19,207 research outputs found
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due
to its application in many knowledge areas. One of the major challenges on
mining data streams is concept drift, which requires the learner to discard the
current concept and adapt to a new one. Ensemble-based drift detection
algorithms have been used successfully to the classification task but usually
maintain a fixed size ensemble of learners running the risk of needlessly
spending processing time and memory. In this paper we present improvements to
the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for
regression that employs social networks theory. In order to detect concept
drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show
improvements in accuracy, especially in concept drift situations and better
performance compared to other state-of-the-art algorithms in both real and
synthetic data
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR
Automatic speech recognition (ASR) has gained a remarkable success thanks to
recent advances of deep learning, but it usually degrades significantly under
real-world noisy conditions. Recent works introduce speech enhancement (SE) as
front-end to improve speech quality, which is proved effective but may not be
optimal for downstream ASR due to speech distortion problem. Based on that,
latest works combine SE and currently popular self-supervised learning (SSL) to
alleviate distortion and improve noise robustness. Despite the effectiveness,
the speech distortion caused by conventional SE still cannot be completely
eliminated. In this paper, we propose a self-supervised framework named
Wav2code to implement a generalized SE without distortions for noise-robust
ASR. First, in pre-training stage the clean speech representations from SSL
model are sent to lookup a discrete codebook via nearest-neighbor feature
matching, the resulted code sequence are then exploited to reconstruct the
original clean representations, in order to store them in codebook as prior.
Second, during finetuning we propose a Transformer-based code predictor to
accurately predict clean codes by modeling the global dependency of input noisy
representations, which enables discovery and restoration of high-quality clean
representations without distortions. Furthermore, we propose an interactive
feature fusion network to combine original noisy and the restored clean
representations to consider both fidelity and quality, resulting in even more
informative features for downstream ASR. Finally, experiments on both synthetic
and real noisy datasets demonstrate that Wav2code can solve the speech
distortion and improve ASR performance under various noisy conditions,
resulting in stronger robustness.Comment: 12 pages, 7 figures, Submitted to IEEE/ACM TASL
Environmental surveillance for Salmonella Typhi as a tool to estimate the incidence of typhoid fever in low-income populations.
Background: The World Health Organisation recommends prioritised use of recently prequalified typhoid conjugate vaccines in countries with the highest incidence of typhoid fever. However, representative typhoid surveillance data are lacking in many low-income countries because of the costs and challenges of diagnostic clinical microbiology. Environmental surveillance (ES) of Salmonella Typhi in sewage and wastewater using molecular methods may offer a low-cost alternative, but its performance in comparison with clinical surveillance has not been assessed. Methods: We developed a harmonised protocol for typhoid ES and its implementation in communities in India and Malawi where it will be compared with findings from hospital-based surveillance for typhoid fever. The protocol includes methods for ES site selection based on geospatial analysis, grab and trap sample collection at sewage and wastewater sites, and laboratory methods for sample processing, concentration and quantitative polymerase chain reaction (PCR) to detect Salmonella Typhi. The optimal locations for ES sites based on digital elevation models and mapping of sewage and river networks are described for each community and their suitability confirmed through field investigation. We will compare the prevalence and abundance of Salmonella Typhi in ES samples collected each month over a 12-month period to the incidence of blood culture confirmed typhoid cases recorded at referral hospitals serving the study areas. Conclusions: If environmental detection of Salmonella Typhi correlates with the incidence of typhoid fever estimated through clinical surveillance, typhoid ES may be a powerful and low-cost tool to estimate the local burden of typhoid fever and support the introduction of typhoid conjugate vaccines. Typhoid ES could also allow the impact of vaccination to be assessed and rapidly identify circulation of drug resistant strains
Offline and Online Models for Learning Pairwise Relations in Data
Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting
Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer
Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear.Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features.Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore.Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination
Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions
Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms.
Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 ÎĽg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis.
Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1
1.1 Welcome to the anthropocene .......................................................................... 1
1.2 From cellular stress responses to ecosystem resilience ................................... 3
1.2.1 The individual pursuit for homeostasis ....................................................... 3
1.2.2 Stability from diversity ................................................................................. 5
1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical
pollution? ................................................................................................................. 6
1.4 Functional ecotoxicological assessment of microbial communities ................... 9
1.5 Molecular tools – the key to a mechanistic understanding of stressor effects
from a functional perspective in microbial communities? ...................................... 12
2. Aims and Hypothesis ......................................................................................... 14
2.1 Research question .......................................................................................... 14
2.2 Hypothesis and outline .................................................................................... 15
2.3 Experimental approach & concept .................................................................. 16
2.3.1 Aquatic freshwater biofilms as model community ..................................... 16
2.3.2 Diuron as model herbicide ........................................................................ 17
2.3.3 Experimental design ................................................................................. 18
3. Structural and physiological changes in microbial communities after chronic
exposure - PICT and altered functional capacity ................................................. 21
3.1 Introduction ..................................................................................................... 21
3.2 Methods .......................................................................................................... 23
3.2.1 Biofilm cultivation ...................................................................................... 23
3.2.2 Dry weight and autotrophic index ............................................................. 23
3.2.4 Pigment analysis of periphyton ................................................................. 23
3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24
3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24
3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26
3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27
3.2.5 Community oxygen metabolism measurements ....................................... 28
3.3 Results and discussion ................................................................................... 29
3.3.1 Comparison of the structural community parameters ............................... 29
3.3.2 Photosynthetic activity and primary production of the communities after
selection phase ................................................................................................. 33
3.3.3 Acquisition of photosynthetic tolerance .................................................... 34
3.3.4 Primary production at exposure conditions ............................................... 36
3.3.5 Tolerance detection in primary production ................................................ 37
3.4 Summary and Conclusion ........................................................................... 40
4. Community gene expression analysis by meta-transcriptomics ................... 41
4.1 Introduction to meta-transcriptomics ............................................................... 41
4.2. Methods ......................................................................................................... 43
4.2.1 Sampling and RNA extraction................................................................... 43
4.2.2 RNA sequencing analysis ......................................................................... 44
4.2.3 Data assembly and processing................................................................. 45
4.2.4 Prioritization of contigs and annotation ..................................................... 47
4.2.5 Sensitivity analysis of biological processes .............................................. 48
4.3 Results and discussion ................................................................................... 48
4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49
4.3.2 Insights into community stress response mechanisms using trend analysis
(DRomic’s) ......................................................................................................... 51
4.3.3 Response pattern in the isoform PS genes .............................................. 63
4.5 Summary and conclusion ................................................................................ 65
5. Community metabolome analysis ..................................................................... 66
5.1 Introduction to community metabolomics ........................................................ 66
5.2 Methods .......................................................................................................... 68
5.2.1 Sampling, metabolite extraction and derivatisation................................... 68
5.2.2 GC-TOF-MS analysis ............................................................................... 69
5.2.3 Data processing and statistical analysis ................................................... 69
5.3 Results and discussion ................................................................................... 70
5.3.1 Characterization of the metabolic fingerprints .......................................... 70
5.3.2 Difference in the metabolic fingerprints .................................................... 71
5.3.3 Differential metabolic responses of the communities to short-term exposure
of diuron ............................................................................................................ 73
5.4 Summary and conclusion ................................................................................ 78
6. Synthesis ............................................................................................................. 79
6.1 Approaches and challenges for linking molecular data to functional
measurements ...................................................................................................... 79
6.2 Methods .......................................................................................................... 83
6.2.1 Summary on the data ............................................................................... 83
6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83
6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83
6.3 Results and discussion ................................................................................... 85
6.3.1 Results of aggregation techniques ........................................................... 85
6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86
6.3.3 Mechanistic view of the molecular stress responses based on KEGG
functions ............................................................................................................ 89
6.4 Consolidation of the results – holistic interpretation and discussion ............... 93
6.4.1 Adaptation to chronic diuron exposure - from molecular changes to
community effects.............................................................................................. 93
6.4.2 Assessment of the ecological costs of Pollution-induced community
tolerance based on primary production ............................................................. 94
6.5 Outlook ............................................................................................................ 9
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