6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Data Center Resource Usage Forecasting with Convolutional Recurrent Neural Networks

Abstract Energy efficiency, scalability, and reliability are increasingly important for sustainable data centers. In this paper, we focus on forecasting real-world resource usage using neural network time series models, specifically utilizing convolutional recurrent long short-term Memory (LSTM) and gated recurrent unit (GRU) architectures. In our analysis, we compare LSTM and GRU in terms of forecasting accuracy and computational complexity during model training. We demonstrate that recurrent neural networks are more accurate and robust compared to the traditional autoregressive integrated moving average (ARIMA) time series model in this complex forecasting problem. GRU achieved a 9% reduction and LSTM a 5% reduction in forecasting mean squared error (MSE) compared to ARIMA. Furthermore, the GRU architecture with a 1D convolution layer outperforms LSTM architecture in both forecast accuracy and training time. The proposed model can be effectively applied to load forecasting as part of a data center computing cluster. In this application, the proposed GRU architecture has 25% fewer trainable parameters in the recurrent layer than the commonly used LSTM.Abstract Energy efficiency, scalability, and reliability are increasingly important for sustainable data centers. In this paper, we focus on forecasting real-world resource usage using neural network time series models, specifically utilizing convolutional recurrent long short-term Memory (LSTM) and gated recurrent unit (GRU) architectures. In our analysis, we compare LSTM and GRU in terms of forecasting accuracy and computational complexity during model training. We demonstrate that recurrent neural networks are more accurate and robust compared to the traditional autoregressive integrated moving average (ARIMA) time series model in this complex forecasting problem. GRU achieved a 9% reduction and LSTM a 5% reduction in forecasting mean squared error (MSE) compared to ARIMA. Furthermore, the GRU architecture with a 1D convolution layer outperforms LSTM architecture in both forecast accuracy and training time. The proposed model can be effectively applied to load forecasting as part of a data center computing cluster. In this application, the proposed GRU architecture has 25% fewer trainable parameters in the recurrent layer than the commonly used LSTM

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis. Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images. Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs. Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3-100%) in the test set (n = 217) of manually labeled helminth eggs. Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.Peer reviewe

Ontology-based Framework for Integration of Time Series Data : Application in Predictive Analytics on Data Center Monitoring Metrics

AbstractMonitoring a large and complex system such as a data center generates many time series of metric data, which are often stored using a database system specifically designed for managing time series data. Different, possibly distributed, databases may be used to collect data representing different aspects of the system, which complicates matters when, for example, developing data analytics applications that require integrating data from two or more of these. From the developer’s point of view, it would be highly convenient if all of the required data were available in a single database, but it may well be that the different databases do not even implement the same query language. To address this problem, we propose using an ontology to capture the semantic similarities among different time series database systems and to hide their syntactic differences. Alongside the ontology, we have developed a Python software framework that enables the developer to build and execute queries using classes and properties defined by the ontology. The ontology thus effectively specifies a semantic query language that can be used to retrieve data from any of the supported database systems, and the Python framework can be set up to treat the different databases as a single data store that can be queried using this semantic language. This is demonstrated by presenting an application involving predictive analytics on resource usage and electricity consumption metrics gathered from a Kubernetes cluster, stored in Prometheus and KairosDB databases, but the framework can be extended in various ways and adapted to different use cases, enabling machine learning research using distributed heterogeneous data sources.Abstract Monitoring a large and complex system such as a data center generates many time series of metric data, which are often stored using a database system specifically designed for managing time series data. Different, possibly distributed, databases may be used to collect data representing different aspects of the system, which complicates matters when, for example, developing data analytics applications that require integrating data from two or more of these. From the developer’s point of view, it would be highly convenient if all of the required data were available in a single database, but it may well be that the different databases do not even implement the same query language. To address this problem, we propose using an ontology to capture the semantic similarities among different time series database systems and to hide their syntactic differences. Alongside the ontology, we have developed a Python software framework that enables the developer to build and execute queries using classes and properties defined by the ontology. The ontology thus effectively specifies a semantic query language that can be used to retrieve data from any of the supported database systems, and the Python framework can be set up to treat the different databases as a single data store that can be queried using this semantic language. This is demonstrated by presenting an application involving predictive analytics on resource usage and electricity consumption metrics gathered from a Kubernetes cluster, stored in Prometheus and KairosDB databases, but the framework can be extended in various ways and adapted to different use cases, enabling machine learning research using distributed heterogeneous data sources

Credit crunch in the U.S. interbank money market 2007-2008: issues in bank funding markets and federal reserve responses.

This study discusses the interbank money market credit crunch in the United States in 2007-2008. The objective is to answer to the question of whether or not the actions of the Federal Reserve were effective and accurate in solving the crisis in the U.S. interbank money market based on the evaluations made in the academic community. The study presents some theory on the liquidity and credit crunches and then moves on to explaining the issues in the interbank money markets and motives of the market operators during the time period examined. Finally the Federal Reserve responses to the problems in the interbank money markets are summarized and their effectiveness and accuracy is analyzed. The methodology used in this study is a literature review with a graphical analysis and simple calculations regarding certain money markets based on data retrieved from varied sources of the Federal Reserve and other institutions. The review limits itself to the interbank money market funding in the United States and to the time period between June 2007 and June 2008. The instruments analyzed have a maturity of three months and lower. The main result was that according to the evaluations, the Federal Reserve responses were in some cases effective although they were not strong enough to prevent the credit crunch from continuing. Cuts of the main policy interest rate were considered inflationary and ineffective to address the issues in the interbank money market. The arguments concerning the bailout of Bear Stearns, repo operations and central bank swap lines were mainly positive. The econometrical analysis is not extensive yet but the research made thus far suggest that the effects of new lending facilities (Term Auction Facility, Term Securities Lending Facility and Primary Dealer Credit Facility) have controversial results on the LIBOR-OIS spread but reduced spreads in the repo market. The main concern regarding the institution bailouts and accepting low-grade collateral was the increasing the risk of moral hazard, thus the irresponsible decision-making

Yksilön valintoja ja yhteisön normeja : elämänkaaritutkimus 1800-luvun alussa syntyneistä talontyttäristä

Pro gradu -tutkielmassani tutkin vuosina 1800-1810 syntyneiden talontytärten elämänkaaria. Tutkimusaineistoni koostuu 57 Viljakkalassa syntyneestä naisesta, joiden elämänkaaria olen kirkonkirjamateriaalin avulla selvittänyt syntymästä kuolemaan saakka. Metodinani käytän prosopografiaa, jota voi luonnehtia ryhmätasolla tapahtuvaksi elämänkerralliseksi ja henkilöhistorialliseksi tutkimukseksi. Teoreettisilta lähtökohdiltaan tutkielmani liittyy osaksi sosiaalihistoriallista tutkimusperinnettä, jossa korostuu etenkin sukupuolihistoriallinen näkökulma. Tutkielmassani tarkastelen, minkälaisia toimintamahdollisuuksia ja vaihtoehtoisia elämänkaaria talonpoikaisen syntyperän omaaville naisille avautui 1800-luvun länsisuomalaisella maaseudulla. Tutkielmassani lähestyn naisten elämänkaaria kronologisten ikävaiheiden kautta. Lapsuudessa tytön sijoittuminen sisarussarjassa vaikutti siihen, minkälaiseksi hänen asemansa perheessä muodostui ja minkälaisia sosiaalisia suhteita hän lapsuudessa solmi. Keskeinen teema lapsuutta tarkasteltaessa on lapsikuolleisuus, joka tuohon aikaan oli merkittävä ilmiö: tutkimusaineistoni naisista 23 kuoli alle 15-vuotiaana. Noin 20-vuotiaina naisten elämänkaaret alkoivat eriytyä. Osa tytöistä muutti lapsuudenkodistaan piikomaan tai avioitui melko nuorina ja osa asui suhteellisen iäkkääksi - jopa lähes 30-vuotiaaksi - lapsuudenkodissaan. Piikominen muodostui myös talontytärten kohdalla tärkeäksi välivaiheeksi kotona vietetyn lapsuuden ja avioliiton solmimisen välille, vaikka talollisten lasten pestautuminen palkolliseksi oli harvinaisempaa kuin alempien yhteiskuntaryhmien lasten. Avioliiton solmi tutkimusaineistoni 34 aikuiseksi eläneestä naisesta 30. Avioitumisikä vaihteli naisten kohdalla runsaasti. Avioitumisiän mediaani oli 25 vuotta, mutta vaihteluväli oli 17-49 vuotta. Näin ollen elämänkaariin mahtui hyvinkin erilaisia vaiheita riippuen siitä, minkä ikäisinä avioliitot solmittiin. Lasten syntyminen voidaan nähdä avioliiton luonnollisena seurauksena. Avioitumisikä vaikutti lasten lukumäärään: mitä nuorempana nainen avioitui, sitä enemmän lapsia syntyi. Aviomiehen yhteiskunnallinen status vaikutti naisten elämänkaariin. Sosiaalista laskua tapahtui talontytärten kohdalla paljon, sillä vain kahdeksan naista solmi avioliiton talollisen kanssa. Suurin osa tytöistä päätyi torppien emänniksi, mutta myös käsityöläiset olivat suosittuja aviomiehiä talontyttärille. Tutkimusaineistossani on esimerkkejä myös siitä, miten talonpoikaisen syntyperän omaava nainen päätyi itselliseksi tai toimi palkollisena myös avioitumisen jälkeen. Lopulta avioliiton kautta hankittu yhteiskunnallinen status kytkeytyi myös vanhuuden elämänpiiriin. Tämä osoittaa, että aiemmilla elämänkaaren vaiheilla oli suuri merkitys myöhemmin elämässä. Naisten elämänkaaret eivät rakentuneet tyhjiössä, vaan niihin vaikutti paitsi henkilön omat valinnat ja elämänkaaren aiemmat tapahtumat, mutta myös yhteisön tavat ja normit

Alkoholin suurkuluttaja leikkauskohteena

Alkoholi selittää Suomessa työikäisten kuolleisuudesta suuremman osan kuin sepelvaltimotauti miehillä tai rintasyöpä naisilla. Se on myös Suomen käytetyin päihde. Alkoholi aiheuttaa suoraan elinvaurioita. Suurin osa suurkuluttajista kehittää rasvamaksan, ja merkittävä osa sairastuu vuosien käytön jälkeen alkoholihepatiittiin tai -kirroosiin, joiden ennuste on konservatiivisestikin hoidettuna huono. Nämä potilaat joutuvat läpikäymään tavanomaisia leikkauksia. Leikkauskuolleisuus on merkittävä, minkä vuoksi maksasairauden etsiminen alkoholisteilta on olennaista. Loppuvaiheen maksakirroosi askitesmuodostuksineen ja portahypertensioineen on elektiivisen leikkauksen vasta-aihe. Oireettomien suurkuluttajien tunnistaminen on haasteellista, ja tässä voidaan kliinisen tutkimuksen tukena käyttää rakenteisia kyselykaavakkeita sekä laboratoriotutkimuksia. On tutkimusnäyttöä, että suurkuluttajat ovat alttiimpia postoperatiivisille komplikaatioille. Näitä ovat erityisesti leikkausalueen kirurgiset komplikaatiot, uusintaleikkaukset, pitkittynyt sairaalahoito, hengitysvajaus, pneumonia ja septiset infektiot. On myös näytetty, että suurkuluttajat hyötyvät suunniteltua leikkausta edeltävästä abstinenssista, jonka tulisi kestää vähintään kuukauden ajan; lyhemmästä abstinenssista ei sen sijaan ole osoitettu olevan merkitsevää hyötyä. Alkoholin suurkuluttajat ovat alttiita alkoholin vieroitusoireyhtymälle (alcoholic withdrawal syndrome, AWS), johon liittyy kasvanut postoperatiivisten komplikaatioiden riski. Leikattavat potilaat hyötyvät sekä AWS:aa ehkäisevistä että sitä lievittävistä hoidoista. Eri hoitomuodoilla ei ole osoitettu olevan merkitsevää eroa. Humalan vaikutuksista leikkauskelpoisuuteen on hyvin niukasti tutkittua tietoa. Farmakologiset yhteisvaikutukset anesteettien kanssa ovat ongelmallisia: opioidien ja alkoholin yhteiskäytön seurauksena voi kehittyä hengityslama. Humalaiset potilaat kannattaa ottaa leikkaukseen vain hätätilanteessa. Leikkauksen jälkeen näitä potilaita tulee hoitaa ainakin hengityskoneesta vieroittamiseen ja ekstubaatioon saakka teho- tai valvontaosastolla

Machine Learning - based Optimization of Biomass Drying Process: Application of Utilizing Data Center Excess Heat

Abstract The utilization of biomass as a renewable energy source holds significant promise for climate mitigation efforts. Excess heat from Nordic data centers offers opportunities for sustainable energy utilization. This research explores the feasibility of using data center excess heat for biomass drying to enhance the biomass energy value. In this study, the challenge of predicting biomass moisture under demanding measurement conditions is addressed by developing a predictive model for exhaust air humidity from the dryer. This model indirectly describes biomass moisture and employs machine learning methods such as linear regression model (LM), gradient boosting machines (GBM), eXtreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), while enhancing transparency through explainable artificial intelligence (XAI) techniques for analyzing and visualizing humidity fluctuations. Based on this study, it can be demonstrated that tree-based ensemble methods GBM, RF, and XGBoost can accurately predict the humidity of air exiting the dryer with coefficient of determination from 0.88 to 0.89. Weather conditions, supply air humidity, and dryer fan speed emerged as key factors affecting drying efficiency, providing actionable insights for process optimization. Specific thresholds for these features can be defined to facilitate process settings. Moreover, improving system air tightness enhances drying efficiency and mitigates weather effects. The model shows promising predictive capabilities for exhaust air humidity, enabling future dynamic modeling to indirectly predict biomass end moisture, enabling adaptive control of drying processes, optimizing production capacities, and advancing sustainable energy through AI-driven solutions.Abstract The utilization of biomass as a renewable energy source holds significant promise for climate mitigation efforts. Excess heat from Nordic data centers offers opportunities for sustainable energy utilization. This research explores the feasibility of using data center excess heat for biomass drying to enhance the biomass energy value. In this study, the challenge of predicting biomass moisture under demanding measurement conditions is addressed by developing a predictive model for exhaust air humidity from the dryer. This model indirectly describes biomass moisture and employs machine learning methods such as linear regression model (LM), gradient boosting machines (GBM), eXtreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP), while enhancing transparency through explainable artificial intelligence (XAI) techniques for analyzing and visualizing humidity fluctuations. Based on this study, it can be demonstrated that tree-based ensemble methods GBM, RF, and XGBoost can accurately predict the humidity of air exiting the dryer with coefficient of determination from 0.88 to 0.89. Weather conditions, supply air humidity, and dryer fan speed emerged as key factors affecting drying efficiency, providing actionable insights for process optimization. Specific thresholds for these features can be defined to facilitate process settings. Moreover, improving system air tightness enhances drying efficiency and mitigates weather effects. The model shows promising predictive capabilities for exhaust air humidity, enabling future dynamic modeling to indirectly predict biomass end moisture, enabling adaptive control of drying processes, optimizing production capacities, and advancing sustainable energy through AI-driven solutions

AI-Assisted Decision Support for District Heating Demand Response

Abstract Digital twins as decision support platforms construct a comprehensive digital representation of large-scale systems. This depiction can be utilised with machine learning to promote value from system data. Research conducted for this paper uses district heating data as basis for decision support digital twin that helps predict demand response situations in a district heating network by using a multi-modal model pipeline. Findings of the study include that computationally generated models can represent the data accurately enough to fit a well-defined purpose, even if model degradation increases variation from the actualised values. Additionally, the study illustrates the capability to observe and manipulate the digital twin environment through an interface, enhancing its value proposition.Abstract Digital twins as decision support platforms construct a comprehensive digital representation of large-scale systems. This depiction can be utilised with machine learning to promote value from system data. Research conducted for this paper uses district heating data as basis for decision support digital twin that helps predict demand response situations in a district heating network by using a multi-modal model pipeline. Findings of the study include that computationally generated models can represent the data accurately enough to fit a well-defined purpose, even if model degradation increases variation from the actualised values. Additionally, the study illustrates the capability to observe and manipulate the digital twin environment through an interface, enhancing its value proposition