Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-scale enhancement and aggregation network for single image deraining

Rain streaks in an image will appear with different sizes and orientations, resulting in severe blurring and visual quality degradation. Previous CNN-based algorithms achieved encouraging derained results, while there are certain limitations in the description of rain streaks and the restoration of scene structure in different environments. In this paper, we propose an efﬁ-cient multi-scale enhancement and aggregation network (MEAN) to solve the single image deraining problem. Speciﬁcally, considering the importance of large receptive ﬁelds and multi-scale features for depicting rain, we introduce a multi-scale enhanced unit (MEU) to capture long-range dependencies and exploit features at different scales. Simultaneously, an attentive aggregation unit (AAU) is designed to utilize the informative features on spacial and channel dimensions, and aggregate effective information to eliminate redundant features for rich scenario details. To improve the deraining performance of the encoder-decoder network, we utilize AAU to ﬁl-ter the information in the encoder network and concatenate useful features to the decoder network, which is conducive to predicting high-quality clean and rain-free images. Experiments results on synthetic datasets and real-world samples show that our method achieves signiﬁcant deraining performance compared to state-of-the-art approaches.
ROBUST SOUND EVENT DETECTION IN REAL WORLD ENVIRONMENT

Ali Gohar: Robust Sound Event Detection in Real World Environment Master of Science Thesis Tampere University Master in Information Technology November 2020 Sound present in our daily life contains very useful information and can help in solving multiple problems. Sound event detection (SED) in real life can be used to build products for security, safety and human convenience. Deep neural networks are quite common and perform very well for such tasks. A lot of work has already been done using deep neural networks and recurrent neural networks to tackle various problems. Considering the informative events for nurses in home care centers and nursing homes, we finalized four target classes (door, water tap, yelling, human fall) to be focused in this thesis. These sound events can also be used in other places e.g home security and public places. Considering other frequent sounds in the daily environment, we took speech, music and coughing as non-target classes. Despite four target classes all other sounds will be considered as background noise and will be ignored. This thesis aims to synthesize a dataset containing all these sound events and train various models to achieve higher accuracy for target classes. Target and non-target class data along with ambience sounds are collected from the internet to create real-life soundscapes. Soundscapes are created in four folds to use the cross-validation technique during training of the model. To ensure the privacy of the speaker, we decided to extract audio embeddings from these soundscapes and use them for further process. Three different types of audio embeddings (openl3, kumar2018, zhao2020) are extracted and compared to find the optimal one for this task. Supervised learning acoustic detection models are trained and validated on this synthesized data to find the finest model for these events. We found that embeddings extracted from Zhao2020 and provided to various classification models outperform the other two types of embeddings extraction models on our synthetic dataset. We achieved the best results by combining a recurrent neural network with max polling.
03 Verma_7043_JLR_Trans

My objective is to advance our understanding of union effects by examining what unions do to managerial practices in the workplace. Unions can be an instrument of social change, but even when they play a larger role in society, their core activity remains in the workplace. Their principal engagement is with management, though their actions may extend to lobbying, politics, and the community at both local and international levels. Therefore, in considering the question, “What do unions do to the workplace? ,” it is important to examine how unions affect management, in general, and human resource management (HRM), in particular.
Assessing the Correlation between the Socioeconomic Level and Cognitive Function among Older Adults in Egypt

APA Citation AbouGabal, M. (2023). Assessing the Correlation between the Socioeconomic Level and Cognitive Function among Older Adults in Egypt [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/2132 MLA Citation AbouGabal, Macy. Assessing the Correlation between the Socioeconomic Level and Cognitive Function among Older Adults in Egypt. 2023. American University in Cairo, Master's Thesis. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/2132
Effect of cropping systems induced shading on the physiology of cowpea varieties intercropped with sorghum based systems in the savannah ecology of Nigeria

Six cowpea varieties (IT98K-131-1, IT04K-227-4, IT89KD-288, IT93K-452-1, IT97K-499-35 and IT99K-573-1-1) were intercropped with a local sorghum (Kaura) using four row arrangements (1C:1S, 2C:2S, Mixed crop and sole) in fields experiment conducted at Minjibir and research farm of the Faculty of Agriculture Bayero University Kano located in the Sudan Savannah ecological zone of Nigeria during the 2015 and 2016 rainy seasons. In the field, the experiment was laid in a randomized complete block design (RCBD), Analysis of variance (ANOVA) showed significant difference on cowpea cropping systems and variety on the physiology of cowpea. At BUK in both years and the combined, cowpea had higher non significant percent light interception at sole, at both sampling period’s varieties IT98K-131-1 and IT93K-452-1 intercepted higher percent light compared with the other varieties similarly chlorophyll content was higher at sole, varieties IT97K-499-35, IT04K-227-4 and IT98K-131-1 had significantly higher chlorophyll content. At Minjibir in both years and the combined, sole row arrangement had the higher non significant percent light interception while variety IT89KD-288 had no significant higher light interception. In both trials, higher percent light interception was recorded in variety IT98K-131-1 and varieties IT04K-227-4 and IT97K-499- 35 had a significant combined effect on chlorophyll content. Keywords: Cropping systems, Cowpea varieties, Shading and physiology
Newton Nonholonomic Source Seeking for Distance-Dependent Maps

The topics of source seeking and Newton-based extremum seeking have flourished, independently, but never combined. We present the first Newton-based source seeking algorithm. The algorithm employs forward velocity tuning, as in the very first source seeker for the unicycle, and incorporates an additional Riccati filter for inverting the Hessian inverse and feeding it into the demodulation signal. Using second-order Lie bracket averaging, we prove convergence to the source at a rate that is independent of the unknown Hessian of the map. The result is semiglobal and practical, for a map that is quadratic in the distance from the source. The paper presents a theory and simulations, which show advantage of the Newton-based over the gradient-based source seeking.
VarSim: A Fast Process Variation-aware Thermal Modeling Methodology Using Green's Functions

Despite temperature rise being a first-order design constraint, traditional thermal estimation techniques have severe limitations in modeling critical aspects affecting the temperature in modern-day chips. Existing thermal modeling techniques often ignore the effects of parameter variation, which can lead to significant errors. Such methods also ignore the dependence of conductivity on temperature and its variation. Leakage power is also incorporated inadequately by state-of-the-art techniques. Thermal modeling is a process that has to be repeated at least thousands of times in the design cycle, and hence speed is of utmost importance. To overcome these limitations, we propose VarSim, an ultrafast thermal simulator based on Green's functions. Green's functions have been shown to be faster than the traditional finite difference and finite element-based approaches but have rarely been employed in thermal modeling. Hence we propose a new Green's function-based method to capture the effects of leakage power as well as process variation analytically. We provide a closed-form solution for the Green's function considering the effects of variation on the process, temperature, and thermal conductivity. In addition, we propose a novel way of dealing with the anisotropicity introduced by process variation by splitting the Green's functions into shift-variant and shift-invariant components. Since our solutions are analytical expressions, we were able to obtain speedups that were several orders of magnitude over and above state-of-the-art proposals with a mean absolute error limited to 4% for a wide range of test cases. Furthermore, our method accurately captures the steady-state as well as the transient variation in temperature.
Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networks

To learn accurate representations of molecules, it is essential to consider both chemical and geometric features. To encode geometric information, many descriptors have been proposed in constrained circumstances for specific types of molecules and do not have the properties to be ``robust": 1. Invariant to rotations and translations; 2. Injective when embedding molecular structures. In this work, we propose a universal and robust Directional Node Pair (DNP) descriptor based on the graph representations of 3D molecules. Our DNP descriptor is robust compared to previous ones and can be applied to multiple molecular types. To combine the DNP descriptor and chemical features in molecules, we construct the Robust Molecular Graph Convolutional Network (RoM-GCN) which is capable to take both node and edge features into consideration when generating molecule representations. We evaluate our model on protein and small molecule datasets. Our results validate the superiority of the DNP descriptor in incorporating 3D geometric information of molecules. RoM-GCN outperforms all compared baselines.
Software Code Quality Measurement: Implications from Metric Distributions

Software code quality is a construct with three dimensions: maintainability, reliability, and functionality. Although many firms have incorporated code quality metrics in their operations, evaluating these metrics still lacks consistent standards. We categorized distinct metrics into two types: 1) monotonic metrics that consistently influence code quality; and 2) non-monotonic metrics that lack a consistent relationship with code quality. To consistently evaluate them, we proposed a distribution-based method to get metric scores. Our empirical analysis includes 36,460 high-quality open-source software (OSS) repositories and their raw metrics from SonarQube1 and CK2. The evaluated scores demonstrate great explainability on software adoption. Our work contributes to the multidimensional construct of code quality and its metric measurements, which provides practical implications for consistent measurements on both monotonic and non-monotonic metrics.1https://www.sonarsource. com2https://github.com/mauricioaniche/ck
Experimental investigation on hole quality in drilling of composite pipes

In recent years, the major reason for the rejection of composite pipes in industrial applications is due to the poor quality of the drilled hole. This paper investigates the effect of drilling process parameters on the hole quality in composite pipes made of an aluminium core surrounded by polyethylene layers. An empirical model is designed for the two input variables using response surface methodology (central composite design). An experimental investigation is carried out to study the effect of spindle speed and feed rate on quality of drilled holes, especially circularity error. It is observed that a moderate spindle speed and low feed rate are most effective in minimizing the circularity error. Microstructural investigation of drilled hole surface is also carried out using scanning electron microscopy (SEM).
CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification

In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. To overcome this challenge, mixing-based methods have emerged as effective alternatives to traditional preprocessing data augmentation techniques. Nonetheless, these methods fail to fully consider the unique features of pathology images, such as local specificity, global distribution, and inner/outer-sample instance relationships. To better comprehend these characteristics and create valuable pseudo samples, we propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. By dividing images into patches based on the granularity of pathology instances and shuffling them within the same batch, the absolute relationships between instances can be effectively preserved when generating new samples. Moreover, we develop a curriculum learning-inspired, loss-driven strategy to handle perturbations and distribution-related noise during training, enabling the model to adaptively fit the augmented data. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets. This innovative instance relationship-centered method has the potential to inform general data augmentation approaches for pathology image classification. The associated codes are available at https://github.com/sagizty/CellMix.
Random directed graph distributions and the triad census in social networks

This paper uses the concept of the triad census as, developed by Holland and Leinhardt, and describes several distributions on directed graphs. Methods are presented for calculating the mean and the covariance matrix of the triad census for the uniform distribution that conditions on the number of choices made by each individual in the social network. Several complex distributions on digraphs are approximated, and an application of these methods to a sociogram is given.
Sign regularity: Refinement of Gantmacher-Krein-Motzkin results on variation diminution and the linear preserver problem

Variation diminution (VD) is a fundamental property in total positivity theory, first studied by Fekete-P\'olya (1912) for one sided P\'olya frequency sequences, followed by Schoenberg (1930), and by Motzkin (1936) who characterized sign regular (SR) matrices using VD and some rank hypotheses. A classical theorem in 1950 by Gantmacher-Krein characterized the strictly sign regular (SSR) $m \times n$ matrices for $m>n$ using this property. In this article we strengthen their result by characterizing all $m \times n$ SSR matrices using VD. We further characterize strict sign regularity of a given sign pattern in terms of VD together with a natural condition motivated by total positivity. We then refine Motzkin's characterization of SR matrices by omitting the rank condition and specifying the sign pattern. More strongly, these characterizations employ single test vectors with alternating sign coordinates - i.e., lying in an alternating bi-orthant. The second contribution of our work includes the study of linear preservers of SR and SSR matrices. The linear preserver problem is an important question in matrix theory and operator theory. We classify all linear mappings $\mathcal{L}:\mathbb{R}^{m\times n}\to\mathbb{R}^{m\times n}$ that preserve: (i) sign regularity and (ii) sign regularity with a given sign pattern, as well as strict versions of these.
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics; (ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines.
Estimating speaker direction on a humanoid robot with binaural acoustic signals

To achieve human-like behaviour during speech interactions, it is necessary for a humanoid robot to estimate the location of a human talker. Here, we present a method to optimize the parameters used for the direction of arrival (DOA) estimation, while also considering real-time applications for human-robot interaction scenarios. This method is applied to binaural sound source localization framework on a humanoid robotic head. Real data is collected and annotated for this work. Optimizations are performed via a brute force method and a Bayesian model based method, results are validated and discussed, and effects on latency for real-time use are also explored.
A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple similar ARO instances in advance using the column and constraint generation algorithm and extract the optimal strategies to generate a training set. We train a machine learning model that predicts high-quality strategies for the here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the wait-and-see decisions. We also introduce an algorithm to reduce the number of different target classes the machine learning algorithm needs to be trained on. We apply the proposed approach to the facility location, the multi-item inventory control and the unit commitment problems. Our approach solves ARO problems drastically faster than the state-of-the-art algorithms with high accuracy.
Model Compression Methods for YOLOv5: A Review

Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and efficiency. While the evident merits of YOLO have yielded to its extensive use in many areas, deploying it on resource-limited devices poses challenges. To address this issue, various neural network compression methods have been developed, which fall under three main categories, namely network pruning, quantization, and knowledge distillation. The fruitful outcomes of utilizing model compression methods, such as lowering memory usage and inference time, make them favorable, if not necessary, for deploying large neural networks on hardware-constrained edge devices. In this review paper, our focus is on pruning and quantization due to their comparative modularity. We categorize them and analyze the practical results of applying those methods to YOLOv5. By doing so, we identify gaps in adapting pruning and quantization for compressing YOLOv5, and provide future directions in this area for further exploration. Among several versions of YOLO, we specifically choose YOLOv5 for its excellent trade-off between recency and popularity in literature. This is the first specific review paper that surveys pruning and quantization methods from an implementation point of view on YOLOv5. Our study is also extendable to newer versions of YOLO as implementing them on resource-limited devices poses the same challenges that persist even today. This paper targets those interested in the practical deployment of model compression methods on YOLOv5, and in exploring different compression techniques that can be used for subsequent versions of YOLO.
Characterising the Hierarchy of Multi-time Quantum Processes with Classical Memory

Memory is the fundamental form of temporal complexity: when present but uncontrollable, it manifests as non-Markovian noise; conversely, if controllable, memory can be a powerful resource for information processing. Memory effects arise from/are transmitted via interactions between a system and its environment; as such, they can be either classical or quantum in nature. From a practical standpoint, quantum processes with classical memory promise near-term applicability: they are more powerful than their memoryless counterpart, yet at the same time can be controlled over significant timeframes without being spoiled by decoherence. However, despite practical and foundational value, apart from simple two-time scenarios, the distinction between quantum and classical memory remains unexplored. We first analyse various physically-motivated candidates regarding a suitable definition for classical memory that lead to remarkably distinct phenomena in the multi-time setting. Subsequently, we systematically characterise the hierarchy of multi-time memory effects in quantum mechanics, many levels of which collapse in the two-time setting, thereby making our results genuinely multi-time phenomena.
RICo: Rotate-Inpaint-Complete for Generalizable Scene Reconstruction

General scene reconstruction refers to the task of estimating the full 3D geometry and texture of a scene containing previously unseen objects. In many practical applications such as AR/VR, autonomous navigation, and robotics, only a single view of the scene may be available, making the scene reconstruction task challenging. In this paper, we present a method for scene reconstruction by structurally breaking the problem into two steps: rendering novel views via inpainting and 2D to 3D scene lifting. Specifically, we leverage the generalization capability of large visual language models (Dalle-2) to inpaint the missing areas of scene color images rendered from different views. Next, we lift these inpainted images to 3D by predicting normals of the inpainted image and solving for the missing depth values. By predicting for normals instead of depth directly, our method allows for robustness to changes in depth distributions and scale. With rigorous quantitative evaluation, we show that our method outperforms multiple baselines while providing generalization to novel objects and scenes.
Bio-realistic Neural Network Implementation on Loihi 2 with Izhikevich Neurons

In this paper, we presented a bio-realistic basal ganglia neural network and its integration into Intel's Loihi neuromorphic processor to perform simple Go/No-Go task. To incorporate more bio-realistic and diverse set of neuron dynamics, we used Izhikevich neuron model, implemented as microcode, instead of Leaky-Integrate and Fire (LIF) neuron model that has built-in support on Loihi. This work aims to demonstrate the feasibility of implementing computationally efficient custom neuron models on Loihi for building spiking neural networks (SNNs) that features these custom neurons to realize bio-realistic neural networks.