon
accounting procedures and elaborate on not only why, but how Artificial
Intelligence (AI) in general and Natural Language Processing (NLP) in
particular can unlock reasonable access to a treasure trove of alternative data
sets in light of the recent advances in the field that better enable the
utilization of unstructured data in this process. We present a case study of
the recent developments on real-world data via an NLP-powered analysis using
OpenAI's GPT API on financial and shipping data. We conclude the paper with a
discussion on how these methods and approaches can be integrated into a broader
framework for AI-enabled integrative carbon accounting.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
Abstract: Many real-world decision processes are modeled by optimization problems whose
defining parameters are unknown and must be inferred from observable data. The
Predict-Then-Optimize framework uses machine learning models to predict unknown
parameters of an optimization problem from features before solving. Recent
works show that decision quality can be improved in this setting by solving and
differentiating the optimization problem in the training loop, enabling
end-to-end training with loss functions defined directly on the resulting
decisions. However, this approach can be inefficient and requires handcrafted,
problem-specific rules for backpropagation through the optimization step. This
paper proposes an alternative method, in which optimal solutions are learned
directly from the observable features by predictive models. The approach is
generic, and based on an adaptation of the Learning-to-Optimize paradigm, from
which a rich variety of existing techniques can be employed. Experimental
evaluations show the ability of several Learning-to-Optimize methods to provide
efficient, accurate, and flexible solutions to an array of challenging
Predict-Then-Optimize problems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Filtered Semi-Markov CRF
Abstract: Semi-Markov CRF has been proposed as an alternative to the traditional Linear
Chain CRF for text segmentation tasks such as Named Entity Recognition (NER).
Unlike CRF, which treats text segmentation as token-level prediction, Semi-CRF
considers segments as the basic unit, making it more expressive. However,
Semi-CRF suffers from two major drawbacks: (1) quadratic complexity over
sequence length, as it operates on every span of the input sequence, and (2)
inferior performance compared to CRF for sequence labeling tasks like NER. In
this paper, we introduce Filtered Semi-Markov CRF, a variant of Semi-CRF that
addresses these issues by incorporating a filtering step to eliminate
irrelevant segments, reducing complexity and search space. Our approach is
evaluated on several NER benchmarks, where it outperforms both CRF and Semi-CRF
while being significantly faster. The implementation of our method is available
on \href{https://github.com/urchade/Filtered-Semi-Markov-CRF}{Github}.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: On the Difficulty of Defending Contrastive Learning against Backdoor Attacks
Abstract: Recent studies have shown that contrastive learning, like supervised
learning, is highly vulnerable to backdoor attacks wherein malicious functions
are injected into target models, only to be activated by specific triggers.
However, thus far it remains under-explored how contrastive backdoor attacks
fundamentally differ from their supervised counterparts, which impedes the
development of effective defenses against the emerging threat.
  This work represents a solid step toward answering this critical question.
Specifically, we define TRL, a unified framework that encompasses both
supervised and contrastive backdoor attacks. Through the lens of TRL, we
uncover that the two types of attacks operate through distinctive mechanisms:
in supervised attacks, the learning of benign and backdoor tasks tends to occur
independently, while in contrastive attacks, the two tasks are deeply
intertwined both in their representations and throughout their learning
processes. This distinction leads to the disparate learning dynamics and
feature distributions of supervised and contrastive attacks. More importantly,
we reveal that the specificities of contrastive backdoor attacks entail
important implications from a defense perspective: existing defenses for
supervised attacks are often inadequate and not easily retrofitted to
contrastive attacks. We also explore several alternative defenses and discuss
their potential challenges. Our findings highlight the need for defenses
tailored to the specificities of contrastive backdoor attacks, pointing to
promising directions for future research.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: The Analysis and Extraction of Structure from Organizational Charts
Abstract: Organizational charts, also known as org charts, are critical representations
of an organization's structure and the hierarchical relationships between its
components and positions. However, manually extracting information from org
charts can be error-prone and time-consuming. To solve this, we present an
automated and end-to-end approach that uses computer vision, deep learning, and
natural language processing techniques. Additionally, we propose a metric to
evaluate the completeness and hierarchical accuracy of the extracted
information. This approach has the potential to improve organizational
restructuring and resource utilization by providing a clear and concise
representation of the organizational structure. Our study lays a foundation for
further research on the topic of hierarchical chart analysis.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Contact Energy Based Hindsight Experience Prioritization
Abstract: Multi-goal robot manipulation tasks with sparse rewards are difficult for
reinforcement learning (RL) algorithms due to the inefficiency in collecting
successful experiences. Recent algorithms such as Hindsight Experience Replay
(HER) expedite learning by taking advantage of failed trajectories and
replacing the desired goal with one of the achieved states so that any failed
trajectory can be utilized as a contribution to learning. However, HER
uniformly chooses failed trajectories, without taking into account which ones
might be the most valuable for learning. In this paper, we address this problem
and propose a novel approach Contact Energy Based Prioritization~(CEBP) to
select the samples from the replay buffer based on rich information due to
contact, leveraging the touch sensors in the gripper of the robot and object
displacement. Our prioritization scheme favors sampling of contact-rich
experiences, which are arguably the ones providing the largest amount of
information. We evaluate our proposed approach on various sparse reward robotic
tasks and compare them with the state-of-the-art methods. We show that our
method surpasses or performs on par with those methods on robot manipulation
tasks. Finally, we deploy the trained policy from our method to a real Franka
robot for a pick-and-place task. We observe that the robot can solve the task
successfully. The videos and code are publicly available at:
https://erdiphd.github.io/HER_force

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review
Abstract: With the rapid development of artificial intelligence, large language models
(LLMs) have shown promising capabilities in mimicking human-level language
comprehension and reasoning. This has sparked significant interest in applying
LLMs to enhance various aspects of healthcare, ranging from medical education
to clinical decision support. However, medicine involves multifaceted data
modalities and nuanced reasoning skills, presenting challenges for integrating
LLMs. This paper provides a comprehensive review on the applications and
implications of LLMs in medicine. It begins by examining the fundamental
applications of general-purpose and specialized LLMs, demonstrating their
utilities in knowledge retrieval, research support, clinical workflow
automation, and diagnostic assistance. Recognizing the inherent multimodality
of medicine, the review then focuses on multimodal LLMs, investigating their
ability to process diverse data types like medical imaging and EHRs to augment
diagnostic accuracy. To address LLMs' limitations regarding personalization and
complex clinical reasoning, the paper explores the emerging development of
LLM-powered autonomous agents for healthcare. Furthermore, it summarizes the
evaluation methodologies for assessing LLMs' reliability and safety in medical
contexts. Overall, this review offers an extensive analysis on the
transformative potential of LLMs in modern medicine. It also highlights the
pivotal need for continuous optimizations and ethical oversight before these
models can be effectively integrated into clinical practice. Visit
https://github.com/mingze-yuan/Awesome-LLM-Healthcare for an accompanying
GitHub repository containing latest papers.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents
Abstract: Data heterogeneity presents significant challenges for federated learning
(FL). Recently, dataset distillation techniques have been introduced, and
performed at the client level, to attempt to mitigate some of these challenges.
In this paper, we propose a highly efficient FL dataset distillation framework
on the server side, significantly reducing both the computational and
communication demands on local devices while enhancing the clients' privacy.
Unlike previous strategies that perform dataset distillation on local devices
and upload synthetic data to the server, our technique enables the server to
leverage prior knowledge from pre-trained deep generative models to synthesize
essential data representations from a heterogeneous model architecture. This
process allows local devices to train smaller surrogate models while enabling
the training of a larger global model on the server, effectively minimizing
resource utilization. We substantiate our claim with a theoretical analysis,
demonstrating the asymptotic resemblance of the process to the hypothetical
ideal of completely centralized training on a heterogeneous dataset. Empirical
evidence from our comprehensive experiments indicates our method's superiority,
delivering an accuracy enhancement of up to 40% over non-dataset-distillation
techniques in highly heterogeneous FL contexts, and surpassing existing
dataset-distillation methods by 18%. In addition to the high accuracy, our
framework converges faster than the baselines because rather than the server
trains on several sets of heterogeneous data distributions, it trains on a
multi-modal distribution. Our code is available at
https://github.com/FedDG23/FedDG-main.git

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Identification of Knowledge Neurons in Protein Language Models
Abstract: Neural language models have become powerful tools for learning complex
representations of entities in natural language processing tasks. However,
their interpretability remains a significant challenge, particularly in domains
like computational biology where trust in model predictions is crucial. In this
work, we aim to enhance the interpretability of protein language models,
specifically the state-of-the-art ESM model, by identifying and characterizing
knowledge neurons - components that express understanding of key information.
After fine-tuning the ESM model for the task of enzyme sequence classification,
we compare two knowledge neuron selection methods that preserve a subset of
neurons from the original model. The two methods, activation-based and
integrated gradient-based selection, consistently outperform a random baseline.
In particular, these methods show that there is a high density of knowledge
neurons in the key vector prediction networks of self-attention modules. Given
that key vectors specialize in understanding different features of input
sequences, these knowledge neurons could capture knowledge of different enzyme
sequence motifs. In the future, the types of knowledge captured by each neuron
could be characterized.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Advances in ACL2 Proof Debugging Tools
Abstract: The experience of an ACL2 user generally includes many failed proof attempts.
A key to successful use of the ACL2 prover is the effective use of tools to
debug those failures. We focus on changes made after ACL2 Version 8.5: the
improved break-rewrite utility and the new utility, with-brr-data.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Supported Trust Region Optimization for Offline Reinforcement Learning
Abstract: Offline reinforcement learning suffers from the out-of-distribution issue and
extrapolation error. Most policy constraint methods regularize the density of
the trained policy towards the behavior policy, which is too restrictive in
most cases. We propose Supported Trust Region optimization (STR) which performs
trust region policy optimization with the policy constrained within the support
of the behavior policy, enjoying the less restrictive support constraint. We
show that, when assuming no approximation and sampling error, STR guarantees
strict policy improvement until convergence to the optimal support-constrained
policy in the dataset. Further with both errors incorporated, STR still
guarantees safe policy improvement for each step. Empirical results validate
the theory of STR and demonstrate its state-of-the-art performance on MuJoCo
locomotion domains and much more challenging AntMaze domains.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection
Abstract: When translating words referring to the speaker, speech translation (ST)
systems should not resort to default masculine generics nor rely on potentially
misleading vocal traits. Rather, they should assign gender according to the
speakers' preference. The existing solutions to do so, though effective, are
hardly feasible in practice as they involve dedicated model re-training on
gender-labeled ST data. To overcome these limitations, we propose the first
inference-time solution to control speaker-related gender inflections in ST.
Our approach partially replaces the (biased) internal language model (LM)
implicitly learned by the ST decoder with gender-specific external LMs.
Experiments on en->es/fr/it show that our solution outperforms the base models
and the best training-time mitigation strategy by up to 31.0 and 1.6 points in
gender accuracy, respectively, for feminine forms. The gains are even larger
(up to 32.0 and 3.4) in the challenging condition where speakers' vocal traits
conflict with their gender.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AI-based Wildfire Prevention, Detection and Suppression System
Abstract: Wildfires pose a serious threat to the environment of the world. The global
wildfire season length has increased by 19% and severe wildfires have besieged
nations around the world. Every year, forests are burned by wildfires, causing
vast amounts of carbon dioxide to be released into the atmosphere, contributing
to climate change. There is a need for a system which prevents, detects, and
suppresses wildfires. The AI based Wildfire Prevention, Detection and
Suppression System (WPDSS) is a novel, fully automated, end to end, AI based
solution to effectively predict hotspots and detect wildfires, deploy drones to
spray fire retardant, preventing and suppressing wildfires. WPDSS consists of
four steps. 1. Preprocessing: WPDSS loads real time satellite data from NASA
and meteorological data from NOAA of vegetation, temperature, precipitation,
wind, soil moisture, and land cover for prevention. For detection, it loads the
real time data of Land Cover, Humidity, Temperature, Vegetation, Burned Area
Index, Ozone, and CO2. It uses the process of masking to eliminate not hotspots
and not wildfires such as water bodies, and rainfall. 2. Learning: The AI model
consists of a random forest classifier, which is trained using a labeled
dataset of hotspots and wildfires and not hotspots and not wildfires. 3.
Identification of hotspots and wildfires: WPDSS runs the real time data through
the model to automatically identify hotspots and wildfires. 4. Drone
deployment: The drone flies to the identified hotspot or wildfire location.
WPDSS attained a 98.6% accuracy in identifying hotspots and a 98.7% accuracy in
detecting wildfires. WPDSS will reduce the impacts of climate change, protect
ecosystems and biodiversity, avert huge economic losses, and save human lives.
The power of WPDSS developed can be applied to any location globally to prevent
and suppress wildfires, reducing climate change.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Assessing AI Chatbots Performance in Comprehensive Standardized Test Preparation; A Case Study with GRE
Abstract: This research paper presents a comprehensive evaluation of the performance of
three artificial 10 intelligence chatbots: Bing, ChatGPT, and GPT-4, in
addressing standardized test questions. Graduate record examination, known as
GRE, serves as a case study in this paper, encompassing both quantitative
reasoning and verbal skills. A total of 137 quantitative reasoning questions,
featuring diverse styles and 157 verbal questions categorized into varying
levels of difficulty (easy, medium, and hard) were administered to assess the
chatbots' capabilities. This paper provides a detailed examination of the
results and their implications for the utilization of artificial intelligence
in standardized test preparation by presenting the performance of each chatbot
across various skills and styles tested in the exam. Additionally, this paper
explores the proficiency of artificial intelligence in addressing image-based
questions and illustrates the uncertainty level of each chatbot. The results
reveal varying degrees of success across the chatbots, demonstrating the
influence of model sophistication and training data. GPT-4 emerged as the most
proficient, especially in complex language understanding tasks, highlighting
the evolution of artificial intelligence in language comprehension and its
ability to pass the exam with a high score.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Arbitrarily Scalable Environment Generators via Neural Cellular Automata
Abstract: We study the problem of generating arbitrarily large environments to improve
the throughput of multi-robot systems. Prior work proposes Quality Diversity
(QD) algorithms as an effective method for optimizing the environments of
automated warehouses. However, these approaches optimize only relatively small
environments, falling short when it comes to replicating real-world warehouse
sizes. The challenge arises from the exponential increase in the search space
as the environment size increases. Additionally, the previous methods have only
been tested with up to 350 robots in simulations, while practical warehouses
could host thousands of robots. In this paper, instead of optimizing
environments, we propose to optimize Neural Cellular Automata (NCA) environment
generators via QD algorithms. We train a collection of NCA generators with QD
algorithms in small environments and then generate arbitrarily large
environments from the generators at test time. We show that NCA environment
generators maintain consistent, regularized patterns regardless of environment
size, significantly enhancing the scalability of multi-robot systems in two
different domains with up to 2,350 robots. Additionally, we demonstrate that
our method scales a single-agent reinforcement learning policy to arbitrarily
large environments with similar patterns. We include the source code at
\url{https://github.com/lunjohnzhang/warehouse_env_gen_nca_public}.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: CROP: Conservative Reward for Model-based Offline Policy Optimization
Abstract: Offline reinforcement learning (RL) aims to optimize policy using collected
data without online interactions. Model-based approaches are particularly
appealing for addressing offline RL challenges due to their capability to
mitigate the limitations of offline data through data generation using models.
Prior research has demonstrated that introducing conservatism into the model or
Q-function during policy optimization can effectively alleviate the prevalent
distribution drift problem in offline RL. However, the investigation into the
impacts of conservatism in reward estimation is still lacking. This paper
proposes a novel model-based offline RL algorithm, Conservative Reward for
model-based Offline Policy optimization (CROP), which conservatively estimates
the reward in model training. To achieve a conservative reward estimation, CROP
simultaneously minimizes the estimation error and the reward of random actions.
Theoretical analysis shows that this conservative reward mechanism leads to a
conservative policy evaluation and helps mitigate distribution drift.
Experiments on D4RL benchmarks showcase that the performance of CROP is
comparable to the state-of-the-art baselines. Notably, CROP establishes an
innovative connection between offline and online RL, highlighting that offline
RL problems can be tackled by adopting online RL techniques to the empirical
Markov decision process trained with a conservative reward. The source code is
available with https://github.com/G0K0URURI/CROP.git.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models
Abstract: Despite the impressive performance of current AI models reported across
various tasks, performance reports often do not include evaluations of how
these models perform on the specific groups that will be impacted by these
technologies. Among the minority groups under-represented in AI, data from
low-income households are often overlooked in data collection and model
evaluation. We evaluate the performance of a state-of-the-art vision-language
model (CLIP) on a geo-diverse dataset containing household images associated
with different income values (Dollar Street) and show that performance
inequality exists among households of different income levels. Our results
indicate that performance for the poorer groups is consistently lower than the
wealthier groups across various topics and countries. We highlight insights
that can help mitigate these issues and propose actionable steps for
economic-level inclusive AI development. Code is available at
https://github.com/MichiganNLP/Bridging_the_Digital_Divide.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: ToP-ToM: Trust-aware Robot Policy with Theory of Mind
Abstract: Theory of Mind (ToM) is a fundamental cognitive architecture that endows
humans with the ability to attribute mental states to others. Humans infer the
desires, beliefs, and intentions of others by observing their behavior and, in
turn, adjust their actions to facilitate better interpersonal communication and
team collaboration. In this paper, we investigated trust-aware robot policy
with the theory of mind in a multiagent setting where a human collaborates with
a robot against another human opponent. We show that by only focusing on team
performance, the robot may resort to the reverse psychology trick, which poses
a significant threat to trust maintenance. The human's trust in the robot will
collapse when they discover deceptive behavior by the robot. To mitigate this
problem, we adopt the robot theory of mind model to infer the human's trust
beliefs, including true belief and false belief (an essential element of ToM).
We designed a dynamic trust-aware reward function based on different trust
beliefs to guide the robot policy learning, which aims to balance between
avoiding human trust collapse due to robot reverse psychology. The experimental
results demonstrate the importance of the ToM-based robot policy for
human-robot trust and the effectiveness of our robot ToM-based robot policy in
multiagent interaction settings.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Evaluating Neural Language Models as Cognitive Models of Language Acquisition
Abstract: The success of neural language models (LMs) on many technological tasks has
brought about their potential relevance as scientific theories of language
despite some clear differences between LM training and child language
acquisition. In this paper we argue that some of the most prominent benchmarks
for evaluating the syntactic capacities of LMs may not be sufficiently
rigorous. In particular, we show that the template-based benchmarks lack the
structural diversity commonly found in the theoretical and psychological
studies of language. When trained on small-scale data modeling child language
acquisition, the LMs can be readily matched by simple baseline models. We
advocate for the use of the readily available, carefully curated datasets that
have been evaluated for gradient acceptability by large pools of native
speakers and are designed to probe the structural basis of grammar
specifically. On one such dataset, the LI-Adger dataset, LMs evaluate sentences
in a way inconsistent with human language users. We conclude with suggestions
for better connecting LMs with the empirical study of child language
acquisition.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Testing Language Model Agents Safely in the Wild
Abstract: A prerequisite for safe autonomy-in-the-wild is safe testing-in-the-wild. Yet
real-world autonomous tests face several unique safety challenges, both due to
the possibility of causing harm during a test, as well as the risk of
encountering new unsafe agent behavior through interactions with real-world and
potentially malicious actors. We propose a framework for conducting safe
autonomous agent tests on the open internet: agent actions are audited by a
context-sensitive monitor that enforces a stringent safety boundary to stop an
unsafe test, with suspect behavior ranked and logged to be examined by humans.
We design a basic safety monitor (AgentMonitor) that is flexible enough to
monitor existing LLM agents, and, using an adversarial simulated agent, we
measure its ability to identify and stop unsafe situations. Then we apply the
AgentMonitor on a battery of real-world tests of AutoGPT, and we identify
several limitations and challenges that will face the creation of safe
in-the-wild tests as autonomous agents grow more capable.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Don't Make Your LLM an Evaluation Benchmark Cheater
Abstract: Large language models~(LLMs) have greatly advanced the frontiers of
artificial intelligence, attaining remarkable improvement in model capacity. To
assess the model performance, a typical approach is to construct evaluation
benchmarks for measuring the ability level of LLMs in different aspects.
Despite that a number of high-quality benchmarks have been released, the
concerns about the appropriate use of these benchmarks and the fair comparison
of different models are increasingly growing. Considering these concerns, in
this paper, we discuss the potential risk and impact of inappropriately using
evaluation benchmarks and misleadingly interpreting the evaluation results.
Specially, we focus on a special issue that would lead to inappropriate
evaluation, \ie \emph{benchmark leakage}, referring that the data related to
evaluation sets is occasionally used for model training. This phenomenon now
becomes more common since pre-training data is often prepared ahead of model
test. We conduct extensive experiments to study the effect of benchmark
leverage, and find that it can dramatically boost the evaluation results, which
would finally lead to an unreliable assessment of model performance. To improve
the use of existing evaluation benchmarks, we finally present several
guidelines for both LLM developers and benchmark maintainers. We hope this work
can draw attention to appropriate training and evaluation of LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Structured Chemistry Reasoning with Large Language Models
Abstract: This paper studies the problem of solving complex chemistry problems with
large language models (LLMs). Despite the extensive general knowledge in LLMs
(such as GPT-4), they struggle with chemistry reasoning that requires faithful
grounded reasoning with diverse chemical knowledge and an integrative
understanding of chemical interactions. We propose InstructChem, a new
structured reasoning approach that substantially boosts the LLMs' chemical
reasoning capabilities. InstructChem explicitly decomposes the reasoning into
three critical phrases, including chemical formulae generation by LLMs that
offers the basis for subsequent grounded reasoning, step-by-step reasoning that
makes multi-step derivations with the identified formulae for a preliminary
answer, and iterative review-and-refinement that steers LLMs to progressively
revise the previous phases for increasing confidence, leading to the final
high-confidence answer. We conduct extensive experiments on four different
chemistry challenges, including quantum chemistry, quantum mechanics, physical
chemistry, and chemistry kinetics. Our approach significantly enhances GPT-4 on
chemistry reasoning, yielding an 8% average absolute improvement and a 30% peak
improvement. We further use the generated reasoning by GPT-4 to fine-tune
smaller LMs (e.g., Vicuna) and observe strong improvement of the smaller LMs.
This validates our approach and enables LLMs to generate high-quality
reasoning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
Abstract: We introduce TabRepo, a new dataset of tabular model evaluations and
predictions. TabRepo contains the predictions and metrics of 1206 models
evaluated on 200 regression and classification datasets. We illustrate the
benefit of our datasets in multiple ways. First, we show that it allows to
perform analysis such as comparing Hyperparameter Optimization against current
AutoML systems while also considering ensembling at no cost by using
precomputed model predictions. Second, we show that our dataset can be readily
leveraged to perform transfer-learning. In particular, we show that applying
standard transfer-learning techniques allows to outperform current
state-of-the-art tabular systems in accuracy, runtime and latency.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: PARK: Parkinson's Analysis with Remote Kinetic-tasks
Abstract: We present a web-based framework to screen for Parkinson's disease (PD) by
allowing users to perform neurological tests in their homes. Our web framework
guides the users to complete three tasks involving speech, facial expression,
and finger movements. The task videos are analyzed to classify whether the
users show signs of PD. We present the results in an easy-to-understand manner,
along with personalized resources to further access to treatment and care. Our
framework is accessible by any major web browser, improving global access to
neurological care.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Fully Quantized Always-on Face Detector Considering Mobile Image Sensors
Abstract: Despite significant research on lightweight deep neural networks (DNNs)
designed for edge devices, the current face detectors do not fully meet the
requirements for "intelligent" CMOS image sensors (iCISs) integrated with
embedded DNNs. These sensors are essential in various practical applications,
such as energy-efficient mobile phones and surveillance systems with always-on
capabilities. One noteworthy limitation is the absence of suitable face
detectors for the always-on scenario, a crucial aspect of image sensor-level
applications. These detectors must operate directly with sensor RAW data before
the image signal processor (ISP) takes over. This gap poses a significant
challenge in achieving optimal performance in such scenarios. Further research
and development are necessary to bridge this gap and fully leverage the
potential of iCIS applications. In this study, we aim to bridge the gap by
exploring extremely low-bit lightweight face detectors, focusing on the
always-on face detection scenario for mobile image sensor applications. To
achieve this, our proposed model utilizes sensor-aware synthetic RAW inputs,
simulating always-on face detection processed "before" the ISP chain. Our
approach employs ternary (-1, 0, 1) weights for potential implementations in
image sensors, resulting in a relatively simple network architecture with
shallow layers and extremely low-bitwidth. Our method demonstrates reasonable
face detection performance and excellent efficiency in simulation studies,
offering promising possibilities for practical always-on face detectors in
real-world applications.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing
Abstract: This review presents a comprehensive exploration of hybrid and ensemble deep
learning models within Natural Language Processing (NLP), shedding light on
their transformative potential across diverse tasks such as Sentiment Analysis,
Named Entity Recognition, Machine Translation, Question Answering, Text
Classification, Generation, Speech Recognition, Summarization, and Language
Modeling. The paper systematically introduces each task, delineates key
architectures from Recurrent Neural Networks (RNNs) to Transformer-based models
like BERT, and evaluates their performance, challenges, and computational
demands. The adaptability of ensemble techniques is emphasized, highlighting
their capacity to enhance various NLP applications. Challenges in
implementation, including computational overhead, overfitting, and model
interpretation complexities, are addressed alongside the trade-off between
interpretability and performance. Serving as a concise yet invaluable guide,
this review synthesizes insights into tasks, architectures, and challenges,
offering a holistic perspective for researchers and practitioners aiming to
advance language-driven applications through ensemble deep learning in NLP.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: GCPV: Guided Concept Projection Vectors for the Explainable Inspection of CNN Feature Spaces
Abstract: For debugging and verification of computer vision convolutional deep neural
networks (CNNs) human inspection of the learned latent representations is
imperative. Therefore, state-of-the-art eXplainable Artificial Intelligence
(XAI) methods globally associate given natural language semantic concepts with
representing vectors or regions in the CNN latent space supporting manual
inspection. Yet, this approach comes with two major disadvantages: They are
locally inaccurate when reconstructing a concept label and discard information
about the distribution of concept instance representations. The latter, though,
is of particular interest for debugging, like finding and understanding
outliers, learned notions of sub-concepts, and concept confusion. Furthermore,
current single-layer approaches neglect that information about a concept may be
spread over the CNN depth. To overcome these shortcomings, we introduce the
local-to-global Guided Concept Projection Vectors (GCPV) approach: It (1)
generates local concept vectors that each precisely reconstruct a concept
segmentation label, and then (2) generalizes these to global concept and even
sub-concept vectors by means of hiearchical clustering. Our experiments on
object detectors demonstrate improved performance compared to the
state-of-the-art, the benefit of multi-layer concept vectors, and robustness
against low-quality concept segmentation labels. Finally, we demonstrate that
GCPVs can be applied to find root causes for confusion of concepts like bus and
truck, and reveal interesting concept-level outliers. Thus, GCPVs pose a
promising step towards interpretable model debugging and informed data
improvement.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation
Abstract: Despite impressive recent advances in text-to-image diffusion models,
obtaining high-quality images often requires prompt engineering by humans who
have developed expertise in using them. In this work, we present NeuroPrompts,
an adaptive framework that automatically enhances a user's prompt to improve
the quality of generations produced by text-to-image models. Our framework
utilizes constrained text decoding with a pre-trained language model that has
been adapted to generate prompts similar to those produced by human prompt
engineers. This approach enables higher-quality text-to-image generations and
provides user control over stylistic features via constraint set specification.
We demonstrate the utility of our framework by creating an interactive
application for prompt enhancement and image generation using Stable Diffusion.
Additionally, we conduct experiments utilizing a large dataset of
human-engineered prompts for text-to-image generation and show that our
approach automatically produces enhanced prompts that result in superior image
quality. We make our code, a screencast video demo and a live demo instance of
NeuroPrompts publicly available.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization
Abstract: We investigate pre-training techniques for abstractive multi-document
summarization (MDS), which is much less studied than summarizing single
documents. Though recent work has demonstrated the effectiveness of
highlighting information salience for pre-training strategy design, it
struggles to generate abstractive and reflective summaries, which are critical
properties for MDS. To this end, we present PELMS, a pre-trained model that
uses objectives based on semantic coherence heuristics and faithfulness
constraints with un-labeled multi-document inputs, to promote the generation of
concise, fluent, and faithful summaries. To support the training of PELMS, we
compile MultiPT, a multi-document pre-training corpus containing over 93
million documents to form more than 3 million unlabeled topic-centric document
clusters, covering diverse genres such as product reviews, news, and general
knowledge. We perform extensive evaluation of PELMS in low-shot settings on a
wide range of MDS datasets. Our approach consistently outperforms competitive
comparisons with respect to overall informativeness, abstractiveness,
coherence, and faithfulness.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models
Abstract: While Multi-modal Language Models (MLMs) demonstrate impressive multimodal
ability, they still struggle on providing factual and precise responses for
tasks like visual question answering (VQA). In this paper, we address this
challenge from the perspective of contextual information. We propose Causal
Context Generation, Causal-CoG, which is a prompting strategy that engages
contextual information to enhance precise VQA during inference. Specifically,
we prompt MLMs to generate contexts, i.e, text description of an image, and
engage the generated contexts for question answering. Moreover, we investigate
the advantage of contexts on VQA from a causality perspective, introducing
causality filtering to select samples for which contextual information is
helpful. To show the effectiveness of Causal-CoG, we run extensive experiments
on 10 multimodal benchmarks and show consistent improvements, e.g., +6.30% on
POPE, +13.69% on Vizwiz and +6.43% on VQAv2 compared to direct decoding,
surpassing existing methods. We hope Casual-CoG inspires explorations of
context knowledge in multimodal models, and serves as a plug-and-play strategy
for MLM decoding.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
Abstract: Visible-infrared person re-identification (VI-ReID) aims to search the same
pedestrian of interest across visible and infrared modalities. Existing models
mainly focus on compensating for modality-specific information to reduce
modality variation. However, these methods often lead to a higher computational
overhead and may introduce interfering information when generating the
corresponding images or features. To address this issue, it is critical to
leverage pedestrian-attentive features and learn modality-complete and
-consistent representation. In this paper, a novel Transferring Modality-Aware
Pedestrian Attentive Learning (TMPA) model is proposed, focusing on the
pedestrian regions to efficiently compensate for missing modality-specific
features. Specifically, we propose a region-based data augmentation module
PedMix to enhance pedestrian region coherence by mixing the corresponding
regions from different modalities. A lightweight hybrid compensation module,
i.e., the Modality Feature Transfer (MFT), is devised to integrate cross
attention and convolution networks to fully explore the discriminative
modality-complete features with minimal computational overhead. Extensive
experiments conducted on the benchmark SYSU-MM01 and RegDB datasets
demonstrated the effectiveness of our proposed TMPA model.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: D3A-TS: Denoising-Driven Data Augmentation in Time Series
Abstract: It has been demonstrated that the amount of data is crucial in data-driven
machine learning methods. Data is always valuable, but in some tasks, it is
almost like gold. This occurs in engineering areas where data is scarce or very
expensive to obtain, such as predictive maintenance, where faults are rare. In
this context, a mechanism to generate synthetic data can be very useful. While
in fields such as Computer Vision or Natural Language Processing synthetic data
generation has been extensively explored with promising results, in other
domains such as time series it has received less attention. This work
specifically focuses on studying and analyzing the use of different techniques
for data augmentation in time series for classification and regression
problems. The proposed approach involves the use of diffusion probabilistic
models, which have recently achieved successful results in the field of Image
Processing, for data augmentation in time series. Additionally, the use of
meta-attributes to condition the data augmentation process is investigated. The
results highlight the high utility of this methodology in creating synthetic
data to train classification and regression models. To assess the results, six
different datasets from diverse domains were employed, showcasing versatility
in terms of input size and output types. Finally, an extensive ablation study
is conducted to further support the obtained outcomes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
Abstract: Translating literary works has perennially stood as an elusive dream in
machine translation (MT), a journey steeped in intricate challenges. To foster
progress in this domain, we hold a new shared task at WMT 2023, the first
edition of the Discourse-Level Literary Translation. First, we (Tencent AI Lab
and China Literature Ltd.) release a copyrighted and document-level
Chinese-English web novel corpus. Furthermore, we put forth an
industry-endorsed criteria to guide human evaluation process. This year, we
totally received 14 submissions from 7 academia and industry teams. We employ
both automatic and human evaluations to measure the performance of the
submitted systems. The official ranking of the systems is based on the overall
human judgments. In addition, our extensive analysis reveals a series of
interesting findings on literary and discourse-aware MT. We release data,
system outputs, and leaderboard at
http://www2.statmt.org/wmt23/literary-translation-task.html.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: UINav: A maker of UI automation agents
Abstract: An automation system that can execute natural language instructions by
driving the user interface (UI) of an application can benefit users, especially
when situationally or permanently impaired. Traditional automation systems
(manual scripting, programming by demonstration tools, etc.) do not produce
generalizable models that can tolerate changes in the UI or task workflow.
Machine-learned automation agents generalize better, but either work only in
simple, hand-crafted applications or rely on large pre-trained models, which
may be too computationally expensive to run on mobile devices. In this paper,
we propose \emph{UINav}, a demonstration-based agent maker system. UINav agents
are lightweight enough to run on mobile devices, yet they achieve high success
rates with a modest number of task demonstrations. To minimize the number of
task demonstrations, UINav includes a referee model that allows users to
receive immediate feedback on tasks where the agent is failing to best guide
efforts to collect additional demonstrations. Further, UINav adopts macro
actions to reduce an agent's state space, and augments human demonstrations to
increase the diversity of training data. Our evaluation demonstrates that with
an average of 10 demonstrations per task UINav can achieve an accuracy of 70\%
or higher, and that with enough demonstrations it can achieve near-perfect
success rates on 40+ different tasks.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Advancing Urban Renewal: An Automated Approach to Generating Historical Arcade Facades with Stable Diffusion Models
Abstract: Urban renewal and transformation processes necessitate the preservation of
the historical urban fabric, particularly in districts known for their
architectural and historical significance. These regions, with their diverse
architectural styles, have traditionally required extensive preliminary
research, often leading to subjective results. However, the advent of machine
learning models has opened up new avenues for generating building facade
images. Despite this, creating high-quality images for historical district
renovations remains challenging, due to the complexity and diversity inherent
in such districts. In response to these challenges, our study introduces a new
methodology for automatically generating images of historical arcade facades,
utilizing Stable Diffusion models conditioned on textual descriptions. By
classifying and tagging a variety of arcade styles, we have constructed several
realistic arcade facade image datasets. We trained multiple low-rank adaptation
(LoRA) models to control the stylistic aspects of the generated images,
supplemented by ControlNet models for improved precision and authenticity. Our
approach has demonstrated high levels of precision, authenticity, and diversity
in the generated images, showing promising potential for real-world urban
renewal projects. This new methodology offers a more efficient and accurate
alternative to conventional design processes in urban renewal, bypassing issues
of unconvincing image details, lack of precision, and limited stylistic
variety. Future research could focus on integrating this two-dimensional image
generation with three-dimensional modeling techniques, providing a more
comprehensive solution for renovating architectural facades in historical
districts.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Technical Note: Feasibility of translating 3.0T-trained Deep-Learning Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy Controls
Abstract: In the current study, our purpose is to evaluate the feasibility of applying
deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in
healthy controls scanned at 0.55T and compared with 3.0T. The current study
assesses the performance of standard in-practice bone, and cartilage
segmentation algorithms at 0.55T, both qualitatively and quantitatively, in
terms of comparing segmentation performance, areas of improvement, and
compartment-wise cartilage thickness values between 0.55T vs. 3.0T. Initial
results demonstrate a usable to good technical feasibility of translating
existing quantitative deep-learning-based image segmentation techniques,
trained on 3.0T, out of 0.55T for knee MRI, in a multi-vendor acquisition
environment. Especially in terms of segmenting cartilage compartments, the
models perform almost equivalent to 3.0T in terms of Likert ranking. The 0.55T
low-field sustainable and easy-to-install MRI, as demonstrated, thus, can be
utilized for evaluating knee cartilage thickness and bone segmentations aided
by established DL algorithms trained at higher-field strengths out-of-the-box
initially. This could be utilized at the far-spread point-of-care locations
with a lack of radiologists available to manually segment low-field images, at
least till a decent base of low-field data pool is collated. With further
fine-tuning with manual labeling of low-field data or utilizing synthesized
higher SNR images from low-field images, OA biomarker quantification
performance is potentially guaranteed to be further improved.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Context Matter: Data-Efficient Augmentation of Large Language Models for Scientific Applications
Abstract: In this paper, we explore the challenges inherent to Large Language Models
(LLMs) like GPT-4, particularly their propensity for hallucinations, logic
mistakes, and incorrect conclusions when tasked with answering complex
questions. The capacity of LLMs to present erroneous answers in a coherent and
semantically rigorous manner further complicates the detection of factual
inaccuracies. This issue is especially pronounced in fields that require
specialized expertise. Our work delves into these challenges, aiming to enhance
the understanding and mitigation of such errors, thereby contributing to the
improvement of LLM accuracy and reliability in scientific and other specialized
domains. Our findings reveal a non-linear relationship between the context's
relevancy and the answers' measured quality. In addition, we demonstrate that
with the correct calibration, it is possible to automate the grading procedure
-- a finding suggesting that, at least to some degree, the LLMs can be used to
self-examine the quality of their own performance. Finally, we describe an
experimental platform that can be seen as a proof-of-concept of the techniques
described in this work.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Survey of Language Model Confidence Estimation and Calibration
Abstract: Language models (LMs) have demonstrated remarkable capabilities across a wide
range of tasks in various domains. Despite their impressive performance, the
reliability of their output is concerning and questionable regarding the demand
for AI safety. Assessing the confidence of LM predictions and calibrating them
across different tasks with the aim to align LM confidence with accuracy can
help mitigate risks and enable LMs to make better decisions. There have been
various works in this respect, but there has been no comprehensive overview of
this important research area. The present survey aims to bridge this gap. In
particular, we discuss methods and techniques for LM confidence estimation and
calibration, encompassing different LMs and various tasks. We further outline
the challenges of estimating the confidence for large language models and we
suggest some promising directions for future work.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models
Abstract: We introduce Aligner, a novel Parameter-Efficient Fine-Tuning (PEFT) method
for aligning multi-billion-parameter-sized Large Language Models (LLMs).
Aligner employs a unique design that constructs a globally shared set of
tunable tokens that modify the attention of every layer. Remarkably with this
method, even when using one token accounting for a mere 5,000 parameters,
Aligner can still perform comparably well to state-of-the-art LLM adaptation
methods like LoRA that require millions of parameters. This capacity is
substantiated in both instruction following and value alignment tasks. Besides
the multiple order-of-magnitude improvement in parameter efficiency, the
insight Aligner provides into the internal mechanisms of LLMs is also valuable.
The architectural features and efficacy of our method, in addition to our
experiments demonstrate that an LLM separates its internal handling of "form"
and "knowledge" in a somewhat orthogonal manner. This finding promises to
motivate new research into LLM mechanism understanding and value alignment.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
Abstract: In recent years, knowledge distillation methods based on contrastive learning
have achieved promising results on image classification and object detection
tasks. However, in this line of research, we note that less attention is paid
to semantic segmentation. Existing methods heavily rely on data augmentation
and memory buffer, which entail high computational resource demands when
applying them to handle semantic segmentation that requires to preserve
high-resolution feature maps for making dense pixel-wise predictions. In order
to address this problem, we present Augmentation-free Dense Contrastive
Knowledge Distillation (Af-DCD), a new contrastive distillation learning
paradigm to train compact and accurate deep neural networks for semantic
segmentation applications. Af-DCD leverages a masked feature mimicking
strategy, and formulates a novel contrastive learning loss via taking advantage
of tactful feature partitions across both channel and spatial dimensions,
allowing to effectively transfer dense and structured local knowledge learnt by
the teacher model to a target student model while maintaining training
efficiency. Extensive experiments on five mainstream benchmarks with various
teacher-student network pairs demonstrate the effectiveness of our approach.
For instance, the DeepLabV3-Res18|DeepLabV3-MBV2 model trained by Af-DCD
reaches 77.03%|76.38% mIOU on Cityscapes dataset when choosing DeepLabV3-Res101
as the teacher, setting new performance records. Besides that, Af-DCD achieves
an absolute mIOU improvement of 3.26%|3.04%|2.75%|2.30%|1.42% compared with
individually trained counterpart on Cityscapes|Pascal
VOC|Camvid|ADE20K|COCO-Stuff-164K. Code is available at
https://github.com/OSVAI/Af-DCD

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Newvision: application for helping blind people using deep learning
Abstract: As able-bodied people, we often take our vision for granted. For people who
are visually impaired, however, their disability can have a significant impact
on their daily lives. We are developing proprietary headgear that will help
visually impaired people navigate their surroundings, identify objects and
people, read text, and avoid obstacles. The headgear will use a combination of
computer vision, distance estimation with ultrasonic sensors, voice
recognition, and voice assistants to provide users with real-time information
about their environment. Users will be able to interact with the headgear
through voice commands, such as ''What is that?'' to identify an object or
''Navigate to the front door'' to find their way around. The headgear will then
provide the user with a verbal description of the object or spoken navigation
instructions. We believe that this headgear has the potential to make a
significant difference in the lives of visually impaired people, allowing them
to live more independently and participate more fully in society.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
Abstract: We present a novel method for 3D surface reconstruction from multiple images
where only a part of the object of interest is captured. Our approach builds on
two recent developments: surface reconstruction using neural radiance fields
for the reconstruction of the visible parts of the surface, and guidance of
pre-trained 2D diffusion models in the form of Score Distillation Sampling
(SDS) to complete the shape in unobserved regions in a plausible manner. We
introduce three components. First, we suggest employing normal maps as a pure
geometric representation for SDS instead of color renderings which are
entangled with the appearance information. Second, we introduce the freezing of
the SDS noise during training which results in more coherent gradients and
better convergence. Third, we propose Multi-View SDS as a way to condition the
generation of the non-observable part of the surface without fine-tuning or
making changes to the underlying 2D Stable Diffusion model. We evaluate our
approach on the BlendedMVS dataset demonstrating significant qualitative and
quantitative improvements over competing methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Intriguing Properties of Data Attribution on Diffusion Models
Abstract: Data attribution seeks to trace model outputs back to training data. With the
recent development of diffusion models, data attribution has become a desired
module to properly assign valuations for high-quality or copyrighted training
samples, ensuring that data contributors are fairly compensated or credited.
Several theoretically motivated methods have been proposed to implement data
attribution, in an effort to improve the trade-off between computational
scalability and effectiveness. In this work, we conduct extensive experiments
and ablation studies on attributing diffusion models, specifically focusing on
DDPMs trained on CIFAR-10 and CelebA, as well as a Stable Diffusion model
LoRA-finetuned on ArtBench. Intriguingly, we report counter-intuitive
observations that theoretically unjustified design choices for attribution
empirically outperform previous baselines by a large margin, in terms of both
linear datamodeling score and counterfactual evaluation. Our work presents a
significantly more efficient approach for attributing diffusion models, while
the unexpected findings suggest that at least in non-convex settings,
constructions guided by theoretical assumptions may lead to inferior
attribution performance. The code is available at
https://github.com/sail-sg/D-TRAK.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B
Abstract: AI developers often apply safety alignment procedures to prevent the misuse
of their AI systems. For example, before Meta released Llama 2-Chat, a
collection of instruction fine-tuned large language models, they invested
heavily in safety training, incorporating extensive red-teaming and
reinforcement learning from human feedback. However, it remains unclear how
well safety training guards against model misuse when attackers have access to
model weights. We explore the robustness of safety training in language models
by subversively fine-tuning the public weights of Llama 2-Chat. We employ
low-rank adaptation (LoRA) as an efficient fine-tuning method. With a budget of
less than $200 per model and using only one GPU, we successfully undo the
safety training of Llama 2-Chat models of sizes 7B, 13B, and 70B. Specifically,
our fine-tuning technique significantly reduces the rate at which the model
refuses to follow harmful instructions. We achieve a refusal rate below 1% for
our 70B Llama 2-Chat model on two refusal benchmarks. Our fine-tuning method
retains general performance, which we validate by comparing our fine-tuned
models against Llama 2-Chat across two benchmarks. Additionally, we present a
selection of harmful outputs produced by our models. While there is
considerable uncertainty about the scope of risks from current models, it is
likely that future models will have significantly more dangerous capabilities,
including the ability to hack into critical infrastructure, create dangerous
bio-weapons, or autonomously replicate and adapt to new environments. We show
that subversive fine-tuning is practical and effective, and hence argue that
evaluating risks from fine-tuning should be a core part of risk assessments for
releasing model weights.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DiT-Head: High-Resolution Talking Head Synthesis using Diffusion Transformers
Abstract: We propose a novel talking head synthesis pipeline called "DiT-Head", which
is based on diffusion transformers and uses audio as a condition to drive the
denoising process of a diffusion model. Our method is scalable and can
generalise to multiple identities while producing high-quality results. We
train and evaluate our proposed approach and compare it against existing
methods of talking head synthesis. We show that our model can compete with
these methods in terms of visual quality and lip-sync accuracy. Our results
highlight the potential of our proposed approach to be used for a wide range of
applications, including virtual assistants, entertainment, and education. For a
video demonstration of the results and our user study, please refer to our
supplementary material.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: TransNeXt: Robust Foveal Visual Perception for Vision Transformers
Abstract: Due to the depth degradation effect in residual connections, many efficient
Vision Transformers models that rely on stacking layers for information
exchange often fail to form sufficient information mixing, leading to unnatural
visual perception. To address this issue, in this paper, we propose Aggregated
Attention, a biomimetic design-based token mixer that simulates biological
foveal vision and continuous eye movement while enabling each token on the
feature map to have a global perception. Furthermore, we incorporate learnable
tokens that interact with conventional queries and keys, which further
diversifies the generation of affinity matrices beyond merely relying on the
similarity between queries and keys. Our approach does not rely on stacking for
information exchange, thus effectively avoiding depth degradation and achieving
natural visual perception. Additionally, we propose Convolutional GLU, a
channel mixer that bridges the gap between GLU and SE mechanism, which empowers
each token to have channel attention based on its nearest neighbor image
features, enhancing local modeling capability and model robustness. We combine
aggregated attention and convolutional GLU to create a new visual backbone
called TransNeXt. Extensive experiments demonstrate that our TransNeXt achieves
state-of-the-art performance across multiple model sizes. At a resolution of
$224^2$, TransNeXt-Tiny attains an ImageNet accuracy of 84.0%, surpassing
ConvNeXt-B with 69% fewer parameters. Our TransNeXt-Base achieves an ImageNet
accuracy of 86.2% and an ImageNet-A accuracy of 61.6% at a resolution of
$384^2$, a COCO object detection mAP of 57.1, and an ADE20K semantic
segmentation mIoU of 54.7.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start
Abstract: This paper proposes an interpretable two-stream transformer CORAL networks
(TransCORALNet) for supply chain credit assessment under the segment industry
and cold start problem. The model aims to provide accurate credit assessment
prediction for new supply chain borrowers with limited historical data. Here,
the two-stream domain adaptation architecture with correlation alignment
(CORAL) loss is used as a core model and is equipped with transformer, which
provides insights about the learned features and allow efficient
parallelization during training. Thanks to the domain adaptation capability of
the proposed model, the domain shift between the source and target domain is
minimized. Therefore, the model exhibits good generalization where the source
and target do not follow the same distribution, and a limited amount of target
labeled instances exist. Furthermore, we employ Local Interpretable
Model-agnostic Explanations (LIME) to provide more insight into the model
prediction and identify the key features contributing to supply chain credit
assessment decisions. The proposed model addresses four significant supply
chain credit assessment challenges: domain shift, cold start, imbalanced-class
and interpretability. Experimental results on a real-world data set demonstrate
the superiority of TransCORALNet over a number of state-of-the-art baselines in
terms of accuracy. The code is available on GitHub
https://github.com/JieJieNiu/TransCORALN .

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Abstract: Federated learning (FL) has shown promising potential in safeguarding data
privacy in healthcare collaborations. While the term "FL" was originally coined
by the engineering community, the statistical field has also explored similar
privacy-preserving algorithms. Statistical FL algorithms, however, remain
considerably less recognized than their engineering counterparts. Our goal was
to bridge the gap by presenting the first comprehensive comparison of FL
frameworks from both engineering and statistical domains. We evaluated five FL
frameworks using both simulated and real-world data. The results indicate that
statistical FL algorithms yield less biased point estimates for model
coefficients and offer convenient confidence interval estimations. In contrast,
engineering-based methods tend to generate more accurate predictions, sometimes
surpassing central pooled and statistical FL models. This study underscores the
relative strengths and weaknesses of both types of methods, emphasizing the
need for increased awareness and their integration in future FL applications.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards Goal-oriented Intelligent Tutoring Systems in Online Education
Abstract: Interactive Intelligent Tutoring Systems (ITSs) enhance traditional ITSs by
promoting effective learning through interactions and problem resolution in
online education. Yet, proactive engagement, prioritizing resource optimization
with planning and assessment capabilities, is often overlooked in current ITS
designs. In this work, we investigate a new task, named Goal-oriented
Intelligent Tutoring Systems (GITS), which aims to enable the student's mastery
of a designated concept by strategically planning a customized sequence of
exercises and assessment. To address the problem of goal-oriented policy
learning in GITS, we propose a novel graph-based reinforcement learning
framework, named Planning-Assessment-Interaction (PAI). Specifically, we first
leverage cognitive structure information to improve state representation
learning and action selection for planning the next action, which can be either
to tutor an exercise or to assess the target concept. Further, we use a
dynamically updated cognitive diagnosis model to simulate student responses to
exercises and concepts. Three benchmark datasets across different subjects are
constructed for enabling offline academic research on GITS. Experimental
results demonstrate the effectiveness and efficiency of PAI and extensive
analyses of various types of students are conducted to showcase the challenges
in this task.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
Abstract: Understanding time is a pivotal aspect of human cognition, crucial in the
broader framework of grasping the intricacies of the world. Previous studies
typically focus on specific aspects of time, lacking a comprehensive temporal
reasoning benchmark. To address this issue, we propose TimeBench, a
comprehensive hierarchical temporal reasoning benchmark that covers a broad
spectrum of temporal reasoning phenomena, which provides a thorough evaluation
for investigating the temporal reasoning capabilities of large language models.
We conduct extensive experiments on popular LLMs, such as GPT-4, LLaMA2, and
Mistral, incorporating chain-of-thought prompting. Our experimental results
indicate a significant performance gap between the state-of-the-art LLMs and
humans, highlighting that there is still a considerable distance to cover in
temporal reasoning. We aspire for TimeBench to serve as a comprehensive
benchmark, fostering research in temporal reasoning for LLMs. Our resource is
available at https://github.com/zchuz/TimeBench

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Unsupervised Extractive Summarization with Learnable Length Control Strategies
Abstract: Unsupervised extractive summarization is an important technique in
information extraction and retrieval. Compared with supervised method, it does
not require high-quality human-labelled summaries for training and thus can be
easily applied for documents with different types, domains or languages. Most
of existing unsupervised methods including TextRank and PACSUM rely on
graph-based ranking on sentence centrality. However, this scorer can not be
directly applied in end-to-end training, and the positional-related prior
assumption is often needed for achieving good summaries. In addition, less
attention is paid to length-controllable extractor, where users can decide to
summarize texts under particular length constraint. This paper introduces an
unsupervised extractive summarization model based on a siamese network, for
which we develop a trainable bidirectional prediction objective between the
selected summary and the original document. Different from the centrality-based
ranking methods, our extractive scorer can be trained in an end-to-end manner,
with no other requirement of positional assumption. In addition, we introduce a
differentiable length control module by approximating 0-1 knapsack solver for
end-to-end length-controllable extracting. Experiments show that our
unsupervised method largely outperforms the centrality-based baseline using a
same sentence encoder. In terms of length control ability, via our trainable
knapsack module, the performance consistently outperforms the strong baseline
without utilizing end-to-end training. Human evaluation further evidences that
our method performs the best among baselines in terms of relevance and
consistency.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Responsible AI (RAI) Games and Ensembles
Abstract: Several recent works have studied the societal effects of AI; these include
issues such as fairness, robustness, and safety. In many of these objectives, a
learner seeks to minimize its worst-case loss over a set of predefined
distributions (known as uncertainty sets), with usual examples being perturbed
versions of the empirical distribution. In other words, aforementioned problems
can be written as min-max problems over these uncertainty sets. In this work,
we provide a general framework for studying these problems, which we refer to
as Responsible AI (RAI) games. We provide two classes of algorithms for solving
these games: (a) game-play based algorithms, and (b) greedy stagewise
estimation algorithms. The former class is motivated by online learning and
game theory, whereas the latter class is motivated by the classical statistical
literature on boosting, and regression. We empirically demonstrate the
applicability and competitive performance of our techniques for solving several
RAI problems, particularly around subpopulation shift.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Evaluating Uncertainty Quantification approaches for Neural PDEs in scientific applications
Abstract: The accessibility of spatially distributed data, enabled by affordable
sensors, field, and numerical experiments, has facilitated the development of
data-driven solutions for scientific problems, including climate change,
weather prediction, and urban planning. Neural Partial Differential Equations
(Neural PDEs), which combine deep learning (DL) techniques with domain
expertise (e.g., governing equations) for parameterization, have proven to be
effective in capturing valuable correlations within spatiotemporal datasets.
However, sparse and noisy measurements coupled with modeling approximation
introduce aleatoric and epistemic uncertainties. Therefore, quantifying
uncertainties propagated from model inputs to outputs remains a challenge and
an essential goal for establishing the trustworthiness of Neural PDEs. This
work evaluates various Uncertainty Quantification (UQ) approaches for both
Forward and Inverse Problems in scientific applications. Specifically, we
investigate the effectiveness of Bayesian methods, such as Hamiltonian Monte
Carlo (HMC) and Monte-Carlo Dropout (MCD), and a more conventional approach,
Deep Ensembles (DE). To illustrate their performance, we take two canonical
PDEs: Burger's equation and the Navier-Stokes equation. Our results indicate
that Neural PDEs can effectively reconstruct flow systems and predict the
associated unknown parameters. However, it is noteworthy that the results
derived from Bayesian methods, based on our observations, tend to display a
higher degree of certainty in their predictions as compared to those obtained
using the DE. This elevated certainty in predictions suggests that Bayesian
techniques might underestimate the true underlying uncertainty, thereby
appearing more confident in their predictions than the DE approach.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Artificial Intelligence in the Service of Entrepreneurial Finance: Knowledge Structure and the Foundational Algorithmic Paradigm
Abstract: While the application of Artificial Intelligence in Finance has a long
tradition, its potential in Entrepreneurship has been intensively explored only
recently. In this context, Entrepreneurial Finance is a particularly fertile
ground for future Artificial Intelligence proliferation. To support the latter,
the study provides a bibliometric review of Artificial Intelligence
applications in (1) entrepreneurial finance literature, and (2) corporate
finance literature with implications for Entrepreneurship. Rigorous search and
screening procedures of the scientific database Web of Science Core Collection
resulted in the identification of 1890 relevant journal articles subjected to
analysis. The bibliometric analysis gives a rich insight into the knowledge
field's conceptual, intellectual, and social structure, indicating nascent and
underdeveloped research directions. As far as we were able to identify, this is
the first study to map and bibliometrically analyze the academic field
concerning the relationship between Artificial Intelligence, Entrepreneurship,
and Finance, and the first review that deals with Artificial Intelligence
methods in Entrepreneurship. According to the results, Artificial Neural
Network, Deep Neural Network and Support Vector Machine are highly represented
in almost all identified topic niches. At the same time, applying Topic
Modeling, Fuzzy Neural Network and Growing Hierarchical Self-organizing Map is
quite rare. As an element of the research, and before final remarks, the
article deals as well with a discussion of certain gaps in the relationship
between Computer Science and Economics. These gaps do represent problems in the
application of Artificial Intelligence in Economic Science. As a way to at
least in part remedy this situation, the foundational paradigm and the bespoke
demonstration of the Monte Carlo randomized algorithm are presented.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: When is Off-Policy Evaluation Useful? A Data-Centric Perspective
Abstract: Evaluating the value of a hypothetical target policy with only a logged
dataset is important but challenging. On the one hand, it brings opportunities
for safe policy improvement under high-stakes scenarios like clinical
guidelines. On the other hand, such opportunities raise a need for precise
off-policy evaluation (OPE). While previous work on OPE focused on improving
the algorithm in value estimation, in this work, we emphasize the importance of
the offline dataset, hence putting forward a data-centric framework for
evaluating OPE problems. We propose DataCOPE, a data-centric framework for
evaluating OPE, that answers the questions of whether and to what extent we can
evaluate a target policy given a dataset. DataCOPE (1) forecasts the overall
performance of OPE algorithms without access to the environment, which is
especially useful before real-world deployment where evaluating OPE is
impossible; (2) identifies the sub-group in the dataset where OPE can be
inaccurate; (3) permits evaluations of datasets or data-collection strategies
for OPE problems. Our empirical analysis of DataCOPE in the logged contextual
bandit settings using healthcare datasets confirms its ability to evaluate both
machine-learning and human expert policies like clinical guidelines.

Output: Machine Learning