Miracle-dz/UniFAR-FTUs huggingface dataset:
BAL IMAGES ANALYSIS FOR THEIR AUTOMATIC QUANTIFICATION

Bronchoalveolar lavage (BAL) images are a medical test from which different pathologies can be extracted based on their cellular distribution. In the hospital La Vall d’Hebron technicians make manual count to determine this cell distribution. We did not have any labelled BAL image, because this was the first contact with the problem. In this project, maxtree-based solution is used to do the first segmentation, and then, a correcting process is carried out in order to correctly label 56 BAL images. With this small dataset, and taking into account the maxtree results in some aspects were not as good as we expected, we decided to test and train a CNN based in the U-Net. We applied specific techniques for small datasets and we tested different parametrizations of the network. Finally, we obtain 70% IoU global score approx. in the validation of the CNN.
Drug Wars, Organized Crime Expansion, and State Capture: Evidence from Mexico

Scholars have long argued that organized crime expansion is extremely difficult and rare since these groups rely on local networks and are unable to operate without some degree of state protection, which they often lack outside their historical strongholds. Yet, Latin America is currently experiencing significant criminal expansion. Why do criminal organizations expand beyond their historical strongholds and what explains where they go? Moreover, what are the implications for democratic accountability and citizen well-being? I argue that government interventions in the drug market can create incentives for drug cartels to diversify their activities and expand to territories with lucrative new business opportunities. Cartels seek to gain state protection when entering new political jurisdictions by exploiting electoral cycles to capture local politicians, allowing them to operate with impunity and worsening citizen well-being. To test my theory, I first provide evidence that Mexico’s War on Drugs of 2007 pushed specialized drug cartels to diversify, and particularly, that all cartels began stealing oil from pipelines. To analyze patterns of expansion, I use a novel dataset on the geographic presence of cartels in Mexico and leverage a temporal shock (War on Drugs) and geography (location of new business opportunities). I show that the crackdown pushed cartels to strategically expand to territories with oil pipelines, and that cartels entering new territories reduced electoral competition and increased violence and crime. Finally, I illustrate state capture through a case study of a municipality that saw cartel intrusion following the crackdown. ∗I thank Sebastian Saiegh, Simeon Nichter, Javier Osorio, Lucia Tiscornia, Dorothy Kronick, Francisco Cantu, Federica Izzo, Rafael Fernandez de Castro, Hannah Baron, Jessica Zarkin, Elena Barham, Kaitlyn Chriswell, Oscar Pocasangre, Megan Erickson, and Adee Weller for their feedback on earlier drafts of the article. I also thank the participants of the UCSD political science workshop, the Illicit Economies and ExtraLegal Actors Conference, the Latin American Peace Science Society Conference, and the 2021-22 fellows at the Center for U.S.-Mexican Studies.
Retirement Behavior of Married Couples in Germany: Evidence from a Natural Experiment

This paper studies the effect of the exogenous introduction of a pension reform in 1992 as a “natural experiment” on retirement behavior among married couples in Germany, using the German Social Economic Panel data. In this reform retirement ages for full pensions of the unemployed, the long-term insured and women have been increased and early retirement was affected by reduced pensions. This reform had a financial impact on individuals at ages 60 and 63. Using a difference-in-difference approach, cohorts retiring after the reform (treatment group) are compared to cohorts retiring before the reform (control group) and differences in the share of retired person in the age groups 60 and 63 to other ages are analyzed. The estimation results show that the financial disincentives of the reform reduce significantly the retirement probability of women at age 60 by 23 percentage points (pp) and the retirement probability of men at the ages of 60 and 63 by 10 pp and 18 pp, respectively. The financial disincentives of the reform reduce significantly stronger the retirement probability of women and men at age 60 in East Germany than in the West. Additionally the reform has a significant impact on women in the East and men in the West to retire at age 63. Furthermore the study investigates the financial disincentives to retire for husbands and wives by the pension reduction of their partners. The estimation shows significant effects of financial constraints of wives on the retirement decisions of their husbands but not vice versa. After controlling for the own financial incentives and additional covariates, husbands are still responsive to the wives’ financial disincentives but wives are not. The paper also indicates that women and men in East Germany with possible lower wages, earnings and wealth responds strongly to their own financial disincentives and their wives’ financial constraints, compared to their counterparts in West Germany. The reaction of men in East Germany is much stronger to their wives’ financial constraints than that of men in West Germany whose wives are exposed to the reform.
Additive Manufacturing of Stretchable Strain Sensors: Fabrication, Optimization and Application

APA Citation Bastawrous, J. N. (2023). Additive Manufacturing of Stretchable Strain Sensors: Fabrication, Optimization and Application [Master's Thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/2073 MLA Citation Bastawrous, John Nady Shihat. Additive Manufacturing of Stretchable Strain Sensors: Fabrication, Optimization and Application. 2023. American University in Cairo, Master's Thesis. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/2073
csgl Mutants and Encodes a P-type ATPase Belonging to the Cu2+-ATPase Subfamily

We have isolated, sequenced, mapped and disrupted a gene, CCC2, from Saccharomyces cerevisiae. This gene displays non-allelic complementation of the Ca2’-sensitive phenotype conferred by the csgl mutation. Analysis of the CCC2p amino acid sequence reveals that it encodes a member of the P-type ATPase family and is most similar to a subfamily thought to consist of Cu2+ transporters, including the human genes that mutate to cause Wilson disease and Menkes disease. The ability of this gene, in two or more copies, to reverse the csgl defect suggests that Ca2’-induced death of csgl mutant cells is related to Cu2+ metabolism. Cells without CCC2 require increased Cu” concentrations for growth. Therefore CCC2p may function to provide Cu2+ to a cellular compartment rather than in removal of excess Cu2+. The sequence of CCC2 is available through GenBank under accession number L36317.
Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.
Schema-Driven Actionable Insight Generation and Smart Recommendation

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a schema-driven method to generate actionable insights from data to drive growth and change. It also introduces a technique to rank the insights to align with user interests based on their feedback. We show preliminary qualitative results of the insights generated using our technique and demonstrate its ability to adapt to feedback.
Non-compact Einstein manifolds with unimodular isometry group

We show that a negative Einstein manifold admitting a proper isometric action of a connected unimodular Lie group with compact, possibly singular, orbit space splits isometrically as a product of a symmetric space and a compact negative Einstein manifold. The proof involves the theory of polar actions, Lie-theoretic arguments and maximum principles.
Measuring Gravitational Wave Speed and Lorentz Violation with the First Three Gravitational-Wave Catalogs

The speed of gravitational waves $v_g$ can be measured with the time delay between gravitational-wave detectors. Our study provides a more precise measurement of $v_g$ using gravitational-wave signals only, compared with previous studies. We select 52 gravitational-wave events that were detected with high confidence by at least two detectors in the first three observing runs (O1, O2, and O3) of Advanced LIGO and Advanced Virgo. We use Markov chain Monte Carlo and nested sampling to estimate the $v_g$ posterior distribution for each of those events. We then combine their posterior distributions to find the 90% credible interval of the combined $v_g$ distribution for which we obtain $0.99^{+0.02}_{-0.02}c$ without the use of more accurate sky localization from the electromagnetic signal associated with GW170817. Restricting attention to the 50 binary black hole events generates the same result, while the use of the electromagnetic sky localization for GW170817 gives a tighter constraint of $0.99^{+0.01}_{-0.02}c$. The abundance of gravitational wave events allows us to apply hierarchical Bayesian inference on the posterior samples to simultaneously constrain all nine coefficients for Lorentz violation in the nondispersive, nonbirefringent limit of the gravitational sector of the Standard-Model Extension test framework. We compare the hierarchical Bayesian inference method with other methods of combining limits on Lorentz violation in the gravity sector that are found in the literature.
Integrating processed-based models and machine learning for crop yield prediction

Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult to calibrate for local conditions, or data-driven machine learning methods, which are known to require large datasets. In this work we investigate potato yield prediction using a hybrid meta-modeling approach. A crop growth model is employed to generate synthetic data for (pre)training a convolutional neural net, which is then fine-tuned with observational data. When applied in silico, our meta-modeling approach yields better predictions than a baseline comprising a purely data-driven approach. When tested on real-world data from field trials (n=303) and commercial fields (n=77), the meta-modeling approach yields competitive results with respect to the crop growth model. In the latter set, however, both models perform worse than a simple linear regression with a hand-picked feature set and dedicated preprocessing designed by domain experts. Our findings indicate the potential of meta-modeling for accurate crop yield prediction; however, further advancements and validation using extensive real-world datasets is recommended to solidify its practical effectiveness.
Mittag-Leffler stability of impulsive fractional-order bi-directional associative memory neural networks with time-varying delays

In the paper, a class of impulsive Caputo fractional-order bi-directional associative memory neural networks with time-varying delays is considered. Applying the fractional Lyapunov method, a sufficient condition for Mittag-Leffler stability of the equilibrium point of the system under consideration is derived. Some earlier results are extended and improved. Our results provide an impulsive control law which stabilizes the impulse-free fractional-order neural network time-delay model. An example is provided to demonstrate the effectiveness of the proposed results.
Neck Rupture and Scission Neutrons in Nuclear Fission

Just before a nucleus fissions a neck is formed between the emerging fission fragments. It is widely accepted that this neck undergoes a rather violent rupture, despite no direct experimental evidence, and only a few contentious theoretical treatments of this fission stage were ever performed in the more than eight decades since nuclear fission was experimentally observed by Hahn and Strassmann and described by Meitner and Frisch in 1939. In the same year, Bohr and Wheeler conjectured that the fission of the nuclear liquid drop would likely be accompanied by the rapid formation of tiny droplets, later identified with either scission neutrons or other ternary fission fragments, a process which has not yet been discussed in a fully quantum many-body framework. The main difficulty in addressing both of these stages of nuclear fission is both are highly non-equilibrium processes. Here we will present the first fully microscopic characterization of the scission mechanism, along with the spectrum and the spatial distribution of scission neutrons, and some upper limit estimates for the emission of charged particles.
Mini-PointNetPlus: a local feature descriptor in deep learning model for 3d environment perception

Common deep learning models for 3D environment perception often use pillarization/voxelization methods to convert point cloud data into pillars/voxels and then process it with a 2D/3D convolutional neural network (CNN). The pioneer work PointNet has been widely applied as a local feature descriptor, a fundamental component in deep learning models for 3D perception, to extract features of a point cloud. This is achieved by using a symmetric max-pooling operator which provides unique pillar/voxel features. However, by ignoring most of the points, the max-pooling operator causes an information loss, which reduces the model performance. To address this issue, we propose a novel local feature descriptor, mini-PointNetPlus, as an alternative for plug-and-play to PointNet. Our basic idea is to separately project the data points to the individual features considered, each leading to a permutation invariant. Thus, the proposed descriptor transforms an unordered point cloud to a stable order. The vanilla PointNet is proved to be a special case of our mini-PointNetPlus. Due to fully utilizing the features by the proposed descriptor, we demonstrate in experiment a considerable performance improvement for 3D perception.
Nonlinear effects in manybody van der Waals interactions

Van der Waals interactions are ubiquitous and they play an important role for the stability of materials. Current understanding of this type of coupling is based on linear response theory, while optical nonlinearities are rarely considered in this context. Many materials, however, exhibit strong optical nonlinear response, which prompts further evaluation of dispersive forces beyond linear response. Here we present a $\textit{Discrete Coupled Nonlinear Dipole}$ approach that takes into account linear and nonlinear properties of all dipolar nanoparticles in a given system. This method is based on a Hamiltonian for nonlinear dipoles, which we apply in different systems uncovering a complex interplay of distance, anisotropy, polarizibilities, and hyperpolarizabilities in the vdW energy. This investigation broadens our basic understanding of dispersive interactions, especially in the context of nonlinear materials.
Framework for Automatic PCB Marking Detection and Recognition for Hardware Assurance

A Bill of Materials (BoM) is a list of all components on a printed circuit board (PCB). Since BoMs are useful for hardware assurance, automatic BoM extraction (AutoBoM) is of great interest to the government and electronics industry. To achieve a high-accuracy AutoBoM process, domain knowledge of PCB text and logos must be utilized. In this study, we discuss the challenges associated with automatic PCB marking extraction and propose 1) a plan for collecting salient PCB marking data, and 2) a framework for incorporating this data for automatic PCB assurance. Given the proposed dataset plan and framework, subsequent future work, implications, and open research possibilities are detailed.
Pixelated scintillator-based compact radio thin layer chromatography scanner for radiopharmaceuticals quality control

We evaluated a compact and cost-effective radio thin-layer chromatography (radio-TLC) scanner for the quality control (QC) of radiopharmaceuticals. We adapted a scintillation detector, which is a Gd3Al2Ga3O12 (GAGG:Ce) scintillation crystal array coupled with a photodiode array. The performance of the scintillator array-based radio-TLC was compared with that of a commercial device. We scanned 1 μCi/μL of Tc-99m and F-18 with each device. The difference between the ROI count ratios of the developed and commercial scanners was less than 1.2%. Our scanner is sensitive enough to take measurements for a radiochemical purity test.
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation

The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pursuit for high-performance and energy-efficient inference, significant research effort has been invested in the design of field-programmable gate array (FPGA)–based CNN accelerators. In this context, single computation engines constitute a popular design approach that enables the deployment of diverse models without the overhead of fabric reconfiguration. Nevertheless, this flexibility often comes with significantly degraded performance on memory-bound layers and resource underutilisation due to the suboptimal mapping of certain layers on the engine’s fixed configuration. In this work, we investigate the implications in terms of CNN engine design for a class of models that introduce a pre-convolution stage to decompress the weights at runtime. We refer to these approaches as on-the-fly. This article presents unzipFPGA, a novel CNN inference system that counteracts the limitations of existing CNN engines. The proposed framework comprises a novel CNN hardware architecture that introduces a weights generator module that enables the on-chip on-the-fly generation of weights, alleviating the negative impact of limited bandwidth on memory-bound layers. We further enhance unzipFPGA with an automated hardware-aware methodology that tailors the weights generation mechanism to the target CNN-device pair, leading to an improved accuracy–performance balance. Finally, we introduce an input selective processing element (PE) design that balances the load between PEs in suboptimally mapped layers. Quantitative evaluation shows that the proposed framework yields hardware designs that achieve an average of 2.57× performance efficiency gain over highly optimised GPU designs for the same power constraints and up to 3.94× higher performance density over a diverse range of state-of-the-art FPGA-based CNN accelerators.
Adaptation of Whisper models to child speech recognition

Automatic Speech Recognition (ASR) systems often struggle with transcribing child speech due to the lack of large child speech datasets required to accurately train child-friendly ASR models. However, there are huge amounts of annotated adult speech datasets which were used to create multilingual ASR models, such as Whisper. Our work aims to explore whether such models can be adapted to child speech to improve ASR for children. In addition, we compare Whisper child-adaptations with finetuned self-supervised models, such as wav2vec2. We demonstrate that finetuning Whisper on child speech yields significant improvements in ASR performance on child speech, compared to non finetuned Whisper models. Additionally, utilizing self-supervised Wav2vec2 models that have been finetuned on child speech outperforms Whisper finetuning.
Structural Credit Assignment with Coordinated Exploration

A biologically plausible method for training an Artificial Neural Network (ANN) involves treating each unit as a stochastic Reinforcement Learning (RL) agent, thereby considering the network as a team of agents. Consequently, all units can learn via REINFORCE, a local learning rule modulated by a global reward signal, which aligns more closely with biologically observed forms of synaptic plasticity. However, this learning method tends to be slow and does not scale well with the size of the network. This inefficiency arises from two factors impeding effective structural credit assignment: (i) all units independently explore the network, and (ii) a single reward is used to evaluate the actions of all units. Accordingly, methods aimed at improving structural credit assignment can generally be classified into two categories. The first category includes algorithms that enable coordinated exploration among units, such as MAP propagation. The second category encompasses algorithms that compute a more specific reward signal for each unit within the network, like Weight Maximization and its variants. In this research report, our focus is on the first category. We propose the use of Boltzmann machines or a recurrent network for coordinated exploration. We show that the negative phase, which is typically necessary to train Boltzmann machines, can be removed. The resulting learning rules are similar to the reward-modulated Hebbian learning rule. Experimental results demonstrate that coordinated exploration significantly exceeds independent exploration in training speed for multiple stochastic and discrete units based on REINFORCE, even surpassing straight-through estimator (STE) backpropagation.