Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs

Biological nervous systems are created in a fundamentally different way than current artificial neural networks. Despite its impressive results in a variety of different domains, deep learning often requires considerable engineering effort to design high-performing neural architectures. By contrast, biological nervous systems are grown through a dynamic self-organizing process. In this paper, we take initial steps toward neural networks that grow through a developmental process that mirrors key properties of embryonic development in biological organisms. The growth process is guided by another neural network, which we call a Neural Developmental Program (NDP) and which operates through local communication alone. We investigate the role of neural growth on different machine learning benchmarks and different optimization methods (evolutionary training, online RL, offline RL, and supervised learning). Additionally, we highlight future research directions and opportunities enabled by having self-organization driving the growth of neural networks.
Field-induced nucleation in threshold switching characteristics of electrochemical metallization devices

In this research, we investigate electrically driven threshold switching (TS) characteristics in electrochemical metallization cells by adopting the field-induced nucleation theory. For this aim, Ag/HfO2 and Ag/TiO2 based TS devices are prepared and examined. First, we carry out the field driven turn-on process to form Ag filaments created as a consequence of sequential nucleation of Ag ions from the bottom electrode. During the filament formation process, it is observed that the prepared devices show switching time exponential in voltage and temperature with different nucleation barrier energies (W0), which confirms the field-induced nucleation theory. Furthermore, we find that the device with higher W0 shows faster dissolution speed. This implies that the slow turn-off speed of the TS device can be improved by finding a material system with a higher W0 value.
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have been widely applied to reduce the memory footprint of LLMs as well as increasing the inference rate. However, a major challenge is that low-bit quantization methods often lead to performance degradation. It is important to understand how quantization impacts the capacity of LLMs. Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models. Specially, we examine the abilities of in-context learning, chain-of-thought reasoning, and instruction-following in quantized LLMs. Our empirical experiments show that these emergent abilities still exist in 4-bit quantization models, while 2-bit models encounter severe performance degradation on the test of these abilities. To improve the performance of low-bit models, we conduct two special experiments: (1) fine-gained impact analysis that studies which components (or substructures) are more sensitive to quantization, and (2) performance compensation through model fine-tuning. Our work derives a series of important findings to understand the impact of quantization on emergent abilities, and sheds lights on the possibilities of extremely low-bit quantization for LLMs.
Opinion mining using Double Channel CNN for Recommender System

Much unstructured data has been produced with the growth of the Internet and social media. A significant volume of textual data includes users' opinions about products in online stores and social media. By exploring and categorizing them, helpful information can be acquired, including customer satisfaction, user feedback about a particular event, predicting the sale of a specific product, and other similar cases. In this paper, we present an approach for sentiment analysis with a deep learning model and use it to recommend products. A two-channel convolutional neural network model has been used for opinion mining, which has five layers and extracts essential features from the data. We increased the number of comments by applying the SMOTE algorithm to the initial dataset and balanced the data. Then we proceed to cluster the aspects. We also assign a weight to each cluster using tensor decomposition algorithms that improve the recommender system's performance. Our proposed method has reached 91.6% accuracy, significantly improved compared to previous aspect-based approaches.
Decentralized Local Updates with Dual-Slow Estimation and Momentum-based Variance-Reduction for Non-Convex Optimization

Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require each node to perform multiple update steps on the parameters of the local model before communicating with others. However, most existing methods could be highly sensitive to data heterogeneity (i.e., non-iid data distribution) and adversely affected by the stochastic gradient noise. In this paper, we propose DSE-MVR to address these problems.Specifically, DSE-MVR introduces a dual-slow estimation strategy that utilizes the gradient tracking technique to estimate the global accumulated update direction for handling the data heterogeneity problem; also for stochastic noise, the method uses the mini-batch momentum-based variance-reduction technique.We theoretically prove that DSE-MVR can achieve optimal convergence results for general non-convex optimization in both iid and non-iid data distribution settings. In particular, the leading terms in the convergence rates derived by DSE-MVR are independent of the stochastic noise for large-batches or large partial average intervals (i.e., the number of local update steps). Further, we put forward DSE-SGD and theoretically justify the importance of the dual-slow estimation strategy in the data heterogeneity setting. Finally, we conduct extensive experiments to show the superiority of DSE-MVR against other state-of-the-art approaches.
Robotic Exploration for Mapping

Robotic Exploration has evolved rapidly in the past two decades as new and more complex techniques have been created to explore unknown regions efficiently. Exciting advancements in exploration, autonomous navigation, and sensor technology have created opportunities for robots to be utilized in new environments and for new objectives ranging from mapping of abandon mines and deep oceans to the efficient creation of indoor models for navigation and search. In this paper we present and discuss a number of examples in research literature of these recent advancements, specifically focusing on robotic exploration algorithms for unmanned vehicles.
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning

Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering. Despite recent advancements, challenges inherent to HAR data, such as noisy data, intra-class variability and inter-class similarity, remain. To address these challenges, we propose an ensemble method, called randomHAR. The general idea behind randomHAR is training a series of deep learning models with the same architecture on randomly selected sensor data from the given dataset. Besides, an agent is trained with the reinforcement learning algorithm to identify the optimal subset of the trained models that are utilized for runtime prediction. In contrast to existing work, this approach optimizes the ensemble process rather than the architecture of the constituent models. To assess the performance of the approach, we compare it against two HAR algorithms, including the current state of the art, on six HAR benchmark datasets. The result of the experiment demonstrates that the proposed approach outperforms the state-of-the-art method, ensembleLSTM.
How time window influences biometrics performance: an EEG-based fingerprints connectivity study

EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results have shown a pronounced effect of the time window on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase of the biometric performance with an increase of the time window. In conclusion, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as EEG fingerprint and that, in this context, it is very important to define a sufficient time window able to collect the subject specific features. Moreover, our preliminary results show that extending the window size beyond a certain maximum does not improve biometric systems' performance.
Metadata-based Malware Detection on Android using Machine Learning

In the digitized world, smartphones and their apps play an important role. To name just a few examples, some apps offer possibilities for entertainment, others for online banking, and others offer support for two-factor authentication. Therefore, with smartphones also, sensitive information is shared; thus, they are a desirable target for malware. The following technical report gives an overview of how machine learning, especially neural networks, can be employed to detect malicious Android apps based on their metadata. Detection based on the metadata is necessary since not all of an app's information is readable from another app due to the security layout of Android. To do so, a comparable big dataset of metadata of apps has been collected for learning and evaluation in this work. The first section, after the introduction, presents the related work, followed by the description of the sources of the dataset and the selection of the features used for machine learning, in this case, only the app permissions. Afterward, a free available dataset is used to find an efficient and effective neural network model for learning and evaluation. Here, the fully connected network type consisting of dense layers is chosen. Then this model is trained and evaluated on the new, more extensive dataset to obtain a representative result. It turns out that this model detects malware with an accuracy of 92.93% based on an app's permissions.
Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging

We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart from 2D sparse cardiac magnetic resonance (CMR) imaging data. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions and are capable of deforming globally and locally. While global geometric parameter functions and deformations capture gross shape features from visual data, local deformations, parameterized as neural diffeomorphic point flows, can be learned to recover the detailed heart shape. Different from iterative optimization methods used in conventional deformable model formulations, NDMs can be trained to learn such geometric parameter functions, global and local deformations from a shape distribution manifold. Our NDM can learn to densify a sparse cardiac point cloud with arbitrary scales and generate high-quality triangular meshes automatically. It also enables the implicit learning of dense correspondences among different heart shape instances for accurate cardiac shape registration. Furthermore, the parameters of NDM are intuitive, and can be used by a physician without sophisticated post-processing. Experimental results on a large CMR dataset demonstrate the improved performance of NDM over conventional methods.
Tight Lower Bounds for Weighted Matroid Problems

In this paper we derive tight lower bounds resolving the hardness status of several fundamental weighted matroid problems. One notable example is budgeted matroid independent set, for which we show there is no fully polynomial-time approximation scheme (FPTAS), indicating the Efficient PTAS of [Doron-Arad, Kulik and Shachnai, SOSA 2023] is the best possible. Furthermore, we show that there is no pseudo-polynomial time algorithm for exact weight matroid independent set, implying the algorithm of [Camerini, Galbiati and Maffioli, J. Algorithms 1992] for representable matroids cannot be generalized to arbitrary matroids. Similarly, we show there is no Fully PTAS for constrained minimum basis of a matroid and knapsack cover with a matroid, implying the existing Efficient PTAS for the former is optimal. For all of the above problems, we obtain unconditional lower bounds in the oracle model, where the independent sets of the matroid can be accessed only via a membership oracle. We complement these results by showing that the same lower bounds hold under standard complexity assumptions, even if the matroid is encoded as part of the instance. All of our bounds are based on a specifically structured family of paving matroids.
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans

Plant breeders and agricultural researchers can increase crop productivity by identifying desirable features, disease resistance, and nutritional content by analysing the Dry Bean dataset. This study analyses and compares different Support Vector Machine (SVM) classification algorithms, namely linear, polynomial, and radial basis function (RBF), along with other popular classification algorithms. The analysis is performed on the Dry Bean Dataset, with PCA (Principal Component Analysis) conducted as a preprocessing step for dimensionality reduction. The primary evaluation metric used is accuracy, and the RBF SVM kernel algorithm achieves the highest Accuracy of 93.34%, Precision of 92.61%, Recall of 92.35% and F1 Score as 91.40%. Along with adept visualization and empirical analysis, this study offers valuable guidance by emphasizing the importance of considering different SVM algorithms for complex and non-linear structured datasets.
Global convergence of a BFGS-type algorithm for nonconvex multiobjective optimization problems

We propose a modified BFGS algorithm for multiobjective optimization problems with global convergence, even in the absence of convexity assumptions on the objective functions. Furthermore, we establish the superlinear convergence of the method under usual conditions. Our approach employs Wolfe step sizes and ensures that the Hessian approximations are updated and corrected at each iteration to address the lack of convexity assumption. Numerical results shows that the introduced modifications preserve the practical efficiency of the BFGS method.
A Case for VR Briefings: Comparing Communication in Daily Audio and VR Mission Control in a Simulated Lunar Mission

Alpha-XR Mission conducted by XR Lab PJAIT focused on research related to individual and crew well-being and participatory team collaboration in ICE (isolated, confined and extreme) conditions. In this two-week mission within an analog space habitat, collaboration, objective execution and leisure was facilitated and studied by virtual reality (VR) tools. The mission commander and first officer, both experienced with virtual reality, took part in daily briefings with mission control. In the first week the briefings were voice-only conducted via a channel on Discord. During the following week last briefings were conducted in VR, using Horizon Workrooms. This qualitative pilot study employing participatory observation revealed that VR facilitates communication, especially on complex problems and experiences, providing the sense of emotional connection and shared understanding, that may be lacking in audio calls. The study points to the need to further explore VR-facilitated communication in high-stake environments as it may improve relationships, well-being, and communication outcomes.
Decomposition and Reconstruction Algorithms for Framelet Packets

Wavelet packets based on orthonormal wavelet bases have been well studied in theory and applications, since they can provide adaptive choice from a library of wavelet bases for a wide range of practically oriented tasks. But the study of wavelet frame packet have been less involved. In this paper, we give the decomposition and reconstruction algorithms for framelet packets constructed from the unitary extension given by Ron and Shen.
Inversion of two new circulant matrices over Zm

In this paper we consider the problem of inverting an n×n RSFMLR circulant matrix with entries over Zm. We present two different algorithms. Our algorithms require different degrees of knowledge of m and n, and their costs range, from n log n loglog n to n log2 n loglog n log m operations over Zm. Moreover, for each algorithm we give the cost in terms of bit operations. Finally, the extended algorithms is used to solve the problem of inverting RSFMLR circulant matrices over Zm.
Network Meta-analysis of Combined Strength and Power Training for Countermovement Jump Height

Abstract Combined strength and power training in a training program is considered to improve the vertical jump, which is frequently quantified using the countermovement jump height. It is not yet clear whether one of the different training set structures, such as complex training, contrast training, compound training and traditional training, is superior to another. The aim of this review is to describe and assess the comparative effects of the set structures on countermovement jump height in healthy subjects. A systematic review and network meta-analysis (NMA) was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Network Meta-Analyses. Three databases were systematically searched. Risk of bias was assessed using the Risk of Bias 2 tool. NMAs were performed using a random-effects model. Twenty-four studies were included. All interventions were superior to control (no intervention) with mean differences ranging from 2.87 [95% confidence interval (CI): 1.99 to 3.74] for complex training to 3.43 (95% CI: 2.61 to 4.26) for traditional training. None of the training interventions were superior compared to each other in strength and/or power trained subjects, as well as in non-strength and/or power trained subjects. The ﬁndings support the combination of strength and power training to improve countermovement jump height.
The Arabinose 5-Phosphate Isomerase KdsD Is Required for Virulence in Burkholderia pseudomallei

The intrinsic resistance of B. pseudomallei to many antibiotics complicates treatment. This opportunistic pathogen possesses a wide range of virulence factors, resulting in severe and potentially fatal disease. ABSTRACT Burkholderia pseudomallei is the causative agent of melioidosis, which is endemic primarily in Southeast Asia and northern Australia but is increasingly being seen in other tropical and subtropical regions of the world. Melioidosis is associated with high morbidity and mortality rates, which is mediated by the wide range of virulence factors encoded by B. pseudomallei. These virulence determinants include surface polysaccharides such as lipopolysaccharide (LPS) and capsular polysaccharides (CPS). Here, we investigated a predicted arabinose-5-phosphate isomerase (API) similar to KdsD in B. pseudomallei strain K96243. KdsD is required for the production of the highly conserved 3-deoxy-d-manno-octulosonic acid (Kdo), a key sugar in the core region of LPS. Recombinant KdsD was expressed and purified, and API activity was determined. Although a putative API paralogue (KpsF) is also predicted to be encoded, the deletion of kdsD resulted in growth defects, loss of motility, reduced survival in RAW 264.7 murine macrophages, and attenuation in a BALB/c mouse model of melioidosis. Suppressor mutations were observed during a phenotypic screen for motility, revealing single nucleotide polymorphisms or indels located in the poorly understood CPS type IV cluster. Crucially, suppressor mutations did not result in reversion of attenuation in vivo. This study demonstrates the importance of KdsD for B. pseudomallei virulence and highlights further the complex nature of the polysaccharides it produces. IMPORTANCE The intrinsic resistance of B. pseudomallei to many antibiotics complicates treatment. This opportunistic pathogen possesses a wide range of virulence factors, resulting in severe and potentially fatal disease. Virulence factors as targets for drug development offer an alternative approach to combat pathogenic bacteria. Prior to initiating early drug discovery approaches, it is important to demonstrate that disruption of the target gene will prevent the development of disease. This study highlights the fact that KdsD is crucial for virulence of B. pseudomallei in an animal model of infection and provides supportive phenotypic characterization that builds a foundation for future therapeutic development.
Spatial Variations in Soil Physicochemical Properties and Enzyme Activities in the Low Mountain Area of Helan Mountain

Abstract: The low mountainous area of Helan Mountain has sparse vegetation, severe surface erosion and a fragile ecological environment. In this study, we analyzed surface soils at five different altitudes in the low mountainous area of Helan Mountain to determine their basic physical and chemical properties. the activities of four common soil enzymes and their correlations. The results showed that with increasing altitude, the soil moisture content and total porosity showed upward trends, while bulk density, organic matter and available phosphorus showed downward trends, and TC, TN and alkali-hydrolyzed nitrogen showed trends that were first decreasing and then increasing. As the altitude increased, the carbon-nitrogen ratio increased and showed a trend of first increasing and then decreasing, while soil urease and alkaline phosphatase activities showed the opposite trends that were first decreasing and then increasing, and sucrase and catalase showed steadily increasing trends. Urease activity and phosphatase activity were significantly positively correlated with soil total nitrogen. Previous studies had also found differences in various physical and chemical property indexes and enzyme activities of surface soil at different altitudes, and that the physical and chemical properties of soil and enzyme activities affect each other.