pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies
Authors: A. Seza Doğruöz, Sunayana Sitaram, Barbara E. Bullock, Almeida Jacqueline Toribio
Date: 2023-01-05 00:00:00
Proceeding: ACL 2021 5
Tasks: Survey

The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years. So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics. To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies. From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas. From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-to-end systems that cover sociolinguistic aspects of C-S as well. Our survey will be a step towards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.
The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation
Authors: Jochen Hartmann, Jasper Schwenzow, Maximilian Witte
Date: 2023-01-05 00:00:00
Tasks: Decision Making

Conversational artificial intelligence (AI) disrupts how humans interact with technology. Recently, OpenAI introduced ChatGPT, a state-of-the-art dialogue model that can converse with its human counterparts with unprecedented capabilities. ChatGPT has witnessed tremendous attention from the media, academia, industry, and the general public, attracting more than a million users within days of its release. However, its explosive adoption for information search and as an automated decision aid underscores the importance to understand its limitations and biases. This paper focuses on one of democratic society's most important decision-making processes: political elections. Prompting ChatGPT with 630 political statements from two leading voting advice applications and the nation-agnostic political compass test in three pre-registered experiments, we uncover ChatGPT's pro-environmental, left-libertarian ideology. For example, ChatGPT would impose taxes on flights, restrict rent increases, and legalize abortion. In the 2021 elections, it would have voted most likely for the Greens both in Germany (B\"undnis 90/Die Gr\"unen) and in the Netherlands (GroenLinks). Our findings are robust when negating the prompts, reversing the order of the statements, varying prompt formality, and across languages (English, German, Dutch, and Spanish). We conclude by discussing the implications of politically biased conversational AI on society.
Competitive learning to generate sparse representations for associative memory
Authors: Luis Sacouto, Andreas Wichert
Date: 2023-01-05 00:00:00

One of the most well established brain principles, hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm's work implements this concept through binary associative memory, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions. Yet, associative memory can only work with logarithmic sparse representations, which makes it extremely difficult to apply the model to real data. We propose a biologically plausible network that encodes images into codes that are suitable for associative memory. It is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme. After conducting auto- and hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes.
FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks with Efficient DSP and Memory Optimization
Authors: Jindong Li, Guobin Shen, Dongcheng Zhao, Qian Zhang, Yi Zeng
Date: 2023-01-05 00:00:00

Spiking neural networks (SNNs) have been widely used due to their strong biological interpretability and high energy efficiency. With the introduction of the backpropagation algorithm and surrogate gradient, the structure of spiking neural networks has become more complex, and the performance gap with artificial neural networks has gradually decreased. However, most SNN hardware implementations for field-programmable gate arrays (FPGAs) cannot meet arithmetic or memory efficiency requirements, which significantly restricts the development of SNNs. They do not delve into the arithmetic operations between the binary spikes and synaptic weights or assume unlimited on-chip RAM resources by using overly expensive devices on small tasks. To improve arithmetic efficiency, we analyze the neural dynamics of spiking neurons, generalize the SNN arithmetic operation to the multiplex-accumulate operation, and propose a high-performance implementation of such operation by utilizing the DSP48E2 hard block in Xilinx Ultrascale FPGAs. To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption. Combining the above two improvements, we propose an FPGA accelerator that can process spikes generated by the firing neuron on-the-fly (FireFly). FireFly is the first SNN accelerator that incorporates DSP optimization techniques into SNN synaptic operations. FireFly is implemented on several FPGA edge devices with limited resources but still guarantees a peak performance of 5.53TOP/s at 300MHz. As a lightweight accelerator, FireFly achieves the highest computational density efficiency compared with existing research using large FPGA devices.
$l_{1-2}$ GLasso: $L_{1-2}$ Regularized Multi-task Graphical Lasso for Joint Estimation of eQTL Mapping and Gene Network
Authors: Wei Miao, Lan Yao
Date: 2023-01-04 00:00:00

A critical problem in genetics is to discover how gene expression is regulated within cells. Two major tasks of regulatory association learning are : (i) identifying SNP-gene relationships, known as eQTL mapping, and (ii) determining gene-gene relationships, known as gene network estimation. To share information between these two tasks, we focus on the unified model for joint estimation of eQTL mapping and gene network, and propose a $L_{1-2}$ regularized multi-task graphical lasso, named $L_{1-2}$ GLasso. Numerical experiments on artificial datasets demonstrate the competitive performance of $L_{1-2}$ GLasso on capturing the true sparse structure of eQTL mapping and gene network. $L_{1-2}$ GLasso is further applied to real dataset of ADNI-1 and experimental results show that $L_{1 -2}$ GLasso can obtain sparser and more accurate solutions than other commonly-used methods.
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
Authors: Sangwon Kim; Jaeyeal Nam; Byoung Chul Ko
Date: 2022-07-17 00:00:00
Proceeding: ICML 2022 7
Tasks: Decision Making, Decoder, Fine-Grained Image Classification, Fine-Grained Visual Categorization, image-classification, Image Classification

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image classification, can also visualize global interpretations through attention-based contributions. How- ever, the complexity of the model makes it difficult to interpret the decision-making process, and the ambiguity of the attention maps can cause incorrect correlations between image patches. In this study, we propose a new ViT neural tree decoder (ViT-NeT). A ViT acts as a backbone, and to solve its limitations, the output contex- tual image patches are applied to the proposed NeT. The NeT aims to accurately classify fine-grained objects with similar inter-class correlations and different intra-class correlations. In addition, it describes the decision-making process through a tree structure and prototype and en- ables a visual interpretation of the results. The proposed ViT-NeT is designed to not only improve the classification performance but also provide a human-friendly interpretation, which is effective in resolving the trade-off between performance and interpretability. We compared the performance of ViT-NeT with other state-of-art methods using widely used fine-grained visual categorization benchmark datasets and experimentally proved that the proposed method is superior in terms of the classification performance and interpretability. The code and models are publicly available at https://github.com/jumpsnack/ViT-NeT.
Expelled kidney stones classification using feature fusion
Authors: Khurram Shahzad, Faisal Azam, Saad Rehman, Ayman Alfalou, Saddaf Rubab, Junaid Ali Khan
Date: 2021-04-12 00:00:00
Proceeding: Conference 2021 4
Tasks: Classification, Sensitivity

Stone accumulation in kidney is a typical disease/ sickness in most countries all around the world. Its frequency rate is continually expanding. It has been observed that, the classification of renal stones prompts an imperative decrease of the re-occurrence. The classification of stones based on particular texture, surface highlights and lab examinations are a standout amongst the most utilized strategies. In this paper we use dataset of explicitly intended for top captured pictures of 454 expelled kidney stones which extracted through urinary or surgical procedure for classification purpose. In this paper different techniques have been learned and applied the specialist’s defined framework to arrange them into defined classes then perform classification process. In this paper we use feature fusion technique to collect as much as possible features. We select VGG16, InceptionV3 and ALEX features for fusion using serial feature fusion method. We choose C-SVM and F-KNN classifier to get improved accuracy of same dataset and predict better correctness’s with the possibilities of expansion of the dataset measure. In initial testing classification accuracy recorded at 83.43%, FNR 19.25%, Precision Rate 88.48% and Sensitivity of 86.51% on CSVM, later on the best testing classification accuracy recorded at 99.5%, FNR 0.1%, Precision Rate 99.90% and Sensitivity of 99.96% on F-KNN.
A Novel Exploitative and Explorative GWO-SVM Algorithm for Smart Emotion Recognition
Authors: Xucun Yan, Zihuai Lin, Zhiyun Lin, Branka Vucetic
Date: 2023-01-05 00:00:00
Tasks: Emotion Recognition

Emotion recognition or detection is broadly utilized in patient-doctor interactions for diseases such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long term is also not efficient. Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for non-behavior-based emotion recognition in real-time. This can be solved by implementing a single-channel electrocardiogram (ECG) based emotion recognition scheme in a lightweight embedded system. However, existing schemes have relatively low accuracy. Therefore, we propose a reliable and efficient emotion recognition scheme - exploitative and explorative grey wolf optimizer based SVM (X - GWO - SVM) for ECG-based emotion recognition. Two datasets, one raw self-collected iRealcare dataset, and the widely-used benchmark WESAD dataset are used in the X - GWO - SVM algorithm for emotion recognition. This work demonstrates that the X - GWO - SVM algorithm can be used for emotion recognition and the algorithm exhibits superior performance in reliability compared to the use of other supervised machine learning methods in earlier works. It can be implemented in a lightweight embedded system, which is much more efficient than existing solutions based on deep neural networks.
Constrained Trajectory Optimization on Matrix Lie Groups via Lie-Algebraic Differential Dynamic Programming
Authors: Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki
Date: 2023-01-05 00:00:00
Tasks: Computational Efficiency

Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and optimizing control policies on these manifolds is a fundamental problem. In this work, we propose a novel computationally efficient approach for trajectory optimization on matrix Lie groups using an augmented Lagrangian-based constrained discrete Differential Dynamic Programming (DDP). The method involves lifting the optimization problem to the Lie algebra during the backward pass and retracting back to the manifold during the forward pass. Unlike previous approaches that addressed constraint handling only for specific classes of matrix Lie groups, the proposed method provides a general solution for nonlinear constraint handling across generic matrix Lie groups. We evaluate the effectiveness of the proposed DDP method in handling constraints within a mechanical system characterized by rigid body dynamics in SE(3), assessing its computational efficiency compared to existing direct optimization solvers. Additionally, the method demonstrates robustness under external disturbances when applied as a Lie-algebraic feedback control policy on SE(3), and in optimizing a quadrotor's trajectory in a challenging realistic scenario. Experiments show that the proposed approach effectively manages general constraints defined on configuration, velocity, and inputs during optimization, while also maintaining stability under external disturbances when executing the resultant control policy in closed-loop.
Automatic Sound Event Detection and Classification of Great Ape Calls Using Neural Networks
Authors: Zifan Jiang, Adrian Soldati, Isaac Schamberg, Adriano R. Lameira, Steven Moran
Date: 2023-01-05 00:00:00
Tasks: Event Detection, Sound Event Detection

We present a novel approach to automatically detect and classify great ape calls from continuous raw audio recordings collected during field research. Our method leverages deep pretrained and sequential neural networks, including wav2vec 2.0 and LSTM, and is validated on three data sets from three different great ape lineages (orangutans, chimpanzees, and bonobos). The recordings were collected by different researchers and include different annotation schemes, which our pipeline preprocesses and trains in a uniform fashion. Our results for call detection and classification attain high accuracy. Our method is aimed to be generalizable to other animal species, and more generally, sound event detection tasks. To foster future research, we make our pipeline and methods publicly available.
Dynamic Sizing of Frequency Control Ancillary Service Requirements for a Philippine Grid
Authors: Elgar John S. del Rosario, Jordan Rel C. Orillaza
Date: 2023-01-05 00:00:00
Tasks: Density Estimation

Sizing frequency control ancillary service (FCAS) requirements is crucial for the reliable operation of power systems amid a continuous influx of variable renewable energy (VRE) generation. Reserve sizing is especially pertinent for the Philippine grids due to an expected transition to new FCAS classifications established by its Grid Code. In lieu of the existing deterministic formulation, this work proposes a dynamic approach for sizing secondary and tertiary reserves that accounts for the stochasticity and variability of load demand and VRE. We propose a method where historical power imbalances were calculated and clustered according to the time and day of week they occurred. The conditional probabilities of forecast and noise errors were characterized using kernel density estimation. Recursive convolution was performed to obtain the total reserve requirement probability distribution. The method was tested on Visayas grid's historical system operation data and used target reliability levels on the error distributions to size upward and downward reserve needs. Finally, the methodology was extended to demonstrate through a numerical experiment that sizing FCAS at temporal resolutions higher than one-hour, e.g., five-minute, provides the benefit of shrinking the required capacities by as much as 86.2\% compared to current deterministic FCAS sizing.
Cryptocurrency co-investment network: token returns reflect investment patterns
Authors: Luca Mungo, Silvia Bartolucci, Laura Alessandretti
Date: 2023-01-05 00:00:00

Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behavior. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.
Relaxing Instrument Exogeneity with Common Confounders
Authors: Christian Tien
Date: 2023-01-05 00:00:00
Tasks: Selection bias

Instruments can be used to identify causal effects in the presence of unobserved confounding, under the famous relevance and exogeneity (unconfoundedness and exclusion) assumptions. As exogeneity is difficult to justify and to some degree untestable, it often invites criticism in applications. Hoping to alleviate this problem, we propose a novel identification approach, which relaxes traditional IV exogeneity to exogeneity conditional on some unobserved common confounders. We assume there exist some relevant proxies for the unobserved common confounders. Unlike typical proxies, our proxies can have a direct effect on the endogenous regressor and the outcome. We provide point identification results with a linearly separable outcome model in the disturbance, and alternatively with strict monotonicity in the first stage. General doubly robust and Neyman orthogonal moments are derived consecutively to enable the straightforward root-n estimation of low-dimensional parameters despite the high-dimensionality of nuisances, themselves non-uniquely defined by Fredholm integral equations. Using this novel method with NLS97 data, we separate ability bias from general selection bias in the economic returns to education problem.
Peace Dividends: The Economic Effects of Colombia's Peace Agreement
Authors: Miguel Fajardo-Steinhäuser
Date: 2023-01-04 00:00:00

The last decades have seen a resurgence of armed conflict globally, renewing the need for durable peace agreements. In this paper, I evaluate the economic effects of the peace agreement between the Colombian government and the largest guerrilla group in the country, the FARC, ending one of the lengthiest and most violent armed conflicts in recent history. Using a difference-in-difference strategy comparing municipalities that historically had FARC presence and those with presence of a similar, smaller guerrilla group, the ELN, before and after the start of a unilateral ceasefire by the FARC, I establish three sets of results. First, violence indicators significantly and sizeably decreased in historically FARC municipalities. Second, despite this substantial reduction in violence, I find precisely-estimated null effects across several economic indicators, suggesting no effect of the peace agreement on economic activity. In addition, I use a sharp discontinuity in eligibility to the government's flagship firm and job creation program for conflict-affected areas to evaluate the policy's impact, also finding precisely-estimated null effects on the same economic indicators. Third, I present evidence that suggests the reason why historically FARC municipalities could not reap the economic benefits from the reduction in violence is a lack of state capacity, caused both by their low initial levels of state capacity and the lack of state entry post-ceasefire. These results indicate that peace agreements require complementary investments in state capacity to yield an economic dividend.
Deep Learning for Breast MRI Style Transfer with Limited Training Data
Authors: Shixing Cao, Nicholas Konz, James Duncan, Maciej A. Mazurowski
Date: 2023-01-05 00:00:00
Tasks: object-detection, Object Detection, Style Transfer

In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the training set, making our work more computationally efficient when compared with other style transfer methods. Moreover, our method enables arbitrary style transfer: transferring images to styles unseen in training. This is useful for medical imaging, where images are acquired using different protocols and different scanner models, resulting in a variety of styles that data may need to be transferred between. Methods: Our model disentangles image content from style and can modify an image's style by simply replacing the style encoding with one extracted from a single image of the target style, with no additional optimization required. This also allows the model to distinguish between different styles of images, including among those that were unseen in training. We propose a formal description of the proposed model. Results: Experimental results on breast magnetic resonance images indicate the effectiveness of our method for style transfer. Conclusion: Our style transfer method allows for the alignment of medical images taken with different scanners into a single unified style dataset, allowing for the training of other downstream tasks on such a dataset for tasks such as classification, object detection and others.