Miracle-dz/UniFAR-FTUs huggingface dataset:
EfficientRepresentation ofMachine Learning Models – investigating optimization options for decision trees in embedded systems Resurseffektiv Representation av Maskininlärningsmodeller

Combining embedded systems and machine learning models is an exciting prospect. However, to fully target any embedded system, with the most stringent resource requirements, the models have to be designed with care not to overwhelm it. Decision tree ensembles are targeted in this thesis. A benchmark model is created with LightGBM, a popular framework for gradient boosted decision trees. This model is first transformed and regularized with RuleFit, a LASSO regression framework. Then it is further optimized with quantization and weight sharing, techniques used when compressing neural networks. The entire process is combined into a novel framework, called ESRule. The data used comes from the domain of frequency measurements in cellular networks. There is a clear use-case where embedded systems can use the produced resource optimized models. Compared with LightGBM, ESRule uses 72ˆ less internal memory on average, simultaneously increasing predictive performance. The models use 4 kilobytes on average. The serialized variant of ESRule uses 104ˆ less hard disk space than LightGBM. ESRule is also clearly faster at predicting a single sample.
A NOVEL MACHINE LEARNING SYSTEM

The huge volume of online reviews makes it difficult for a human to process and extract all significant information to make decisions. As a result, there has been a trend to develop systems that can automatically summarize opinions from a set of reviews. In this respect, the automatic classification and information extraction from users’ comments, also known as sentiment analysis (SA) becomes vital to offer users the best responses to users’ queries, based on their preferences. In this paper, a novel system hat offers personalized user experiences and solves the semantic-pragmatic gap was presented. Having a system for forecasting sentiments might allow us, to extract opinions from the internet and predict online user’s favorites, which could determine valuable for commercial or marketing research. The data used belongs to the tagged corpus positive and negative processed movie reviews introduced by Pang and Lee[1]. The results show that even when a small sample is used, sentiment analysis can be done with high accuracy if appropriate natural language processing algorithms applied.
Playing Angry Birds with a Neural Network and Tree Search

In this paper, we introduce a method that combines a deep neural network and tree search for an Angry Birds AI agent. This neural network is trained first by supervised learning from another expert and then by reinforcement learning from self-play. Tree search enhanced by the neural network trained with supervised learning is used to strengthen the agent’s game play policy during reinforcement learning. To the authors’ knowledge, this is the first time that this approach is used to develop an Angry Birds AI agent. Our agent participates in the 2018 Angry Birds AI Competition and will be made available after the competition. The authors hope other researchers can gain some pieces of useful information from our findings and make deep learning more popular in Angry Birds AI Competition.
Numerical Anchoring, Perceived Returns, and Asset Prices∗

Investors often observe financial information in dollar amounts, even though investment performance is measured in percentage returns. I develop a model in which observing financial information in dollar amounts may distort investors’ perception of returns. When converting from dollar amounts to percentage returns and vice-versa, individuals anchor on a customary dollar amount and do not fully adjust to amounts that substantially deviate from it. Behavior of this sort leads to predictable mistakes. I empirically test two predictions of the model in one online experiment and one field experiment with more than 170,000 investors from a brokerage firm. The online experiment leverages a within-subject design to confirm the first prediction: individuals underestimate the future value of large dollar amounts relative to small dollar amounts for a given percentage return and a time horizon. I use the field experiment to confirm the second prediction: investors respond more strongly to the same percentage gains when they are described in larger dollar amounts. This simple treatment increases investor purchase of a financial product by 24.5%. Having found supportive evidence for the model, I apply it to understand the effect of share prices — quoted in dollar terms — on the behavior of stock returns. Consistent with the model, I find that stocks with similar past returns, but higher share prices, exhibit stronger return predictability and are less likely to show reversal in the long-run, controlling for size. Based on this finding, I propose a trading strategy that enhances the performance of momentum strategies in the stock market. ∗I am extremely grateful to my advisors John Campbell, Xavier Gabaix, and Andrei Shleifer, for their support and guidance. I also thank Jenna Anders, Claudia Goldin, Adriano Fernandes, Dev Patel, and Adrienne Sabety for helpful comments. I acknowledge generous financial assistance from the Alfred P. Sloan Foundation Pre-Doctoral Fellowship on the Economics of an Aging Workforce, awarded through the NBER. I thank additional fund from the Paul M. Warburg Fund and the Jorge Paulo Lemann Research Grant at Harvard. The two experiments in this paper went through IRB Review: IRB-Fall-2018 (Insper, Brazil), IRB18-1575 (Harvard), and IRB19-1627 (Harvard). I thank Klenio Barbosa, Sergio Firpo, Marcos Lisboa for facilitating the IRB review of the field experiment in Brazil. All errors are my own. †Harvard University. e-mail: paulocosta@g.harvard. edu
Public Communication as a Tool to Implement Environmental Policies

Climate change is one of the greatest challenges of our times. We humans must change our behavior in the short run to mitigate the effects of climate change in the long run. Policymakers develop environmental policies to motivate this behavioral change. However, people may nonetheless fail to change to more sustainable practices, defeating the effectiveness of environmental policies. Public communications, such as mass media campaigns, can enhance compliance with environmental policies, but they have not yet lived up to their potential. In this contribution, I propose that environmental communication would be more effective if public communicators took account of the (often counterintuitive) social and psychological processes that influence sustainable behaviors. I present research evidence of the impact of cognitive biases (discounting, control perceptions, optimism bias, denial, defensive avoidance, and reactance), emotions (fear and hope), and expectations (about the intentions of the communication source and other people's environmental behaviors) on the power of environmental public communication as a tool to promote sustainable behaviors. If social and psychological processes are neglected in the design of environmental public communications, messages can backfire and achieve the opposite of what was intended. To improve environmental communications, I propose three communication design suggestions: keep it simple, balance the message, and provide an action perspective.
Aspirations and women's empowerment: Evidence from Kyrgyzstan

Can having higher aspirations, or goals for the future, contribute to the empowerment of women? A growing literature shows that aspirations increase a host of forward-looking economic and political behaviors, from entrepreneurship to civic engagement. This has stimulated interest in development interventions aimed at reducing behavioral poverty traps. At the same time, a wealth of literature suggests that women’s empowerment and involvement in decision-making can be welfare-improving. It can increase household income and asset wealth by ensuring that women are economically active and have high levels of human capital; increase technical efficiency on the farm; and improve health, nutrition, and education outcomes for children. Linking these two literatures, we posit that one route to women’s empowerment may be to raise aspirations—either those of a woman herself, or those of her husband, who often wields considerable influence over her decision-making authority and access to resources. We find that having a husband who sets ambitious goals for himself predicts more egalitarian gender attitudes for both the husband and his wife. Higher aspirations on the part of wives also predict more egalitarian gender attitudes (for both the husband and his wife), but they additionally predict greater involvement of women in household decision-making. This suggests that efforts to fuel either men’s or women’s ambition can shift gender attitudes, but that targeting women is the more effective way to build women’s decision-making power. ∗Katrina Kosec is a Senior Research Fellow, Kamiljon Akramov is a Research Fellow, and Jie Song is a Research Analyst at the International Food Policy Research Institute (IFPRI), 1201 Eye Street, NW Washington, DC 20005. Bakhrom Mirkasimov is an Assistant Professor at Westminster International University in Tashkent (WIUT). This research was part funded by the CGIAR Research Program on Policies, Institutions, and Markets led by IFPRI, and the Ministry of the Russian Federation under IFPRIâĂŹs Collaborative Research and Capacity Strengthening Program for Enhancing Agricultural Productivity and Food and Nutritional Security in Central Asia. Comments and suggestions are welcome and may be emailed to k.kosec@cgiar.org. We are grateful to Cheryl Doss and Jo Swinnen for comments and helpful discussions. All errors are the sole responsibility of the authors.
UWB Channel Characterization for Compact L-Shape Configurations for Body-Centric Positioning Applications

This letter presents an analysis on the body-centric channel parameter classification for various compact three-base-station L-shape configurations utilizing only a two-dimensional (2-D) plane for installation. Four different L-shape configurations (<italic>xz</italic>/<italic>yz</italic> plane) are studied (facing-front/side/back) by varying the position of the base stations in an indoor environment. Results and analyses highlight the variation of the channel parameters with respect to the orientation of the base station configurations and the presence of the human subject. Channel parameter values (peak power delay profile/rms delay spread <inline-formula><tex-math notation="LaTeX">$({{\boldsymbol{\sigma }_{\boldsymbol{\tau }}}})$</tex-math></inline-formula>/Kurtosis<inline-formula><tex-math notation="LaTeX">$(\boldsymbol {\kappa })$</tex-math></inline-formula>) are reported for line-of-sight (LOS: −65 to −50 dB/0.5–5 ns/40–60) and non-line-of-sight (NLOS: −80 to −65 dB/10–25 ns/5–25). The three dimensional (3-D) localization accuracy obtained is highest (1–3 cm) for the <italic>xz</italic> plane L-shape configuration facing-front, which has maximum number of LOS links (70%). The accuracy decreases by 1–2 cm for the <italic>xz</italic> plane L-shape configuration facing-back due to the increase in NLOS links (70%) between the wearable antennas and the base stations.
IMAGE RECTIFICATION AND SYNTHESIS FOR AN IMAGE BASED AUTOMATIC GUIDANCE SYSTEM IN A 3D MODELLED SITE

We are developing a system to allow localization and guidance of people inside a 3D modelled site using imaging techniques. The system operates in two phases. Firstly, a database of the site has to be built by an expert. Then a user wishing to locate himself in this site takes pictures with a portable device which includes a standard camera and an orientation sensor. These pictures are compared with the contents of the database and if a high correlation is detected, the user can be located inside the site. Finally, the desired path through the site from the actual user’s location to the place where he wants to go is found, allowing the guidance. The database of the site consists in a 3D textured model. To build this database, an omnidirectional 3D sensor has been developed. A rotation invariant representation of locations inside the site is computed using a virtual omnidirectional sensor. Two steps are necessary to match the user’s images with the 3D database. Firstly, the 3D database has to be dynamically projected by this virtual sensor onto 2D omnidirectional images using an optimized method. Secondly, the user’s images have to be projected by the same virtual sensor using information from the orientation sensor. In this paper, we focus on the following two tasks: omnidirectional image synthesis and image rectification.
A RANKING AND PRIORITIZING METHOD FOR BRIDGE MANAGEMENT

Bridge networks are major capital assets which require continuing investment in order to maintain the bridges within acceptable limits of safety and serviceability. Capital budgeting decisions at the network level are commonly based on ranking procedures to define priorities for the distribution of available funds among the different projects in a network. Ranking and prioritizing procedures have been widely used by several departments of transportation to evaluate and select bridge projects as these procedures provide the insight needed for the decision making process. This paper presents the main ranking procedures currently used by transportation agencies and proposes an alternative innovative ranking method for bridge networks, based on the Multi-Attribute Utility Theory (MAUT). The theory enables the decision makers to include multiple and conflicting criteria and to incorporate qualitative and quantitative measurements in the ranking process. In addition, the theory provides flexibility for the decision makers in expressing their degree of satisfaction with each bridge attribute and captures the decision makers’ attitude toward risk. The proposed framework includes the development of a hierarchy structure, defining the objectives and the decision criteria, and presenting an innovative technique to develop the necessary utility functions. A case study is used to demonstrate the applicability and the validity of the proposed ranking procedure.
Barley and Emmer improve redox status and modulate some immune parameters in rats

Barley and Emmer contain many antioxidant compounds, but their biological effects in vivo, particularly at the level of redox status and immune functions have not been investigated. The objective of this study is to evaluate the physiological responses of some immunological and redox parameters to dietary barley and emmer flours in a LA RIVISTA DI SCIENZA DELL’ALIMENTAZIONE, NUMERO 1, GENNAIO-MARZO 2010, ANNO 39 18 rat model. Experimental diets, one rich in barley and the other in emmer flour (53%) have been fed for 6 weeks to growing male rats. After the experimental period total polyphenols content, total antioxidant capacity and protein carbonyl concentration were measured in plasma. Lymphocytes were isolated from mesenteric limphonodes and cellular proliferation and T-cell subpopulations were analyzed. The barley and emmer flours showed elevated phenolic contents, induced elevated plasmatic antioxidant capacity and low oxidative status. The proliferative responses and the CD4+/CD8+ ratios of mesenteric lymphocytes of rats fed with barley and with emmer were also elevated without significative differences between them. Parole chiave: orzo, farro, linfociti mesenterici, stato redox, ratti.
FAB: Toward Flow-aware Buffer Sharing on Programmable Switches

Conventional buffer sizing techniques consider an output port with multiple queues in isolation and provide guidelines for the size of the queue. In practice, however, switches consist of several ports that share a buffering chip. Hence, chip manufacturers, such as Broadcom, are left to devise a set of proprietary resource sharing algorithms to allocate buffers across ports. This algorithm dynamically adjusts the buffer size for output queues and directly impacts the packet loss and latency of individual queues. We show that the problem of allocating buffers across ports, although less known, is indeed responsible for fundamental inefficiencies in today's devices. In particular, the per-port buffer allocation is an ad-hoc decision that (at best) depends on the remaining buffer cells on the chip instead of the type of traffic. In this work, we advocate for a flow-aware and device-wide buffer sharing scheme (FAB), which is practical today in programmable devices. We tested FAB on two specific workloads and showed that it can improve the tail flow completion time by an order of magnitude compared to conventional buffer management techniques.
FROM SCOUR TO DUNE MIGRATION: UNDERSTANDING AND PREDICTING SEABED MOBILITY NEAR OFFSHORE STRUCTURES AT DIFFERENT SPATIAL SCALES

Several offshore wind farms (OWF) are currently operating or being developed on a series of sand ridges on the Belgian continental shelf known as the Flemish Banks. In order to guarantee the stability of structures such as subsea cables and turbine foundations, the minimum and maximum projected bed level over the project lifespan should be known. Two applications are discussed in which sea bed dynamics are constrained to optimise the design of OWFs. The Jacket Scour Predictor (JaSP) model, a data –driven model of time-dependent scour depth development for jacket foundation, is presented. The model calibration allows to identify potential shortcomings of existing formulations. The estimated sea bed level (ESBL) methodology allows to define defendable bounding values of expected sea bed mobility in the absence of structures, based on a statistical analysis of dune migration rates.
Quantum-enhanced Machine Learning in the NISQ era

Quantum computation technologies have reached a new level of sophistication with the release of the first commercial offerings. Likewise, Machine Learning is popular for use-cases in both industry and research. With Quantum Machine Learning, one hopes to combine both areas in a symbiotic relationship to achieve an advantage in artificial intelligence with the use of quantum technologies. Recently presented approaches make use of quantum technologies in combination with classical hardware resources in order to mitigate the problems imposed by shortcomings of quantum computers of the current generation. Some of these approaches use quantum circuits with free parameters, which are optimized to solve problems and objectives in Machine Learning. This work presents a concept for automated modelling of these quantum circuits, with the goal of constructing suitable circuits for the task of classification. The concept is implemented in a prototype and validated in experiments.
Relationship Between Wind Speed and Gas Exchange

Relationships between wind speed and gas transfer, combined with knowledge of the partial pressure difference of CO2 across the air-sea interface are frequently used to determine the CO2 flux between the ocean and the atmosphere. Little attention has been paid to the influence of variability in wind speed on the calculated gas transfer velocities and the possibility of chemical enhancement of CO2 exchange at low wind speeds over the ocean. The effect of these parameters is illustrated using a quadratic dependence of gas exchange on wind speed which is fit through gas transfer velocities over the ocean determined by the natural-14C disequilibrium and the bomb-14C inventory methods. Some of the variability between different data sets can be accounted for by the suggested mechanisms, but much of the variation appears due to other causes. Possible causes for the large difference between two frequently used relationships between gas transfer and wind speed are discussed. To determine fluxes of gases other than CO2 across the air-water interface, the relevant expressions for gas transfer, and the temperature and salinity dependence of the Schmidt number and solubility of several gases of environmental interest are included in an appendix.
Effects of Angry Birds-like Live Streaming on Working Memory

This paper investigates whether and to which degree watching Angry Birds-like live streaming could affect the spectator’s working memory (WM). We prepared Angry Birds-like gameplay on the Twitch streaming platform in which a domino effect based on Rube Goldberg Machine (RGM) mechanism is featured in each generated game level. The spectators’ working memory are measured using the N-back task, and our results show a possibility that watching the proposed live streaming can increase the WM performance.
School of Information

• Albright, Kendra S. , Professor • Boon, Belinda (2006), Associate Professor, Ph.D., University of Texas, Austin, 2006 • Campana, Kathleen (2017), Assistant Professor, Ph.D., University of Washington, 2018 • Gracy, Karen (2007), Associate Professor • Hajibayova, Lala (2014), Assistant Professor, Ph.D., Indiana University, Bloomington, 2014 • Harper, Sarah M. (2001), Professor, Ph.D., Kent State University, 2003 • Hegmann, Elda (2011), Assistant Professor, Ph.D., Laval University, 2003 • Hudak, Christine A. (2012), Professor, Ph.D., Cleveland State University, 1998 • Jakli, Antal I. (1989), Professor, Ph.D., University of Budapest, 1986 • Martens, Marianne (2012), Associate Professor, Ph.D., Rutgers, The State University of New Jersey, 2012 • Matteson, Miriam L. (2012), Associate Professor, Ph.D., University of Maryland, 2009 • Meehan, Rebecca A. (2012), Associate Professor, Ph.D., Case Western Reserve University, 1997 • Nichols, Mary A. (1996), Senior Lecturer, M.L. S. , Kent State University, 1993 • Robins, David B. (2004), Associate Professor • Salaba, Athena (1993), Professor • Sherman, Paul J. (2013), Assistant Professor, Ph.D., University of Texas at Austin, 1997 • Smith, Catherine L. (2010), Associate Professor • Soyka, Heather (2017), Assistant Professor • Zeng, Marcia L. (1992), Professor • Zhang, Yin (1999), Professor
Geomembrane and pipe design issues in deeper heap leach pads

The design of heap leach pads over 150 m thick in large mining projects has occurred often in the last decade due to economic issues, restrictive topographic conditions, lack of space on the property or mine concession boundaries, reduction of closure and remediation costs, and reduction of the availability of agricultural land. In general, the objective has been to reduce the impact of mining activity by designing deeper ore heaps over smaller areas, which requires rigorous design and proper selection of geosynthetic components. This paper presents the experience of designing deeper heap leach pads, mainly valley fill facilities, in which solutions to the issues related to the high loads affecting the behavior of the geomembrane and solution collection pipes have been successfully implemented in the design and construction of the facilities. Also, a revision of historical high-load pipe deflection tests and numerical modeling is presented. In addition, recommendations for implementation and execution of high-load laboratory puncture tests, the need for high-load large-scale deflection tests in large pipes, and the selection criteria and calibration of numerical models for pipe deflection analysis are presented in order to simulate real and critical field conditions. This may lead to future, more detailed studies.