Miracle-dz/UniFAR-FTUs huggingface dataset:
Ignorance Is Bliss: Emotion, Politics, and Why Whites Avoid Information About Race

This study investigated partisan differences, as well as guilt, anger, and shame as motivating emotions, to understand and predict information avoidance on topics related to race and policing. We predicted liberals would be more likely than conservatives to seek information on topics related to race; guilt, anger, and shame would be motivating emotions to seek information. We expected liberals to use guilt, anger, and shame as motivating emotions to seek information, and expected conservatives to experience lower levels of these emotions, and therefore avoid such information. Participants (N=420) were given information about police violence against people of color. Following this, participants completed the Positive and Negative Affect Schedule and finished by responding to an Information Avoidance questionnaire. Results showed that political orientation, anger, and shame had significant effects in predicting information avoidance (IA), while guilt had no significant effect. Anger and shame had a significant effect in motivating liberals to seek information, while guilt had a significant effect in motivating conservatives to avoid. These findings suggest that information about victims of racialized violence do not enact the same motivation response in conservatives as they do in liberals. Therefore, it is important to understand these implications when reaching consensus on the divergent viewpoints of racial injustices.
Litter quality and temperature modulate microbial diversity effects on decomposition in model experiments

The consequences of decline in biodiversity for ecosystem functioning is a major concern in soil ecology. Recent research efforts have been mostly focused on terrestrial plants, while, despite their importance in ecosystems, little is known about soil microbial communities. This work aims at investigating the effects of fungal and bacterial species richness on the dynamics of leaf litter decomposition. Synthetic microbial communities with species richness ranging from 1 to 64 were assembled in laboratory microcosms and used in three factorial experiments of decomposition. Thereafter, the functionality of the different microcosms was determined by measuring their capability to decompose materials with different chemical properties, including two species of litter (Quercus ilex L. and Hedera helix L.), cellulose strips and woody sticks. Incubation was done in microcosms at two temperatures (12°C and 24°C) for 120 days. The number of microbial species inoculated in the microcosms positively affected decomposition rates of Q. ilex and H. helix litters, while relationships found for cellulose and wood were not statistically significant. Diversity effect was greater at higher incubation temperature. We found lower variability of decay rates in microcosms with higher inoculated species richness of microbial communities. Our study pointed out that the relationships between inoculum microbial diversity and litter decomposition is dependent on temperature and litter quality. Therefore, the loss of microbial species may adversely affects ecosystem functionality under specific environmental conditions.
Energy Evaluation of a 5- and a 7-Stage Processor Pipeline Across Nominal and Near-Threshold Supply Voltages Master’s Thesis in Embedded Electronic System Design

Pipelined architectures have been used in commercial processors for the last three decades, achieving significant speedups over non-pipelined processors. Besides architectural advancements, CMOS technology scaling has improved the performance of hardware by offering decreasing transistor switching delays in every new technology generation. Technology scaling, however, is inextricably linked to supply voltage (VDD) scaling. The downside of VDD scaling is that the performance of a particular technology generation degrades with lower VDDs. On the other hand, reduction of the VDD decreases the switching power dissipation, which, due to its quadratic dependence on the VDD, reduces faster than the performance degrades. Since there is a plethora of design dimensions to this problem, processor pipelines considering both performance, power, and, consequently, energy metrics are very complex to design. In this thesis a fiveand a seven-stage pipeline are investigated, with respect to metrics such as timing, power, and energy. Evaluations are made in the nominal VDD domain, in which the five-stage pipeline is considered as a baseline to which the seven-stage one is compared with different branch target buffer (BTB) implementations. Assessments in the near-threshold VDD domain are carried out as well, in which the seven-stage design is synthesized using nominal-VDD gate libraries then mapped to libraries recharacterized for near-threshold VDDs. Five different EEMBC benchmarks are used to compare the efficiency of configurations over algorithms with different requirements. In the nominal, 1.1-V VDD domain at a 65-nm process the most energy-efficient sevenstage pipeline design dissipates 20 μW while running at 650 MHz at the worst-case process corner. At the lowest available VDD of 0.4 V in the near-threshold domain, the same design consumes only 0.035 μW at 13.7 MHz at the typical process corner. The most timing critical paths are also reported, and found to be located toward the instruction and data caches’ input ports. In spite of the decreasing power dissipation, energy consumption is expected to decrease only down to a certain VDD and then start increasing again due to the increase in execution times and leakage energy. Considering the trends throughout the thesis’ results and in other works, it is concluded that the optimal energy point for the seven-stage design is around the 350-mV VDD mark. Acknowledgements Besides my supervisor, Per Larsson-Edefors, who provided all the information and guidance I needed, I would like to thank Alen Bardizbanyan for his support of the tools and valuable input on technical issues, Daniel Moreau for his help with generating power and energy numbers, and Sven Knutsson for his feedback on the report. Arpad Jokai, Gothenburg, Sweden, June 16, 2015
Machine learning-based framework for 3D surface reconstruction using low-cost 3D sensors

In this work, we propose the use of the Neural Gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other Email addresses: sorts@dtic.ua. es (Sergio Orts-Escolano), jgarcia@dtic.ua. es (Jose Garcia-Rodriguez), jserra@dtic.ua. es (Jose Antonio Serra-Perez), jimeno@dtic.ua. es (Antonio Jimeno-Morenilla), agarcia@dtic.ua. es (Alberto Garcia-Garcia), vmorell@dccia.ua. es (Vicente Morell), miguel@dccia.ua. es (Miguel Cazorla) Preprint submitted to Applied Soft Computing January 12, 2015
Birth Weight and Cardiovascular Risk Comprehensive Physiology formulated

Low birth weight serves as a crude proxy for impaired growth during fetal life and indicates a failure for the fetus to achieve its full growth potential. Low birth weight can occur in response to numerous etiologies that include complications during pregnancy, poor prenatal care, parental smoking, maternal alcohol consumption, or stress. Numerous epidemiological and experimental studies demonstrate that birth weight is inversely associated with blood pressure and coronary heart disease. Sex and age impact the developmental programming of hypertension. In addition, impaired growth during fetal life also programs enhanced vulnerability to a secondary insult. Macrosomia, which occurs in response to maternal obesity, diabetes, and excessive weight gain during gestation, is also associated with increased cardiovascular risk. Yet, the exact mechanisms that permanently change the structure, physiology, and endocrine health of an individual across their lifespan following altered growth during fetal life are not entirely clear. Transmission of increased risk from one generation to the next in the absence of an additional prenatal insult indicates an important role for epigenetic processes. Experimental studies also indicate that the sympathetic nervous system, the renin angiotensin system, increased production of oxidative stress, and increased endothelin play an important role in the developmental programming of blood pressure in later life. Thus, this review will highlight how adverse influences during fetal life and early development program an increased risk for cardiovascular disease including high blood pressure and provide an overview of the underlying mechanisms that contribute to the fetal origins of cardiovascular pathology. © 2015 American Physiological Society. Compr Physiol 5:9971025, 2015.
Measures Bern Post-Session Report for Patients – Short Form 2000

The role of therapeutic processes in predicting premature termination of psychotherapy has been a particular focus of recent research. The purpose of this study was to contrast outpatients who completed therapy and those who dropped out with respect to their self-reported in-session experiences of self-esteem, mastery, clarification and the therapeutic alliance. The 296 patients with mixed disorders were treated with an integrative form of cognitive–behavioural therapy without pre-determined time limit (M=20.2 sessions). Multilevel analyses indicated that patients who did not completetreatment reported, on average, lower levels of self-esteem, mastery and clarification and lower ratings of their therapeutic alliance in treatment in contrast to patients who completed therapy. Patient-reported change in self-esteem experiences over the course of treatment turned out to be the strongest predictor of dropout from psychotherapy or successful completion. When dropout occurred before the average treatment length was reached, patients reported fewer clarifying experiences as early as the first session and their ratings of the therapeutic alliance were characterized by an absence of positive development. Both of these aspects seem to be involved in patients’ decisions to leave treatment early. The findings underscore the importance of the therapeutic process in understanding the mechanisms behind treatment dropout. Copyright © 2014 John Wiley & Sons, Ltd.
Party change, social media and the rise of ‘citizen-initiated’ campaigning

This article argues that digital media are introducing a new grassroots-based mode of ‘citizen-initiated campaigning’ (CIC) that challenges the dominant professionalized model of campaign management by devolving power over core tasks to the grassroots. After defining the practice through reference to the 2008 campaign of Barack Obama and online parties literature, we devise a measure of CIC that is applied to UK parties in the 2010 election. Our findings show that CIC is emerging outside the U.S. and adoption is associated with major party status, although it may be of particular appeal to political actors facing a resource deficit. The conclusions focus on the implications of CIC for new forms of party membership, indirect voter mobilization and the contextual factors influencing this new model of campaigning.
Benefits and costs of aphid phenological bet-hedging strategies

Intended readership I am looking for a host institute to research bet-hedging strategies in the seasonal reproductive mode switches of aphids. The intended methods leave room for collaborative side-projects beyond the study question (e.g. molecular control of photoperiodism, or sharing aphid lines from throughout Europe), so this article might be of interest to anyone working with aphids. In addition, I would be happy to receive feedback from experts in bet-hedging theory, phenotypic plasticity and photoperiodism. by elevated ﬁ tness in harsh years, so this well-known short-term ﬁ tness e ﬀ ect is not considered a real cost of bet-hedging. In contrast to the e ﬀ ects on arithmetic mean ﬁ tness, I hypothesize that bet-hedging also carries a long-term (geometric mean) ﬁ tness costs, in that the ability to generate phenotypic variance is costly per se . With this research idea I seek evidence for bet-hedging and assess its costs and bene ﬁ ts, using aphids and their polyphenism in reproductive modes as model system. I plan to use aphid clones from environments along a gradient of temperature variability, and induce switches in reproductive modes under controlled conditions. To test for bet-hedging, I will correlate variance in phenotype determination with variability of the original environment. To determine the costs of bet-hedging, I will compare population growth of bet-hedgers with non-hedging clones. I will then combine bene ﬁ ts and costs of bet-hedging by calculating the geometric (long-term) mean ﬁ tness in predictable and unpredictable environments.
Exploring Intrinsic Gender Identity Using Second Life

Virtual worlds (VWs) provide an environment to understand and explore notions of gender and identity, particularly given the ability for users to experiment with gender in online worlds. Our study analyses gender identity using the virtual space of Second Life (SL) to explore experiences and responses to gender in an avatar. We introduced 46 novice users to the VW of SL in order to see if real life gender influenced their choices of avatar. Participants selected the gender of their original avatar and once they were used to SL, they were then asked to change the gender of that avatar. We used mixed methods research consisting of paper based questionnaires (n=34) and focus groups (n=46) conducted in SL. Nearly all participants chose an initial avatar that reflected their real-life gender with females (n=22) reporting higher levels of identification with this initial avatar. Females were significantly more concerned with the gender-specific appearance of their initial avatar. On swapping gender, females reported higher levels of discomfort and many changed back before 7 minutes. Males (n=24) did not report significant discomfort with their changed-gender avatar and did not revert back to their original avatar as quickly. Our findings suggest that female participants in this study tended to reinforce gender binaries through such things as clothing, hairstyles and behaviors of their avatars. Male participants were less likely to experience discomfort through changing the gender of their avatar (with the males noting they still perceived an avatar with a female appearance as male).
An Individual Differences Analysis of Double-Aspect Stimulus Perception.

Ten double-aspect, human vs. nonhuman stimuli (e.g., vase faces) were presented to a heterogeneous sample of 520 individuals for identification. An individual differences multidimensional scaling analysis resulted in a fourdimensional stimulus space used to subgroup like perceiving Ss. Characterization of the responses to the double-aspect stimuli by each subgroup demonstrated the valueof the approach in studying selective information processing.
Delft University of Technology Effects of data ambiguity and cognitive biases on the interpretability of machine learning models in humanitarian decision making

The effectiveness of machine learning algorithms depends on the quality and amount of data and the operationalization and interpretation by the human analyst. In humanitarian response, data is often lacking or overburdening, thus ambiguous, and the time-scarce, volatile, insecure environments of humanitarian activities are likely to inflict cognitive biases. This paper proposes to research the effects of data ambiguity and cognitive biases on the interpretability of machine learning algorithms in humanitarian decision making.
Fully-Dynamic Space-Efficient Dictionaries and Filters with Constant Number of Memory Accesses

A fully-dynamic dictionary is a data structure for maintaining sets that supports insertions, deletions and membership queries. A filter approximates membership queries with a one-sided error. We present two designs: 1. The first space-efficient fully-dynamic dictionary that maintains both sets and random multisets and supports queries, insertions, and deletions with a constant number of memory accesses in the worst case with high probability. The comparable dictionary of Arbitman, Naor, and Segev [FOCS 2010] works only for sets. 2. By a reduction from our dictionary for random multisets, we obtain a space-efficient fully-dynamic filter that supports queries, insertions, and deletions with a constant number of memory accesses in the worst case with high probability (as long as the false positive probability is $2^{-O(w)}$, where $w$ denotes the word length). This is the first in-memory space-efficient fully-dynamic filter design that provably achieves these properties. We also present an application of the techniques used to design our dictionary to the static Retrieval Problem.
Understanding BERT performance in propaganda analysis

In this paper, we describe our system used in the shared task for fine-grained propaganda analysis at sentence level. Despite the challenging nature of the task, our pretrained BERT model (team YMJA) fine tuned on the training dataset provided by the shared task scored 0.62 F1 on the test set and ranked third among 25 teams who participated in the contest. We present a set of illustrative experiments to better understand the performance of our BERT model on this shared task. Further, we explore beyond the given dataset for false-positive cases that likely to be produced by our system. We show that despite the high performance on the given testset, our system may have the tendency of classifying opinion pieces as propaganda and cannot distinguish quotations of propaganda speech from actual usage of propaganda techniques.
Defense response by inter-active bio-protector and chitosan to Sclerotium rolfsii Wilt disease on cowpea, Brazilian Oxisol

Pathogenic microorganisms increase enzyme production and plant response, causing injuries and even plant death. Chitosan has shown potential to induce reactions in plant pathogens. An “in vitro” assay determines the action of chitosan and evaluates the optimal concentration to act against (Sclerotium rolfsii). The anti-microbial activity of chitosan and the bio-protector produced by the inter-active bacteria Beijerinckia indica and fungus Caenorhabditis elegans that contain chitosan were tested in a pot experiment against S. rolfsii on cowpea grown in a Brazilian Oxisol. Biofertilizer and chitosan in foliar application were used with and without S. rolfsii. The plants were analyzed, and the disease severity index, soluble protein and enzymatic activities were determined. The “in vitro” test showed chitosan effectiveness against S. rolfsii. The pot experiment with sterilized and non-sterilized soil confirmed the ability of the bio-protector that contains chitosan from C. elegans and chitosan application to increase enzymatic activities processed by S. rolfsii. The higher concentrations of the bio-protector and chitosan (4.0 and 6.0 mg mL-1) were directly related to the catalase and peroxidase activities controlling plant resistance and the disease severity index. The bio-protector may be a viable alternative to soluble fertilizer, and recommended for organic and sustainable agriculture. These findings are important for the establishment of sustainable agriculture and to avoid the use of pesticides. Key words: Antifungal activity, biopolymer, enzyme activities, plant resistance, postharvest analyzes, sustainable agriculture.
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web

We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences. We use 32 snapshots of a curated common crawl corpus (Wenzel et al, 2019) totaling 71 billion unique sentences. Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English. We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark. Further, we evaluate on competitive translation benchmarks such as WMT and WAT. Using only mined bitext, we set a new state of the art for a single system on the WMT’19 test set for English-German/Russian/Chinese. In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT’19 systems, which train on the WMT training data and augment it with backtranslation. We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop. All of the mined bitext will be freely available.
Impact of Narrow Lanes on Arterial Road Vehicle Crashes: A Machine Learning Approach

In this paper we adopted state-of-the-art machine learning algorithms, namely: random forest (RF) and least squares boosting, to model crash data and identify the optimum model to study the impact of narrow lanes on the safety of arterial roads. Using a ten-year crash dataset in four cities in Nebraska, two machine learning models were assessed based on the prediction error. The RF model was identified as the best model. The RF was used to compute the importance of the lane width predictors in our regression model based on two different measures. Subsequently, the RF model was used to simulate the crash rate for different lane widths. The Kruskal-Wallis test, was then conducted to determine if simulated values from the four lane width groups have equal means. The test null hypothesis of equal means for simulated values from the four lane width groups was rejected. Consequently, it was concluded that the crash rates from at least one lane width group was statistically different from the others. Finally, the results from the pairwise comparisons using the Tukey and Kramer test showed that the changes in crash rates between any two lane width conditions were statistically significant.
Solidification kinetics of a Cu-Zr alloy: ground-based and microgravity experiments

Experimental and theoretical results obtained in the MULTIPHAS-project (ESA-European Space Agency and DLR-German Aerospace Center) are critically discussed regarding solidification kinetics of congruently melting and glass forming Cu50Zr50 alloy samples. The samples are investigated during solidification using a containerless technique in the Electromagnetic Levitation Facility [1]. Applying elaborated methodologies for ground-based and microgravity experimental investigations [2], the kinetics of primary dendritic solidification is quantitatively evaluated. Electromagnetic Levitator in microgravity (parabolic flights and on board of the International Space Station) and Electrostatic Levitator on Ground are employed. The solidification kinetics is determined using a high-speed camera and applying two evaluation methods: “Frame by Frame” (FFM) and “First Frame – Last Frame” (FLM). In the theoretical interpretation of the solidification experiments, special attention is given to the behavior of the cluster structure in Cu50Zr50 samples with the increase of undercooling. Experimental results on solidification kinetics are interpreted using a theoretical model of diffusion controlled dendrite growth.