Miracle-dz/UniFAR-FTUs huggingface dataset:
CAMBRIDGE WORKING PAPERS IN ECONOMICS Carbon pricing and the elasticity of CO2 emissions

We study the impact of carbon pricing on CO2emissions across five sectors for a panel of 39 countries over 1990-2016. Using newly constructed sector-level carbon price data, we implement a novel approach to estimate the changes in CO2emissions associated with (i) the introduction of carbon pricing regardless of the price level; (ii) the implementation effect as a function of the price level; and (iii) post-implementation marginal changes in the CO2price.We find that the introduction of carbon pricing has reduced growth in CO2emissions by 1% to 2.5% on average relative to counterfactual emissions, with most abatement occurring in the electricity and heat sector. Exploiting variation in carbon pricing to explain heterogeneity in treatment effects, we find an imprecisely estimated semielasticity of a 0.05% reduction in emissions growth per average $1/metric ton (hereafter abbreviated as: ton) of CO2.After the carbon price has been implemented, each marginal price increase of $1/tCO2 has temporarily lowered the growth rate of CO2emissions by around 0.01%. These are disappointingly small effects. Simulating potential future emissions reductions in response to carbon price paths, we conclude that –in the absence of complementary non-pricing policy interventions – carbon pricing alone, even if implemented globally, is unlikely to be sufficient to achieve emission reductions consistent with the Paris climate agreement. Reference Details CWPE 20116 Published 30 November 2020
Rapid Rule-Based Reward Reversal and the Lateral Orbitofrontal Cortex

Abstract Humans and other primates can reverse their choice of stimuli in one trial when the rewards delivered by the stimuli change or reverse. Rapidly changing our behavior when the rewards change is important for many types of behavior, including emotional and social behavior. It is shown in a one-trial rule-based Go-NoGo deterministic visual discrimination reversal task to obtain points, that the human right lateral orbitofrontal cortex and adjoining inferior frontal gyrus is activated on reversal trials, when an expected reward is not obtained, and the non-reward allows the human to switch choices based on a rule. This reward reversal goes beyond model-free reinforcement learning. This functionality of the right lateral orbitofrontal cortex shown here in very rapid, one-trial, rule-based changes in human behavior when a reward is not received is related to the emotional and social changes that follow orbitofrontal cortex damage, and to depression in which this non-reward system is oversensitive and over-connected.
Directing Incoming CS Students to an Appropriate Introductory Computer Science Course

Full Paper. Research. We discuss possible ways to direct students to right level of introductory programming. While many schools offer college preparatory or advanced placement courses in computing, there is still, unfortunately, a large part of the "college-ready" population that has no opportunity to learn computing at all before they arrive. Regulation of CS education at the state/province or national level is still rare (but growing). Thus incoming students possess a wide range of skills and knowledge. When coupled with increasing enrollments, this diversity of experience can result in courses having large numbers of both absolute beginners and seasoned coders. Such courses are difficult to teach, intimidate novice students, and bore those with more experience. This can result in low engagement and retention.Unlike mathematics and language arts, introductory courses in CS vary widely from one institution to another in both conceptual material and programming language used. A standard point of entry to college mathematics is a calculus course, with some students instead starting earlier with pre-calculus or an algebra refresher, and others starting out in the second-term calculus course. There is rarely a concern about student skill being hidden by notational or other language differences, because the language of mathematics is close to universal. Similarly, freshman language arts courses in reading and/or writing assume a certain level of skill and maturity of comprehension and expressiveness in the target language; otherwise remedial courses are provided.We investigate placement of incoming first year students into appropriate introductory computer science courses at higher education institutions where there is more than one choice of first course. The goal is to determine the best way to decide which first course would be the most helpful for each student.
EFFECT OF NAOH MOLARITIES ON STRENGTH PROPERTIES OF GROUND PERLITE-BASED GEOPOLYMER MORTARS

Geopolymers are the eco-friendly binders derived from activation of alumina silicates such as fly ash and metakaolin with alkaline activators. Ground perlite, a volcanic alumina silicate, has a potential to be a precursor material for geopolymer production thanks to its high SiO2 content and amorphous form. This paper presents the investigation of geopolymer production with ground perlite and NaOH. For this purpose, mortar mixtures are made with four different NaOH solutions (3M, 6M, 9M and 12M). Mortar specimens with dimensions of 4*4*16 cm are taken from the mixtures and heat curing process is applied to the specimens after casting. The flexural and compressive strength tests are conducted on the mortar specimens at 7 and 28 days of curing. The test results showed that the flexural and compressive strengths of the ground perlite based geopolymer mortars increase as the increment in molarity of NaOH solution, significantly.
Educational Process Mining for Verifying Student Learning Paths in an Introductory Programming Course

This full paper of the research-to-practice category addresses the problem of organizing instructional materials and assessment activities in e-learning courses and their effects on learning outcomes. Usually, the teacher organizes the course sequence according to his didactic-pedagogical strategies and expects this help to guide the student through his learning process in the course. However, unless restrictions are imposed, students may choose to follow different paths than those indicated in the material’s organization. A question emerges from this context: what are the impacts on the students learning outcomes when they take learning paths other than expected by the teacher? In Virtual Learning Environments, student’s interaction with course materials can be stored in the so-called event logs. With the support of Educational Process Mining, it is possible to track the path of how and what specific actions students perform during learning, resulting in process models and historical statistical information. This paper aims to present the application results of PM techniques to verify the students learning paths in an introductory programming course. We used a Moodle event log containing 24605 events collected from 73 undergraduate students. For experiments, we divided this original log file into five other segments of datasets among passed and failed students variations. Techniques to obtain statistical information, Heuristic Miner algorithm to process discovery, and other techniques were applied from the implementations available in ProM Framework and scripts based on PM4Py library. The results showed that overall approved and failed students took different paths and event numbers to perform activities in the course. Besides, we obtained control-flows and frequencies of the activities and connections, thus making it possible to identify the dependencies, which resources started or ended the process, among other things. The analysis of these results provides general and specific information on students’ learning paths and can help teachers observe students’ behavior patterns and progress.
Semantic-Aware Sentiment Analysis: A Survey

Ontologies are known to serve well as a knowledge base for several NLP problems, including sentiment analysis. This report talks about the importance of semantics in the task of sentiment analysis. In this paper, we present a concise survey of various approaches of 1) aspect-based sentiment analysis, 2) ontology-based sentiment analysis and 3) semantics in sentiment analysis, which provide a base for our research. We observe that, although ontologies have proved to be important for various NLP tasks, their beneﬁts in neural sentiment analysis is a much less-explored area of research.
Halo model approach for the 21-cm power spectrum at cosmic dawn

Prior to the epoch of reionisation, the 21-cm signal of the cosmic dawn is dominated by the Lyman-$\alpha$ coupling and gas temperature fluctuations caused by the first sources of radiation. While early efforts to model this epoch relied on analytical techniques, the community quickly transitioned to more expensive semi-numerical models. Here, we re-assess the viability of simpler approaches that allow for rapid explorations of the vast astrophysical parameter space. We propose a new analytical method to calculate the 21-cm power spectrum based on the framework of the halo model. Both the Lyman-$\alpha$ coupling and temperature fluctuations are described by overlapping radiation flux profiles that include spectral red-shifting and source attenuation due to look-back (light-cone) effects. The 21-cm halo model is compared to the semi-numerical code 21cmFAST exhibiting generally good agreement, i.e., the power spectra differ by less than a factor of three over a large range of $k$-modes and redshifts. We show that the remaining differences between the two methods are comparable to the expected variations from modelling uncertainties associated with the abundance, bias, and accretion rates of haloes. While these current uncertainties must be reduced in the future, our work suggests that inference at acceptable accuracy will become feasible with very efficient halo models of the cosmic dawn.
The Kondo problem is a few-body problem.

Quenching of local moments in metals generally induces long-range electronic correlations known as the Kondo screening cloud. We prove that this non-locality is not intrinsically many-body in character, but is rather encoded in the spatial dependence of the natural one-body orbitals that are eigenstates of the single-particle density matrix. Limiting electronic interactions to a few of these orbitals, we find that Kondo correlated ground states can be converged in the thermodynamic limit to exponential accuracy by numerical diagonalization of the resulting few-body problem. An emergent many-body character is only observed for exponentially large Kondo lengths, i.e. in close vicinity to a quantum critical point.
Modelling the speciation and biogeochemistry of iron in oceanic surface waters at the Bermuda Atlantic Time-series Study site

By means of numerical modelling the cycling of iron between its various physical (dissolved, colloidal, particulate) and chemical (redox state and organic complexation) forms in the upper mixed layer of the ocean is analysed. Using the model an initial quantitative assessment is made of how this cycling influences iron uptake by phytoplankton and its loss via particulate export. The model is forced with observed dust deposition rates, mixed layer depths, and solar radiation at the site of the Bermuda Atlantic Timeseries Study (BATS). It contains an optimised ecosystem model which yields results close to the observational data from BATS. Firstly, the results of a zero-dimensional model approach show that the mixed layer cycle strongly influences the cycling of iron between its various forms. This was mainly attributed to the light dependency of photoreductive processes and to the seasonality of primary production. The daily photochemical cycle is driven primarily by the production of superoxide and its amplitude depends on the concentration and speciation of dissolved copper. Model results are largely insensitive to the dominant form of dissolved iron introduced via dust deposition, and also to the form of iron that is taken up directly during algal growth. In the model solutions, the role of the colloidal pumping mechanism depends strongly on assumptions made regarding rates of colloid aggregation and photoreduction rate. Secondly, a one-dimensional approach of the model is coupled with the General Ocean Turbulence Model (GOTM). The combined model was able to simulate the temporal patterns and vertical profiles of dissolved iron in the upper ocean at the BATS site reasonably well. Subsurface model profiles depended strongly on the parameter values chosen for loss processes affecting iron, colloidal aggregation and scavenging onto particles. Current estimates for these parameters result in depletion of dFe. A high stability constant of iron-binding organic ligands is required to reproduce the observed degree of organic complexation below the mixed layer. Solubility of atmospherically deposited iron higher than 2% leads to dissolved iron concentrations higher than observations. Despite neglecting ultraviolet radiation, the model produces diurnal variations and mean vertical profiles of dFe which are in good agreement with observations.
Open-World Learning Without Labels

Open-world learning is a problem where an autonomous agent detects things that it does not know and learns them over time from a non-stationary and never-ending stream of data; in an open-world environment, the training data and objective criteria are never available at once. The agent should grasp new knowledge from learning without forgetting acquired prior knowledge. Researchers proposed a few open-world learning agents for image classification tasks that operate in complex scenarios. However, all prior work on open-world learning has all labeled data to learn the new classes from the stream of images. In scenarios where autonomous agents should respond in near real-time or work in areas with limited communication infrastructure, human labeling of data is not possible. Therefore, supervised open-world learning agents are not scalable solutions for such applications. Herein, we propose a new framework that enables agents to learn new classes from a stream of unlabeled data in an unsupervised manner. Also, we study the robustness and learning speed of such agents with supervised and unsupervised feature representation. We also introduce a new metric for open-world learning without labels. We anticipate our theories and method to be a starting point for developing autonomous true open-world never-ending learning agents.
Learning crystal field parameters using convolutional neural networks

We present a deep machine learning algorithm to extract crystal field (CF) Stevens parameters from thermodynamic data of rare-earth magnetic materials. The algorithm employs a two-dimensional convolutional neural network (CNN) that is trained on magnetization, magnetic susceptibility and specific heat data that is calculated theoretically within the single-ion approximation and further processed using a standard wavelet transformation. We apply the method to crystal fields of cubic, hexagonal and tetragonal symmetry and for both integer and half-integer total angular momentum values JJ of the ground state multiplet. We evaluate its performance on both theoretically generated synthetic and previously published experimental data on CeAgSb_22, PrAgSb_22 and PrMg_22Cu_99, and find that it can reliably and accurately extract the CF parameters for all site symmetries and values of JJ considered. This demonstrates that CNNs provide an unbiased approach to extracting CF parameters that avoids tedious multi-parameter fitting procedures.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating

Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.
Prediction of neonatal mortality in Sub-Saharan African countries using data-level linkage of multiple surveys

Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approaches. This is partly due to disjoint data collection efforts employed across locations, times, and variations of modalities. On the other hand, state-of-the-art methods for small data problem are confined to image modalities. In this work, we proposed a data-level linkage of disjoint surveys across Sub-Saharan African countries to improve prediction performance of neonatal death and provide cross-domain explainability.
Electron hydrodynamics of two-dimensional anomalous Hall materials

We study two-dimensional electron systems in the hydrodynamic regime. We show that a geometrical Berry curvature modifies the effective Navier-Stokes equation for viscous electron flow in topological materials. For small electric fields, the Hall current becomes negligible compared to the viscous longitudinal current. In this regime, we highlight an unconventional Poiseuille flow with an asymmetric profile and a deviation of the maximum of the current from the center of the system. In a two-dimensional infinite geometry, the Berry curvature leads to current whirlpools and an asymmetry of potential profile. This phenomenon can be probed by measuring the asymmetric non-local resistance profile.
MaaSSim - agent-based two-sided mobility platform simulator

Two-sided mobility platforms, such as Uber and Lyft, widely emerged in the urban mobility landscape, bringing disruptive changes to transportation systems worldwide. This calls for a simulation framework where researchers from various and across disciplines may introduce models aimed at representing the dynamics of platform-driven urban mobility systems. In this work, we present MaaSSim, an agent-based simulator reproducing the transport system used by two kind of agents: (i) travellers, requesting to travel from their origin to destination at a given time, and (ii) drivers supplying their travel needs by offering them rides. An intermediate agent, the platform, allows demand to be matched with supply. Agents are decision makers, specifically, travellers may decide which mode they use or reject an incoming offer. Similarly, drivers may opt-out from the system or reject incoming requests. All of the above behaviours are modelled through user-defined modules, representing agents' taste variations (heterogeneity), their previous experiences (learning) and available information (system control). MaaSSim is an open-source library available at a public repository this http URL, along with a set of tutorials and reproducible use-case scenarios.
Prediction-Based Reachability for Collision Avoidance in Autonomous Driving

Safety is an important topic in autonomous driving since any collision may cause serious injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance. However, due to the worst-case assumption on the car’s future behaviours, reachability might result in too much conservatism such that the normal operation of the vehicle is badly hindered. In this paper, we leverage the power of trajectory prediction and propose a prediction-based reachability framework to compute safety controllers. Instead of always assuming the worst case, we cluster the car’s behaviors into multiple driving modes, e.g. left turn or right turn. Under each mode, a reachability-based safety controller is designed based on a less conservative action set. For online implementation, we first utilize the trajectory prediction and our proposed mode classifier to predict the possible modes, and then deploy the corresponding safety controller. Through simulations in a T-intersection and an 8-way roundabout, we demonstrate that our prediction-based reachability method largely avoids collision between two interacting cars and reduces the conservatism that the safety controller brings to the car’s original operation.
Symptom structure of ICD-11 Complex Posttraumatic Stress Disorder (CPTSD) in trauma-exposed foster children: examining the International Trauma Questionnaire – Child and Adolescent Version (ITQ-CA)

ABSTRACT Background: The 11th edition of the International Classification of Diseases (ICD-11) introduces Posttraumatic Stress Disorder (PTSD) and Complex Posttraumatic Stress Disorder (CPTSD) as two distinct trauma-related disorders. Numerous studies support the proposed symptom structure of ICD-11 CPTSD in adults, but only a few studies have examined CPTSD symptom structure in children, reporting diverging results. To assess ICD-11 CPTSD in children, the International Trauma Questionnaire (ITQ) was recently adapted for children and adolescents (ITQ-CA), with no validated German version available yet. Objective: This study aimed (1) to test the symptom structure of ICD-11 CPTSD in a sample of trauma-exposed foster children using the ITQ-CA, and (2) to examine the concurrent, convergent and discriminant validity of the German ITQ-CA. Method: Altogether, 161 Austrian foster children completed a set of standardized measures, resulting in a final sample of 135 trauma-exposed foster children meeting the inclusion criteria. Psychometric properties of the ITQ-CA were assessed using confirmatory factor analysis (CFA), bivariate correlations and multivariate regression. Results: CFA supported ICD-11 CPTSD symptom structure in children as a two-factor higher-order model with PTSD and Disturbances in Self-Organization (DSO) as correlated factors with very good model fit, while a one-factor higher-order model also fitted the data very well. High factor loadings and excellent levels of internal reliability evidenced the psychometric adequacy of the ITQ-CA. Concurrent and convergent validity were evidenced by high correlations between ITQ-CA scales and criterion variables (PTSD symptoms, depression, anxiety, dissociation, lifetime traumatization). Discriminant validity was partly supported by PTSD and DSO being differently predicted by exogenous criterion variables. Conclusions: CPTSD symptom structure in children is in support of the ICD-11 conceptualization. The reliability and validity of the German ITQ-CA are evidenced for the first time, identifying it as an easy-to-use screening instrument to assess ICD-11 PTSD and CPTSD in children. Further implications and areas for upcoming studies are discussed. HIGHLIGHTS: • CPTSD symptom structure in children supports the ICD-11 conceptualization. • The German ITQ-CA was validated as a viable screening instrument to assess ICD-11 PTSD and CPTSD in children. • PTSD and DSO were associated with lifetime traumatization, depression, anxiety and dissociation.