Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and sequencing Mycobacterium tuberculosis aDNA from archaeological remains

Tuberculosis has been an important disease throughout human history, shaping countless past populations. The archaeological study of the causative agents of tuberculosis, members of the Mycobacterium tuberculosis Complex (MTBC), is hindered by the non-diagnostic nature of tuberculosis-associated skeletal changes. As such, ancient DNA (aDNA) or palaeogenetic analyses have become an important tool for identifying tuberculosis in past populations. However, due to the age and variable preservation of aDNA, there are often issues with sporadic results and false negatives. The overall aim of the work presented here was to use different methods, including traditional target-specific PCR, to identify and detect tuberculosis aDNA in archaeological remains. The main objectives within this overarching aim were to first test a method called whole genome amplification (WGA), used to non-specifically amplify all the DNA within a sample, and its potential to improve the yield of aDNA from skeletal remains (Chapters 3 and 4). To determine the extent of its impact, WGA was used in a comparative context, where each archaeological sample analysed was separately subjected to two methods of MTBC detection - the traditional targeted PCR method and the same method assisted by the initial application of WGA. The results show that applying WGA before the traditional targeted PCR methodology to detect the presence of MTBC pathogens in skeletal remains is only useful and viable in some cases, likely depending on the age and preservation of the sample. The second objective was to use next generation sequencing to obtain more information on the aDNA composition of certain archaeological samples and answer questions beyond the scope of traditional target-specific PCR techniques (Chapter 5). Although most of the sequencing runs were variably unsuccessful, the composition of two samples, both known to probably contain tuberculosis aDNA, could be analysed. The samples both contained similar amounts of mycobacterial aDNA and varying amounts of both human and even potentially human intestinal flora DNA. Finally, the third objective was to determine if MTBC aDNA could be detected in a rib sample from Private William Braine of the lost Franklin Expedition using standard target-specific PCR (Chapter 6). In this case study, no evidence of tuberculosis ancient DNA was found. The work done through-out highlights the difficulties of ancient DNA research and, in Chapter 4, shows the importance of using more than a single sample to evaluate methods for application in palaeogenetic contexts.
Changes in the composition of Escherichia coli murein as it ages during exponential growth

Escherichia coli murein was specifically labeled with [14C]diaminopimelic acid in the mutant strains W7 (dap lysA) and BUG6. Pulse-labeled heat-denatured E. coli cells were digested with 2 mg of egg-white lysozyme per ml to degrade the murein completely and free any lipoprotein-bound muropeptide trimers, dimers, and monomers. Pulse-chase experiments showed that the relative percentage of trimers and dimers found in the newly synthesized murein increased somewhat with time at the expense of monomers. The increase in cross-links indicated that the radioactive monomers served as acceptors in multisite transpeptidations occurring after the labeling period. The content of nonreducing monomers (C7 and C8) remained unaltered, indicating that the oligosaccharide chain length did not change with time. A gradual conversion of the reducing disaccharide tetrapeptide monomer to its tripeptide analog occurred during chasing. Braun lipoprotein was linked to about 2% of the murein subunits within 30 s of the incorporation of subunits into insoluble murein, and after one-half a generation of chase, lipoprotein-associated muropeptides had approached the maximum (16% of the total murein subunits). The distribution of muropeptides was similar in lipoprotein-linked and lipoprotein-free murein, showing that the enzyme that links Braun lipoprotein to murein does not discriminate between monomers, dimers, and trimers. No evidence for a chasable, soluble polymer of murein was found in our experiments. Hence, our data support the idea that new murein is incorporated directly into the sacculus without first existing as a soluble intermediate.
Intranasal Application of Lactococcus lactis W136 Is Safe in Chronic Rhinosinusitis Patients With Previous Sinus Surgery

Objective: Modulation of the dysbiotic gut microbiome with “healthy” bacteria via a stool transplant or supplementation is increasingly practiced, however this approach has not been explored in the nasal passages. We wished to verify whether Lactococcus lactis W136 (L. lactis W136) bacteria could be safely applied via irrigation to the nasal and sinus passages in individuals with chronic rhinosinusitis (CRS) with previous undergone endoscopic sinus surgery, and whether this was accompanied by bacterial community flora modification. Study Design: Prospective open-label pilot trial of safety and feasibility. Setting: Academic tertiary hospital center. Subjects and Methods: Twenty-four patients with CRS refractory to previous medical and surgical therapy received a 14-day course of BID sinus irrigations containing 1.2 × 109 CFU live L. lactis W136. Patients were monitored for safety using questionnaire, sinus endoscopy, otoscopy, UPSIT-40 smell testing, and endoscopically-obtained conventional sinus culture and a swab for 16S microbiome profiling. Results: All 24 patients receiving at least one treatment successfully completed treatment. L. lactis W136 probiotic treatment was safe, with no major adverse events or new infections. Treatment was associated with improvement in sinus symptoms, QOL, and mucosal scores, which remained improved during the subsequent 14-day observation period. Microbiome changes associated with treatment were limited to an increase of the pathobiont Dolosigranulum pigrum, a bacteria identified as potentially beneficial in the upper airways. Subgroup analysis suggested differences in microbiomes and responses for CRSsNP and CRSwNP phenotypes, but these did not attain significance. Conclusion: Intranasal irrigation of live L. lactis W136 bacteria to patients with refractory chronic rhinosinusitis was safe, and was associated with effects on symptoms, mucosal aspect and microbiome composition. Intranasal bacteria may thus find a role as a treatment strategy for CRS. Clinical Trials Registration: www.ClinicalTrials. gov. identifier: NCT04048174.
A Novel Model of Adoption of M-Commerce in Saudi Arabia

The market of Saudi Arabia offers considerable potential for the success of mobile commerce (m-commerce) technology. In this context, this study aims to investigate the factors that influence the intentions of Saudi citizens and firms to use m-commerce technology. This study paper advances the literature by proposing a novel model to investigate the motivations behind adopting m-commerce in Saudi Arabia. The model defines factors that influence both customer and provider intentions of adopting m-commerce. The paper also outlines the methodology to be followed to evaluate the proposed model.
Energetic Optimization of the Use of Battery Shunting Locomotive in Industrial Plant with Regenerative Brake

Shunting locomotives perform specific functions characterized by short mileages, variable load weights and a high idling rate. Battery technology based on traction is a very suitable solution for shunting locomotives due to the possibility of accumulating kinetic energy in the power supply. The optimization of the energy efficiency of regenerative braking and the possibilities for efficient shunting in industrial plant were studied.
PaGraph: Scaling GNN training on large graphs via computation-aware caching

Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data. By leveraging GPU accelerators, existing frameworks combine both mini-batch and sampling for effective and efficient model training on large graphs. However, this setup faces a scalability issue since loading rich vertices features from CPU to GPU through a limited bandwidth link usually dominates the training cycle. In this paper, we propose PaGraph, a system that supports general and efficient sampling-based GNN training on single-server with multi-GPU. PaGraph significantly reduces the data loading time by exploiting available GPU resources to keep frequently accessed graph data with a cache. It also embodies a lightweight yet effective caching policy that takes into account graph structural information and data access patterns of sampling-based GNN training simultaneously. Furthermore, to scale out on multiple GPUs, PaGraph develops a fast GNN-computation-aware partition algorithm to avoid cross-partition access during data parallel training and achieves better cache efficiency. Evaluations on two representative GNN models, GCN and GraphSAGE, show that PaGraph achieves up to 96.8% data loading time reductions and up to 4.8X performance speedup over the state-of-the-art baselines. Together with preprocessing optimization, PaGraph further delivers up to 16.0X end-to-end speedup.
Retrospective games in Intel Technology Poland

One of the Agile principles is that the team should regularly reflect on “how to become more effective, then tunes and adjusts its behavior accordingly”. While the setup of a retrospective session is intuitive, in praxis, conducting successful retrospectives is challenging. This paper is a continuation of our previous work on the use collaborative games in addressing common retrospective problems. In addition to the replication of our previous action research in a new context, we aim to investigate whether preliminary anonymous idea generation mitigates negative social influences that have been identified as causes of poor performance of brainstorming. The obtained results confirms the previous findings that game-based retrospectives produces better results than the standard retrospective as well as improves participants’ creativity, involvement, and communication. Our findings also suggest benefits to the preliminary anonymous idea generation.
Exploration into Deep Learning Text Generation Architectures for Dense Image Captioning

Image captioning is the process of generating a textual description that best fits the image scene. It is one of the most important tasks in computer vision and natural language processing and has the potential to improve many applications in robotics, assistive technologies, storytelling, medical imaging and more. This paper aims to analyse different encoder-decoder architectures for dense image caption generation while focusing on the text generation component.Already trained models for image feature generation are utilized with transfer learning. These features are used for describing the regions using three different models for text generation. We propose three deep learning architectures for generating one-sentence captions of Regions of Interest (RoIs). The proposed architectures reflect several ways of integrating features from images and text. The proposed models were evaluated and compared with several metrics for natural language generation. The experimental results demonstrate that injecting image features into a decoder RNN while generating a caption word by word is the best performing architecture among the architectures explored in this paper.
The orienteering Problem with Replenishment

The planning of long-duration missions for resource-constrained agents is a longstanding challenge not only in field robotics research, but also in the fields of vehicle routing and logistics. To efficiently plan missions over a larger time scale than an agent’s resource capacity natively allows, it is necessary to exploit the availability of recharging stations, where an agent can replenish its spent resources at the cost of time. This paper presents the Orienteering Problem with Replenishment, a novel extension of the orienteering problem, which can fully utilise the existence of recharging stations by not only revisiting such stations, but also intelligently managing the amount of time spent recharging at each stop. By leveraging the above mechanisms for overcoming the resource limitations of the platform, the proposed problem formulation allows for the planning of longer term missions. A suite of computational tests are performed to assess the proposed formulation, and its performance is analysed with respect to the impact of intelligent utilisation of the recharge points on reward acquisition. Finally, limitations of the proposed problem formulation are discussed, and avenues for future work are presented.
Enhanced dynamic properties of Ge-on-Si mode-evolution waveguide photodetectors

This work discusses coupled three-dimensional electromagnetic and electrical simulations of a Ge-on-Si waveguide photodetector where light is fed through a lateral waveguide. The numerical results show that this coupling solution leads to more uniform photon and carrier distributions along the Ge absorber compared to a conventional butt-coupled detector, allowing a broader electrooptical bandwidth for high input power levels in good agreement with available experimental measurements.
A Novel Method for Power Generation Using Trains

Wind energy is one of the most renewable sources that has a very high potential for use. This paper presents a method for producing electric energy using the kinetic energy of wind by installing special wind turbines in locomotives. Many studies have been conducted on the installation of wind turbines near the railway (tunnels, large fields, etc.), but few of these papers focus on wind turbines as part of their locomotives and trains. Wind energy causes the wind turbine to travel through the train movement. Several designs have been taken so far on the installation of wind turbines along the train rails. The movement of the train itself would cause the wind to flow through the wind turbine blades mounted on the locomotive. In this paper, two types of known wind turbines are studied and then a complementary method and complement is proposed in Siemens ER24PC locomotive engines used in Iranian railway systems. Reducing the fuel consumption of trains is carefully considered based on the generated power. A battery storage system is also proposed that provides power over low wind speeds.
1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

In this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (−40dB to +20dB). The traditional electronic support measure (ESM) systems require high SNR for radar signal classification. LPI (low probability of intercept) radar signals that are received by ESM systems are usually corrupted by noise. So, we demonstrate through extensive simulations that it is possible to achieve high classification performance at low SNR levels providing that the underlying radar signals are known in advance. MF bank classifier, 1D Convolutional Neural Networks (CNNs) and the minimum distance classifier using spectral-domain features (the skewness, the kurtosis, and the energy of the dominant frequency) have been derived for the radar signal classification and their performances have been compared with each other and with the optimal classifier.
Dynamic Computation Offloading With Energy Harvesting Devices: A Hybrid-Decision-Based Deep Reinforcement Learning Approach

Mobile-edge computing (MEC) with energy harvesting (EH) is becoming an emerging paradigm to improve the computation experience for the Internet-of-Things (IoT) devices. For a multidevice multiserver MEC system, the frequently varied harvested energy, along with changeable computation task loads and time-varying computation capacities of servers, increase the system’s dynamic. Therefore, each device should learn to make coordinated actions, such as the offloading ratio, local computation capacity, and server selection, to achieve a satisfactory computation quality. Thus, the MEC system with EH devices is highly dynamic and face two challenges: 1) continuous–discrete hybrid action spaces and 2) coordination among devices. To deal with such problem, we propose two deep reinforcement learning (DRL)-based algorithms: 1) hybrid-decision-based actor–critic learning (Hybrid-AC) and 2) multidevice hybrid-AC (MD-Hybrid-AC) for dynamic computation offloading. Hybrid-AC solves the hybrid action space with an improvement of <italic>actor–critic</italic> architecture, where the <inline-formula> <tex-math notation="LaTeX">${{actor}}$ </tex-math></inline-formula> outputs continuous actions (offloading ratio and local computation capacity) corresponding to every server, and the <inline-formula> <tex-math notation="LaTeX">${{critic}}$ </tex-math></inline-formula> evaluates the continuous actions and outputs the discrete action of server selection. MD-Hybrid-AC adopts the framework of centralized training with decentralized execution. It learns coordinated decisions by constructing a centralized <inline-formula> <tex-math notation="LaTeX">${{critic}}$ </tex-math></inline-formula> to output server selections, which considers the continuous action policies of all devices. Simulation results show that the proposed algorithms achieve a good balance between consumed time and energy, and have a significant performance improvement compared with baseline offloading policies.
A RF Predistortion Approach to in-Device Coexistence between 2.4 GHz Wi-Fi and 4G LTE Signals

This paper presents the efficiency enhancement of RF power amplifiers (PA) using a RF predistortion (PD) technique and evaluates its performance in coexistence of the 2.4 GHz Wi-Fi and 4G LTE signals. For coexistence applications of the 2.4 GHz Wi-Fi and 4G LTE systems, a bulk acoustic wave (BAW) filter is practically adopted as a Wi-Fi coexistence filter at the Wi-Fi transmitter to greatly attenuate the signal leakage from the 2.4 GHz Wi-Fi band into the 4G LTE bands for mitigating adjacent channel interference. The presented RF PD technique shows the robust performance over widely adopted digital predistortion (DPD) technique in such coexistence applications, where strictly bandlimited channels close to the band edges are applied. In practical 2.4 GHz Wi-Fi applications, we reveal the effectiveness of the RF PD on all channels and the ineffectiveness of the DPD on some channels close to the band edges for the first time in suppressing spectral regrowth in the coexistence of Wi-Fi and LTE signals.
D-band Phased-Array TX and RX Front Ends Utilizing Radio-on-Glass Technology

This paper reports wide-band TX and RX phased-array front ends for D-band (130-170 GHz), implemented in 0.13 µm SiGe BiCMOS process with fT/fmax of 300/500 GHz. The TX front end consists of a wide-band vector-modulator phase shifter followed by a PA. The TX yields an output P1dB of 10 dBm and a total gain of 16 dB. The RX consists of an LNA, wide-band vector-modulator and a buffer stage. It exhibits a gain of 22 dB and a noise figure of 10 dB. A glass interposer is designed with 8 flip-chip TX/RX front ends and an integrated 8x16 slot antenna array, forming a radio-on-glass (RoG) D-band phased-array module.
Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

Plant counting and location are essential for both plant breeding experiments and production agriculture. Stand count indicates the overall emergence of plants compared to the number of seeds that were planted, while location provides information on the associated variability within a plot or geographic area of a field. Deep learning has been successfully applied in various application domains, including plant phenotyping. This article proposes the use of deep learning techniques, more specifically, anchor-free detectors, to identify and count maize plants in RGB images acquired from unmanned aerial vehicles. The results were obtained using a modified CenterNet architecture, with validation performed against manual human annotation. Experimental results demonstrated an overall precision $>$95% for examples where training and testing were performed on the same field. Few-shot learning was also explored, where the trained network was 1) directly applied to the fields in other geographic areas and 2) updated using small quantities of training data from the other locations.
Evaluating Convolutional Neural Networks Reliability depending on their Data Representation

Safety-critical applications are frequently based on deep learning algorithms. In particular, Convolutional Neural Networks (CNNs) are commonly deployed in autonomous driving applications to fulfil complex tasks such as object recognition and image classification. Ensuring the reliability of CNNs is thus becoming an urgent requirement since they constantly behave in human environments. A common and recent trend is to replace the full-precision CNNs to make way for more optimized models exploiting approximation paradigms such as reduced bit-width data type. If from one hand this is poised to become a sound solution for reducing the memory footprint as well as the computing requirements, it may negatively affect the CNNs resilience. The intent of this work is to assess the reliability of a CNN-based system when reduced bit-widths are used for the network parameters (i.e., synaptic weights). The approach evaluates the impact of permanent faults in CNNs by adopting several bit-width schemes and data types, i.e., floating-point and fixed-point. This determines the trade-off between the CNN accuracy and the bits required to represent network weights. The characterization is performed through a fault injection environment built on the darknet open source framework. Experimental results show the effects of permanent fault injections on the weights of LeNet-5 CNN.
Dynamic Sparse Coded Multi-Hop Transmissions Using Reinforcement Learning

Packet transmissions over multi-hop lossy links are important in the future space-air-ground integrated networks. This letter considers sparse coded transmissions with reduced complexity compared to the well-known random linear network coding, which is known to be efficient in multi-hop lossy links. We propose a reinforcement learning framework for dynamically adjusting coding parameters on-line to improve the performance based on decoder feedback. A key advantage of the dynamic scheme is that it does not require a prior knowledge of the environment nor an analysis model. Extensive evaluation shows that it adapts well in scenarios where link conditions are unknown and/or changing, and achieves performance close to that of the optimal fixed schemes found by exhaustive search.
A 7.4dBm EIRP, 20.2% DC-EIRP Efficiency 148 GHz Coupled Loop Oscillator with Multi-feed Antenna in 22nm FD-SOI

This paper presents a 148 GHz coupled loop oscillator with multi-feed (MF) octagonal slot antenna. The MF antenna combines the output power from four single ended fundamental oscillators (fo) and back radiates the combined fundamental power through a silicon lens. The output power from each oscillator is ac coupled to the following stage using a phase compensation capacitor, whose value is selected to increase the oscillation frequency of the coupling loop network. The oscillator is fabricated in 22nm FD-SOI process and has a peak EIRP of 7.4 dBm and a corresponding DC-EIRP efficiency of 20.2%. To the best of the authors’ knowledge, this offers over an order of magnitude improvement in DC-EIRP efficiency among 100-200 GHz VCOs reported to date.