Miracle-dz/UniFAR-FTUs huggingface dataset:
Dedicated spectrometers based on diffractive optics: Design, modelling and evaluation

Abstract The described design of diffractive optical elements for low cost IR-spectrometers gives a built-in wavelength reference and allows ‘spectral arithmetic’ to be implemented in the optical performance of the DOE. The diffractive element combines the function of the lenses and the grating and eliminates the need for alignment of those components in the standard scanned grating spectrometer design. The element gives out a set of foci, each with one spectral component, which are scanned across a detector, thus relaxing the demands for scan angle control. It can thus be regarded as an alternative solution to a beam splitter and band pass filter instrument. Software tools have been designed to ease the adaptation of the design to different applications. To model the performance of the spectrometers we have implemented a scalar Rayleigh-Sommerfeldt diffraction model. The gold-coated elements are produced by injection moulding using a compact disc (CD) moulding technique and mould inlays mastered by e-beam lithography. The optimized selection of wavelength bands and the classification of the measured signal use a combination of principal component analysis and robust statistical methods. Typical applications will be material characterization of recycled plastics and gas monitoring. Spectrometers for two different applications have been built and tested. Comparisons between the design goals and the measured performance have been made and show good agreements.
Setting up a Digital Twin Assisted Greenhouse Architecture

: The present article contains a study about utilizing the Digital Twins concept in the field of contemporary agricultural production. Through this study, an exemplary architecture has been developed regarding the conversion of a conventional greenhouse to a digital greenhouse. A digital greenhouse modus operandi features a great number of advantages compared with the traditional workflow in a conventional greenhouse. The purpose of the work is to propose tools for assisting the possible reduction of the consumption of the used resources for the crops. This requires the application of automation of tools for cultivation such as Controlled Environment Agriculture (CEA). The article shows that the Digital Twins concept can immensely contribute towards controlling the agricultural environment and at the same time improve performance and quality while reducing the consumption of resources for a variety of crops. The proposed workflow starts by identifying the parameters that need to be taken into account and finally proposes several cyber and physical tools for setting up a Digital Twin for the case of a greenhouse. The objective of this study was the development of a DT architecture that would be able to optimize productivity in the context of CEA applications.
Design and Performance Evaluation of Mobile mmWave Initial Access Protocols

The extension of 5G networks towards the millimeter-wave (mmWave) spectrum is a reaction to the exponential growth of wireless data traffic and the need for significantly higher data rates [1]. This move has been mirrored with Wi-Fi amendments IEEE 802.11ad/aj/ay, and there is already speculation whether 6G will use even higher frequencies than 5G. However, the drastic change of carrier frequency comes with new challenges, primarily due to the hostile propagation characteristics of mmWaves [2]. To overcome these, antenna arrays and analog/hybrid beamforming transceiver ought to be used [3]. MmWave communication thereby becomes directional such that even detecting an available network becomes cumbersome. Therefore, base station (BS) and user equipment (UE) side pencil beams have to be aligned precisely by means of beam management (a.k.a. beamsteering), in particular, beam training protocols aim to establish the mmWave link in the first place. Naturally, when taking user mobility into account, the beams need to be realigned continuously by means of beam tracking and switching protocols to ensure consistent multi-Gbps connectivity [4, 5].
LiquidRust: Refinement Types for Imperative Languages with Ownership

Software correctness is a central goal for software development. One way to improve correctness is using strong and descriptive types, that express correctness properties as types, and verifying their compatibility with the programs by using type systems. In particular Refinement Types demonstrated, that even with a verification system that is restricted to a decidable logic, intricate properties can be expressed in a lightweight and gradual way and verified automatically. However existing approaches for adapting Refinement Types from functional to imperative languages showed that the presence of mutable data and references entails compromising on at least one of the core features of Refinement Types, which are the automated, decidable verification and minimal, approachable specification. This thesis presents a Refinement Type system with minimal compromises by taking advantage of Rust’s restriction to unique mutable references. To this end, we augment a flow-sensitive Refinement Type system with a varying typing context that facilitates type updates and tracks reference destinations. We define a notion of subtyping on this context and argue for the soundness of our typing rules. Furthermore we implement a prototype verifier for an extended version of our type system as a plugin to the Rust compiler and evaluate its efficacy on a selection of examples, demonstrating automatic verification and minimal, approachable specification. Our work provides insights into the unique advantages that Rust’s ownership system provides for a Refinement Type system. Additionallywe analyse a large dataset of existing Rust code to gauge how relevant language features are used in practice.
Bayesian Reinforcement Learning for Single-Episode Missions in Partially Unknown Environments

: We consider planning for mobile robots conducting missions in real-world domains where a priori unknown dynamics affect the robot’s costs and transitions. We study single-episode missions where it is crucial that the robot appropriately trades off exploration and exploitation, such that the learning of the environment dynamics is just enough to effectively complete the mission. Thus, we propose modelling unknown dynamics using Gaussian processes, which provide a principled Bayesian framework for incorporating online observations made by the robot, and using them to predict the dynamics in unexplored areas. We then formulate the problem of mission planning in Markov decision processes under Gaussian process predictions as Bayesian model-based reinforcement learning. This allows us to employ solution techniques that plan more efﬁciently than previous Gaussian process planning methods are able to. We empirically evaluate the beneﬁts of our formulation in an underwater autonomous vehicle navigation task and robot mission planning in a realistic simulation of a nuclear environment.
Design and fabrication of a high-density 2D fiber array for holographic switching applications

Holographic two-dimensional beam steering using a pixellated spatial light modulator (SLM) is a flexible and accurate technique to implement advanced space-switching, building-block functions for optical network nodes. We present the design of a specific fiber array for large-capacity holographic switches. We use two-dimensional and irregular array topology to optimize the space-bandwidth product of the SLM and to avoid crosstalk due to the coupling of higher diffraction orders. The fiber-array module is integrated by inserting the fibres through a pair of etched silicon plates aligned and fixed using a steel mount. The device is tested through near-field and fiber-to-fiber free space coupling tests. An average fiber-to-fiber insertion loss of 1 dB is achieved for a prototype containing 64 fibers.
The Impact of Videos on Higher Education Courses

Nowadays, higher education institutions aim to adapt their teaching methods with the recent technological evolution, which affects not only the learners but also the instructors. Such a change in society can be used to minimize a very common concern in higher education: students’ academic lack of success. This lack of success can be addressed through strategies to captivate and engage students with the course topics and the learning system itself. An example of such a strategy includes the adoption and integration of different formats of content and study materials, such as videos. With this research, we analyze how students’ acquisition of knowledge and academic success is influenced by the use of course contents such as videos. Hence, we explore two different scenarios regarding their creation: videos produced by students and videos produced by professors/instructors. Subsequently, these videos were uploaded into a learning software application (Learning Management System), where students from the course were able to access and visualize them
Material intensity, productivity and economic growth

Many models of economic growth exclude materials from the production function. Growing environmental pressures and resource prices suggest that this may be increasingly inappropriate. This paper explores the relationship between material intensity, productivity and national accounts using a panel data set of manufacturing subsectors in the United States over 47 years. The ﬁrst contribution is to identify sectoral production functions that explicitly incorporate material inputs while allowing for heterogeneity in both technology and productivity. The second contribution is that the paper ﬁnds a negative correlation between material intensity and total factor productivity (TFP) — sectors that are less material-intensive have higher rates of productivity. This ﬁnding is replicated at the ﬁrm level. We propose tentative hypotheses to explain this association, but testing is left for further work. Depending upon the nature of the mechanism linking a reduction in material intensity to an increase in TFP, the implications could be signiﬁcant: policies that reduce material intensity, such as shifting taxation from labour to natural resources, would increase productivity and economic growth. A third contribution is to suggest that an empirical bias in productivity, as measured in national accounts, may arise due to the exclusion of material inputs. Current conventions of measuring productivity in national accounts may overstate the productivity of resource-intensive sectors relative to other sectors.
Unity and diversity in Asian American language variation: Data from Chinese, Filipino, Korean, and Vietnamese Americans

The present study examined sociophonetic variation in a small sample of Asian Americans in Boston, Massachusetts representing four ethnic groups: Chinese, Filipino, Korean, and Vietnamese. Analyzing these speakers’ English production in tasks eliciting both casual and careful speech, we focused on four linguistic features comprising features observed in Eastern New England and in certain Asian American groups. Three features (R-DELETION, LVOCALIZATION, L/R-CONFLATION) were coded auditorily and one (LOW BACK RAISING of /ɑ/ to /ɔ/) acoustically. Overall, results showed low use of Eastern New England features (R-DELETION, LOW BACK RAISING), high use of L-VOCALIZATION, and no use of L/R-CONFLATION, but also significant differences in specific patterns of use according to ethnicity and speech style. Ethnicity was a significant predictor of the occurrence of R-DELETION and LVOCALIZATION, and also a significant predictor of first formant (F1) values in the low back vowels, although no clear vowel merger was found. Careful speech showed lower rates of R-DELETION and L-VOCALIZATION and less overlap of the low back vowels as compared to casual speech. These findings reveal similarities and differences in speech production among ethnically diverse Asian Americans and highlight the need for further investigation of phonetic variation within this community.
Packet Loss Detection in the Data Plane

The task of monitoring the operation of computer networks is a key component to ensure the performance of current distributed systems. In particular, we are interested in the use of monitoring to detect, in a timely manner, network anomalies such as links that exhibit excessive packet loss. In this work we survey the main techniques that can be used to detect network anomalies, giving emphasis to techniques that leverage the availability of programmable switches to increase the efficiency of the monitoring tasks. Inspired by the advantages and disadvantages of related work, we propose a research road-map that aims at improving the task of localizing faulty links in networks that have a mix of programmable and non-programmable switches.
Skeleton Clustering: Graph-Based Approach for Dimension-Free Density-Aided Clustering

Density-based clustering can identify clusters with irregular shapes and has intuitive interpretations, but struggles with large-dimensional data due to the curse of dimensionality. We introduce a graph-based clustering framework called Skeleton Clustering to adopt density-based clustering idea to multivariate and even high-dimensional data. The proposed framework constructs a graph representation of the data as a first step and combines prototype methods, density-based clustering, and hierarchical clustering. We propose surrogate density measures based on the skeleton graph that are less dependent on the dimension and have meaningful geometric interpretations. We show by empirical studies that the proposed skeleton clustering method leads to reliable clusters in multivariate and even high-dimensional data with irregular shapes.
Valkyrie: Improving Fuzzing Performance Through Deterministic Techniques

Greybox fuzzing has received much attention from developers and researchers due to its success in discovering bugs within many programs. However, randomized algorithms have limited fuzzers’ effectiveness. First, branch coverage feedback that is based on random edge ID can lead to branch collision. Besides, state-of-the-art fuzzers heavily rely on randomized methods to reach new coverage. Even fuzzers with a solver rely on incorrect assumptions, limiting their ability to solve branches and forcing them to turn to randomness as a last resort.We believe deterministic techniques deliver consistent, predictable, reproducible results. We propose Valkyrie, a greybox fuzzer whose performance is boosted primarily by deterministic techniques. Valkyrie combines collision-free branch coverage with context sensitivity to maintain accuracy while introducing an instrumentation removal algorithm to reduce overhead. It also pioneers a new mutation method, compensated step, allowing fuzzers that use solvers to adapt to real-world fuzzing scenarios without using randomness. We implement and evaluate Valkyrie’s effectiveness on the standard benchmark Magma, and a wide variety of real-world programs. Valkyrie triggered 21 unique integer and memory errors, 10.5% and 50% more than AFL++ and Angora, respectively. Valkyrie shows little to no variance across ten trials and is the fastest to trigger half of the bugs. Valkyrie reached 8.2% and 12.4% more branches in real-world programs, compared with AFL++ and Angora, respectively. We also verify that our branch counting and mutation method is better than the state-of-the-art, which shows that deterministic techniques trump random techniques in consistency, predictability, reproducibility, and performance.
The Application of the Activated Carbon from Cordia africana Leaves for Adsorption of Chromium (III) from an Aqueous Solution

The objective of this study is to investigate the adsorption performance of activated carbon derived from the leaves of Cordia africana for the removal of Cr (III) from an aqueous solution. The plant sample was collected, washed, dried, grounded, and sieved at 125 μm mesh size. Adsorbent activation was done using H3PO4 at concentrations of 25–85% and pyrolysis temperature of 400–500°C. The activated carbon was characterized by proximate, SEM, BET, and FTIR analyses. A batch adsorption study was conducted to determine the effect of contact time, adsorbent dose, initial chromium concentration, and mixing speed on Cr (III) removal. The regeneration of the activated carbon was investigated by using 1 M of HNO3 as a desorbing solution for seven cycles. At optimum acid concentration and pyrolysis temperature, a surface area of 700 m2/g was recorded. The moisture content, volatile matter, ash composition, fixed carbon, and bulk density of the activated carbon were found to be 5.3%, 24.2%, 6.2%, 64.3%, and 0.75 g/mL, respectively. The SEM and FTIR analyses indicated that the surface morphology was full of cracks and different peaks were associated with plenty of functional groups, respectively. The maximum Cr (III) removal was attained at a contact time of 180 min (89%), adsorbent dose of 1.5 g (54%), initial concentration of 0.6 g/L (47%), and mixing speed of 300 rpm (64%). The equilibrium data were better described by Freundlich isotherm at R2 value of 0.88, which implies that the adsorption process is conducted on a heterogeneous surface. The pseudo-first-order kinetics model with R2 value of 0.99 best fits with the equilibrium data, which implies that physisorption controls the adsorption kinetics. Generally, it can be concluded that this locally prepared adsorbent is promising for the removal of chromium from industrial wastewater, but further factorial approach assessment has to be checked.
PROCESS MINING IN CANCER PREDICTION OF PROGNOSIS BY USING CLASSIFIERS BASED ON MIXED VARIABLES

A healthy decision-making process for health of the individual is a major challenge in this day and age of abundance of information all over the place. Machine learning, data mining and computation statistics have become among the top research areas which enable individuals empowered to make important decisions that will improve the results of any field. A high demand for data handling is evident in the healthcare sector, since the increase in the number of patients is proportional the increase in population and lifestyle changes. Strategies for early diagnosis and prognosis predictions of diseases are of great importance at the present time to offer better healthcare for the entire human population. Data mining is an advantage in the development of high-quality and effective models for applications to predict health. Since cancer has been everywhere in recent years, information from the cancer registry are being used as medical information in this research. The principal goal of the thesis is to create an efficient and effective classification model for predictions of prognosis for cancer. The majority of the current system is based on diagnosis prediction model from surveys or screening data since the data is readily available and simple to gather due to the insensitivity of the variables involved in these studies. For prognosis prediction, it requires specific information about the patients who are receiving treatment for a recognized disease. Hospitals and community registries managed by the state constitute the most important sources for data gathering. Electronic hospital records that are well-maintained that include histopathology data are not accessible in India to researchers. Thus, data on cancer obtained from an US accessible data centre was utilized in this study to conduct all experiments. This research study is a model that improves accuracy of predictions by utilizing the appropriate methods for data mining for each stage. Prognosis refers to the survival percentage of cancer patients generally, but is also a measure of how severe the cancer in the future timeline that the person. The two-fold goal of this study is to discover the key response variables that are a part of the prediction system used to determine the prognosis as well as enhance the predictive models.
AI for Industry at the Computer Science Faculty of the Free University of Bozen-Bolzano

We provide an overview of some of the main research lines and project activities focused on AI for industry and developed within the Faculty of Computer Science of the Free University of Bozen-Bolzano (unibz). Each section refers to a research line connected to a funded project. In particular, we highlight project details, participants
Transmission electron microscopy study on FeSi2 nanoparticles synthesized by electron-beam evaporation

We have synthesized epitaxially grown iron disilicide (FeSi2) nanoparticles using an electron-beam evaporation technique and characterized them by transmission electron microscopy (TEM). An Fe film was deposited on a Si(100) substrate, followed by thermal annealing at 1073K for 2h. It was found that epitaxially grown nanoparticles with an average size of ∼10nm were formed just beneath the Si surface, suggesting that the deposited Fe atoms diffuse into the substrate. Every single phase of nanoparticles was examined in detail by TEM observation, nanobeam electron diffraction, and energy-dispersive x-ray spectroscopy. Plan-view and cross-sectional TEM observations revealed that these nanoparticles consist of α-, β-, and γ-FeSi2. It was found that the morphology of nanoparticles is closely related to the phases. The α and β phases consist of angled hemisphere and asymmetric triangle-shaped nanoparticles, respectively, while the γ phase consists of hemispherical or columnar-shaped nanoparticles. These particle.
Validity of effective-medium theory for optical properties of embedded nanocrystallites from ab initio supercell calculations

The optical properties of Ge and Si nanocrystals embedded in a crystalline matrix are calculated using an ab initio supercell method. Spectra of effective nanocrystallite materials including confinement effects are extracted by means of effective-medium theory. Spectra for different composites are calculated and compared to results obtained directly. The applicability of the Maxwell-Garnett effective-medium theory is demonstrated for photon energies below the main absorption peak of the host material. The minimum crystallite diameter treatable by effective-medium theory is found to be approximately 1 nm. The transferability of the crystallite spectra is discussed, its limits are investigated.
Designing Computer Games to Help Physics Students Understand Newton's Laws of Motion

This research, explores, the design of a computer environment for helping science students to learn about Newtonian dynamics. The learning environment incorporates games set in the context of a Newtonian computer microworld, where students have to control the motion of a spaceship in order to achieve goals such as hitting a target or navigating a maze. The purpose of the games is to focus the students' attention on various aspects of the implications of Newton's laws. A set of general design principles guided the design of the games and microworld. These include: (I) represent the phenomena of the domain clearly; (2) eliminate irrelevant complexities from the computer microworld; (3) focus the students on as peers of their knowledge.that need revising; (4) facilitate the use of problem-solving heuristics; (5) encourage the application of relevant knowledge from other domains; and (6) encourage better ways of representing and thinking about the domain. A controlled study indicated that playing the games im.