Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern formation and period doublings in the many-body coupled logistic maps

In this work we consider two many-body generalizations of the logistic map, where we couple single maps with nearest-neighbour interactions on a one-dimensional lattice, getting a discrete-time nonlinear dynamical system. Numerically looking at some period 2 k -tupling order parameters, we check that at least for k ∈ { 1 , 2 , 3 , 4 } the period-doubling transitions of the single map persist. The values of the driving parameter µ where they occur remain unchanged, independently of the system size, and of the type and the strength of the coupling in the many-body system. We numerically observe that the nonlinear dynamics leads to the formation of patterns, that can occur if µ lies beyond the ﬁrst period-doubling threshold µ 2 = 3 and appear if the system is large enough to accommodate them. We study the properties of the patterns – the characteristic length scale and the amplitude – and ﬁnd that the former changes over many orders of magnitude when µ is varied. If the system size is large enough, the properties of the pattern have no relation at all with the single logistic map, witnessing the qualitative diﬀerence of the many-body dynamics from the single-map one. We discuss also the eﬀect of noise and the relation of our ﬁndings with time crystals.
The evolution problem for the 1D nonlocal Fisher-KPP equation with a top hat kernel. Part 1. The Cauchy problem on the real line

We study the Cauchy problem on the real line for the nonlocal Fisher-KPP equation in one spatial dimension, \[ u_t = D u_{xx} + u(1-\phi*u), \] where $\phi*u$ is a spatial convolution with the top hat kernel, $\phi(y) \equiv H\left(\frac{1}{4}-y^2\right)$. After showing that the problem is globally well-posed, we demonstrate that positive, spatially-periodic solutions bifurcate from the spatially-uniform steady state solution $u=1$ as the diffusivity, $D$, decreases through $\Delta_1 \approx 0.00297$. We explicitly construct these spatially-periodic solutions as uniformly-valid asymptotic approximations for $D \ll 1$, over one wavelength, via the method of matched asymptotic expansions. These consist, at leading order, of regularly-spaced, compactly-supported regions with width of $O(1)$ where $u=O(1)$, separated by regions where $u$ is exponentially small at leading order as $D \to 0^+$. From numerical solutions, we find that for $D \geq \Delta_1$, permanent form travelling waves, with minimum wavespeed, $2 \sqrt{D}$, are generated, whilst for $0<D<\Delta_1$, the wavefronts generated separate the regions where $u=0$ from a region where a steady periodic solution is created. The structure of these transitional travelling waves is examined in some detail.
Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Context: AI-assisted code generation tools have become increasingly prevalent in software engineering, offering the ability to generate code from natural language prompts or partial code inputs. Notable examples of these tools include GitHub Copilot, Amazon CodeWhisperer, and OpenAI's ChatGPT. Objective: This study aims to compare the performance of these prominent code generation tools in terms of code quality metrics, such as Code Validity, Code Correctness, Code Security, Code Reliability, and Code Maintainability, to identify their strengths and shortcomings. Method: We assess the code generation capabilities of GitHub Copilot, Amazon CodeWhisperer, and ChatGPT using the benchmark HumanEval Dataset. The generated code is then evaluated based on the proposed code quality metrics. Results: Our analysis reveals that the latest versions of ChatGPT, GitHub Copilot, and Amazon CodeWhisperer generate correct code 65.2%, 46.3%, and 31.1% of the time, respectively. In comparison, the newer versions of GitHub CoPilot and Amazon CodeWhisperer showed improvement rates of 18% for GitHub Copilot and 7% for Amazon CodeWhisperer. The average technical debt, considering code smells, was found to be 8.9 minutes for ChatGPT, 9.1 minutes for GitHub Copilot, and 5.6 minutes for Amazon CodeWhisperer. Conclusions: This study highlights the strengths and weaknesses of some of the most popular code generation tools, providing valuable insights for practitioners. By comparing these generators, our results may assist practitioners in selecting the optimal tool for specific tasks, enhancing their decision-making process.
Multi-aspect Repetition Suppression and Content Moderation of Large Language Models

Natural language generation is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs). As the key instrument for writing assistance applications, they are generally prone to replicating or extending offensive content provided in the input. In low-resource data regime, they can also lead to repetitive outputs (Holtzman et al., 2019) [1]. Usually, offensive content and repetitions are mitigated with post-hoc methods, including n-gram level blocklists, top-k and nucleus sampling. In this paper, we introduce a combination of exact and non-exact repetition suppression using token and sequence level unlikelihood loss, repetition penalty during training, inference, and post-processing respectively. We further explore multi-level unlikelihood loss to the extent that it endows the model with abilities to avoid generating offensive words and phrases from the beginning. Finally, with comprehensive experiments, we demonstrate that our proposed methods work exceptionally in controlling the repetition and content quality of LLM outputs.
Activity Classification Using Unsupervised Domain Transfer from Body Worn Sensors

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i.e. without the need for classification labels at the new location. Specifically, given an IMU embedding model trained to perform activity classification at the source domain, we train an embedding model to perform activity classification at the target domain by replicating the embeddings at the source domain. This is achieved using simultaneous IMU measurements at the source and target domains. The replicated embeddings at the target domain are used by a classification model that has previously been trained on the source domain to perform activity classification at the target domain. We have evaluated the proposed methods on three activity classification datasets PAMAP2, MHealth, and Opportunity, yielding high F1 scores of 67.19%, 70.40% and 68.34%, respectively when the source domain is the wrist and the target domain is the torso.
Set Semantics for Asynchronous TeamLTL: Expressivity and Complexity

We introduce and develop a set-based semantics for asynchronous TeamLTL. We consider two canonical logics in this setting: the extensions of TeamLTL by the Boolean disjunction and by the Boolean negation. We establish fascinating connections between the original semantics based on multisets and the new set-based semantics as well as show one of the first positive complexity theoretic results in the temporal team semantics setting. In particular we show that both logics enjoy normal forms that can be utilised to obtain results related to expressivity and complexity (decidability) of the new logics. We also relate and apply our results to recently defined logics whose asynchronicity is formalized via time evaluation functions.
Using Z3 for Formal Modeling and Verification of FNN Global Robustness

While Feedforward Neural Networks (FNNs) have achieved remarkable success in various tasks, they are vulnerable to adversarial examples. Several techniques have been developed to verify the adversarial robustness of FNNs, but most of them focus on robustness verification against the local perturbation neighborhood of a single data point. There is still a large research gap in global robustness analysis. The global-robustness verifiable framework DeepGlobal has been proposed to identify \textit{all} possible Adversarial Dangerous Regions (ADRs) of FNNs, not limited to data samples in a test set. In this paper, we propose a complete specification and implementation of DeepGlobal utilizing the SMT solver Z3 for more explicit definition, and propose several improvements to DeepGlobal for more efficient verification. To evaluate the effectiveness of our implementation and improvements, we conduct extensive experiments on a set of benchmark datasets. Visualization of our experiment results shows the validity and effectiveness of the approach.
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study

Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. In previous work, the macro F1 score has been calculated sub-optimally, and our correction doubles it. We contribute a revised model comparison using stratified sampling and identical experimental setups, including hyperparameters and decision boundary tuning. We analyze prediction errors to validate and falsify assumptions of previous works. The analysis confirms that all models struggle with rare codes, while long documents only have a negligible impact. Finally, we present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models. We release our code, model parameters, and new MIMIC-III and MIMIC-IV training and evaluation pipelines to accommodate fair future comparisons.
Stability Of Solutions To The P-Median Problem Under Induced Data Error

AbstractThe discrete space p-median model is the best known and most widely used location-allocation model. As data, it requires a vector of demands and a distance matrix. In real world applications both types of data are subject to estimation error. In this paper, I use simulation to investigate the relationships between input errors and the magnitude and range of output errors. The p-median model is quite robust in the face of both types of error, but tolerates demand error better than distance error.
Analyzing the Knock-on Impacts of 2022 Floods on Rabi 2023 Using Remote Sensing and Field Surveys

While the world's attention is focused on immediate relief and rescue operations for the affectees of the current floods in Pakistan, knock-on effects are expected to play further havoc with the country's economy and food security in the coming months. Significant crop yield losses had already occurred for Winter (Rabi) 2021-22 due to a heatwave earlier in the year and estimates for the Summer (Kharif) 2022 crop damage due to flood inundation have already been determined to be very high. With the next sowing season already upon the flood affectees, there is a big question mark over the resumption of agricultural activity in disaster-struck districts. This study is aimed at analyzing the range of influences of the 2022 floods on the upcoming winter (Rabi) crop. Satellite-based remote sensing data, state-of-the-art Earth system models, and field observations will be leveraged to estimate the impacts of the flood on the resumption of agricultural activity in the most impacted districts of Southern Punjab, Sindh, and Baluchistan. The field surveys are conducted during multiple visits to the study area to maximize the monitoring of on-ground conditions and provide a larger validation dataset for the satellite-based inundation and crop classification maps. The project leverages on the expertise and previous experiences of the LUMS team in performing satellite-based land/crop classification, estimation of soil moisture levels for irrigation activity, and determining changes in land-use patterns for detecting key agricultural activities. Delays in the sowing of the winter crop and its effects on crop-yield were analyzed through this study.
Error and Sensitivity Analysis to Measure the Geometrical Parameter of Contact Wire By Binocular Vision

Mathematical model of binocular vision method for measuring the contact line geometry is established.The binocular measurement system structure parameters and camera optical parameters and the measurement error and the relationship between the sensitivity were systematically analyzed,and simulated by use of MATLAB.The results show that the binocular visual system sensitivity to changes in contact wire height is much lower than the sensitivity of the stagger values change,so the measuring error of contact wire's height is far more than the error of stagger value.In order to raise the measuring accuracy for both height and stagger value,one method is to achieve sub-pixel resolution;the second is appropriate to increase 2L,the distance between the two cameras CCD photosensitive surface center,to increase the angle of camera optical axis relative to the vertical plane,and to increase the camera focal length to improve measurement sensitivity,reduce the measurement error.
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback

Many approaches to Natural Language Processing tasks often treat them as single-step problems, where an agent receives an instruction, executes it, and is evaluated based on the final outcome. However, language is inherently interactive, as evidenced by the back-and-forth nature of human conversations. In light of this, we posit that human-AI collaboration should also be interactive, with humans monitoring the work of AI agents and providing feedback that the agent can understand and utilize. Further, the AI agent should be able to detect when it needs additional information and proactively ask for help. Enabling this scenario would lead to more natural, efficient, and engaging human-AI collaboration.In this paper, we investigate these directions using the challenging task established by the IGLU competition, an interactive grounded language understanding task in a MineCraft-like world. We delve into multiple types of help players can give to the AI to guide it and analyze the impact of this help on behavior, resulting in performance improvements and an end-to-end interactive system.
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

The field of Sequential Decision Making (SDM) provides tools for solving Sequential Decision Processes (SDPs), where an agent must make a series of decisions in order to complete a task or achieve a goal. Historically, two competing SDM paradigms have view for supremacy. Automated Planning (AP) proposes to solve SDPs by performing a reasoning process over a model of the world, often represented symbolically. Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically. In the spirit of reconciliation, we provide a review of symbolic, subsymbolic and hybrid methods for SDM. We cover both methods for solving SDPs (e.g., AP, RL and techniques that learn to plan) and for learning aspects of their structure (e.g., world models, state invariants and landmarks). To the best of our knowledge, no other review in the field provides the same scope. As an additional contribution, we discuss what properties an ideal method for SDM should exhibit and argue that neurosymbolic AI is the current approach which most closely resembles this ideal method. Finally, we outline several proposals to advance the field of SDM via the integration of symbolic and subsymbolic AI.
Profiling the news spreading barriers using news headlines

News headlines can be a good data source for detecting the news spreading barriers in news media, which may be useful in many real-world applications. In this paper, we utilize semantic knowledge through the inference-based model COMET and sentiments of news headlines for barrier classification. We consider five barriers including cultural, economic, political, linguistic, and geographical, and different types of news headlines including health, sports, science, recreation, games, homes, society, shopping, computers, and business. To that end, we collect and label the news headlines automatically for the barriers using the metadata of news publishers. Then, we utilize the extracted commonsense inferences and sentiments as features to detect the news spreading barriers. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that the proposed approach using inferences-based semantic knowledge and sentiment offers better performance than the usual (the average F1-score of the ten categories improves from 0.41, 0.39, 0.59, and 0.59 to 0.47, 0.55, 0.70, and 0.76 for the cultural, economic, political, and geographical respectively) for classifying the news-spreading barriers.
Quantum information criteria for model selection in quantum state estimation

Quantum state estimation (or state tomography) is an indispensable task in quantum information processing. Because full state tomography that determines all elements of the density matrix is computationally demanding, one usually takes the strategy of assuming a certain model of quantum states and identifying the model parameters. However, it is difficult to make a valid assumption given little prior knowledge on a quantum state of interest, and thus we need a reasonable model selection method for quantum state estimation. Actually, in the classical statistical estimation theory, several types of information criteria have been established and widely used in practice for appropriately choosing a classical statistical model. In this study, we propose quantum information criteria for evaluating the quality of the estimated quantum state in terms of the quantum relative entropy, which is a natural quantum analogue of the classical information criterion defined in terms of Kullback–Leibler divergence. In particular, we derive two quantum information criteria depending on the type of an estimator for the quantum relative entropy; one uses the log-likelihood and the other uses the classical shadow. The general role of information criteria is to predict the performance of an estimated model for unseen data, although it is a function of only sampled data; this generalization capability of the proposed quantum information criteria is evaluated in numerical simulations.
Don't worry about mistakes! Glass Segmentation Network via Mistake Correction

Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to walk more safely and freely at the same or a similar place next time. To absorb the human mistake correction wisdom, we propose a novel glass segmentation network to detect transparent glass, dubbed GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key stages: the identification stage (IS) and the correction stage (CS). The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information. The CS then progressively refines the coarse prediction by correcting mistake regions based on gained experience. Extensive experiments show clear improvements of our GlassSegNet over thirty-four state-of-the-art methods on three benchmark datasets.
The D4/D8 Model and Holographic QCD

As a top-down holographic approach, the D4/D8 model is expected to be a holographic version of QCD, since it almost includes all the elementary features of QCD based on string theory. In this manuscript, we review the fundamental properties of the D4/D8 model with respect to the D4-brane background and the embedding of the flavor branes, holographic quark, gluon, meson, baryon and glueball with various symmetries; then, we take a look at some interesting applications and developments based on this model.
Fast and reliable entanglement distribution with quantum repeaters: Principles for improving protocols using reinforcement learning

Future quantum technologies such as quantum communication, quantum sensing, and distributed quantum computation, will rely on networks of shared entanglement between spatially separated nodes. In this work, we provide improved protocols/policies for entanglement distribution along a linear chain of nodes, both homogeneous and inhomogeneous, that take practical limitations such as photon losses, non-ideal measurements, and quantum memories with short coherence times into account. For a wide range of parameters, our policies improve upon previously known policies, such as the"swap-as-soon-as-possible"policy, with respect to both the waiting time and the fidelity of the end-to-end entanglement. This improvement is greatest for the most practically relevant cases, namely, for short coherence times, high link losses, and highly asymmetric links. To obtain our results, we model entanglement distribution using a Markov decision process, and then we use the Q-learning reinforcement learning (RL) algorithm to discover new policies. These new policies are characterized by dynamic, state-dependent memory cutoffs and collaboration between the nodes. In particular, we quantify this collaboration between the nodes. Our quantifiers tell us how much"global"knowledge of the network every node has. Finally, our understanding of the performance of large quantum networks is currently limited by the computational inefficiency of simulating them using RL or other optimization methods. Thus, in this work, we present a method for nesting policies in order to obtain policies for large repeater chains. By nesting our RL-based policies for small repeater chains, we obtain policies for large repeater chains that improve upon the swap-as-soon-as-possible policy, and thus we pave the way for a scalable method for obtaining policies for long-distance entanglement distribution.
Ultrasonic micromotors

Initial work is presented on an ultrasonic micromotor. Small polysilicon blocks were both translated and rotated using traveling flexural waves in thin membranes. The acoustic wavelength was typically 100 mu m. The interdigital transducer voltage was typically 5 V; motion was observed at as low as 1.5 V. The acoustic wave power was about 1 mW. Linear motion quickly moved the blocks off of the 3-mm by 8-mm membrane region, but rotational motion was quite stable. Three possible mechanisms for energy transfer from the acoustic wave to the blocks were examined. Preliminary results in air and in vacuum indicate that a fluid boundary layer is responsible for the energy transfer. <<ETX>>