Miracle-dz/UniFAR-FTUs huggingface dataset:
Dark diversity estimation based on a single matrix of binary observations

: Ecological theory and nature conservation have traditionally relied solely on observed local diversity. However, the biodiversity of a site also includes the absent species that are present in the surrounding region and can potentially inhabit the site’s particular ecological conditions. These unobserved species constitute the “dark diversity” of the site. Dark diversity is by definition unobservable and can only be estimated - in binary fashion or as a degree of certainty about species membership. This thesis compares the effectiveness of several implementations of non-negative matrix factorization ( NMF) and basic autoencoders (a type of artificial neural network) to generate probabilistic values about a species’ membership in a specific site based on a single matrix of binary observations. We find that it is possible to generate a suitability matrix that is highly correlated with the underlying suitability values with both methods and that using autoencoders specifically for dark diversity predictions has a lot of potential for even more future improvements.
Data Processing Techniques for the TAC-KBP 2019 EDL Task

1.2. TAC EDL 2015-2017 and OntoNotes Compared to the YAGO data set, the TAC and OntoNotes data sets are much smaller. Each training set contains less than one hundred thousand sentences, but they are cleaner and contain no nested entities nor mentions of several entity types. A good reason to make use of the TAC corpus is that the five entity types in TAC EDL 2015-2017 (PER, ORG, GPE, LOC and FAC) match five of the first level entity types in AIDA, our target annotation system. OntoNotes uses another ontology and contains 18 entity types. In our previous experiments, however, we found that incorporating re-tagged OntoNotes data into the TAC EDL 2015 training data improves the system performance on the TAC EDL 2017 test set. Thus, we use these corpora as a seed for training a model on TAC ontology, and apply this model to generate synthetic annotations on sentences from YAGO.
Semantic Distance and Creativity in Linguistic Synaesthesia

In this work, we aim at quantitatively assessing the creativity of linguistic combinations in terms of semantic distance, using synaesthetic metaphors (e.g. bitter voice) as a case study. We created an evaluation dataset containing examples of synaesthesia that are actually occurring in corpora and automatically generated synaesthetic metaphors, together with a control set of non-synaesthetic adjective-noun combinations. Then, we tested on the dataset three quantitative models of linguistic creativity that have been proposed in the NLP and in the cognitive science literature, and we compared their performance in discriminating between creative and non-creative, directional and non-directional synaesthetic metaphors, and between synaesthetic metaphors and nonsynaesthetic phrases.
Design and Evaluation of Personalized Free Trials

Free trial promotions, where users are given a limited time to try the product for free, are a commonly used customer acquisition strategy in the Software as a Service (SaaS) industry. We examine how trial length affect users' responsiveness, and seek to quantify the gains from personalizing the length of the free trial promotions. Our data come from a large-scale field experiment conducted by a leading SaaS firm, where new users were randomly assigned to 7, 14, or 30 days of free trial. First, we show that the 7-day trial to all consumers is the best uniform policy, with a 5.59% increase in subscriptions. Next, we develop a three-pronged framework for personalized policy design and evaluation. Using our framework, we develop seven personalized targeting policies based on linear regression, lasso, CART, random forest, XGBoost, causal tree, and causal forest, and evaluate their performances using the Inverse Propensity Score (IPS) estimator. We find that the personalized policy based on lasso performs the best, followed by the one based on XGBoost. In contrast, policies based on causal tree and causal forest perform poorly. We then link a method's effectiveness in designing policy with its ability to personalize the treatment sufficiently without over-fitting (i.e., capture spurious heterogeneity). Next, we segment consumers based on their optimal trial length and derive some substantive insights on the drivers of user behavior in this context. Finally, we show that policies designed to maximize short-run conversions also perform well on long-run outcomes such as consumer loyalty and profitability.
Role Comparison of a Student Teacher and Cooperating Teacher in Classroom Management: On the Scene and Behind the Scenes

This case study, which focuses on a student teacher and her cooperating teacher, reveals the differences in classroom management that are mainly associated with the distinct roles in teaching practice. An inductive qualitative analysis of video-recordings of classes taught by the student teacher and cooperating teacher was performed and interviews were conducted with both teachers. The results reveal that behaviour management is more difficult for the student teacher because it is influenced by factors connected with the role of the teacher. In instructional management, the student teacher can more easily draw from thorough lesson preparation or subject-matter knowledge. The findings of this study strengthen the need for the improved education of cooperating teachers to reflect on classroom management with student teachers.
BabelFish: Fusing Address Translations for Containers

Cloud computing has begun a transformation from using virtual machines to containers. Containers are attractive because multiple of them can share a single kernel, and add minimal performance overhead. Cloud providers leverage the lean nature of containers to run hundreds of them on a few cores. Furthermore, containers enable the serverless paradigm, which leads to the creation of short-lived processes.In this work, we identify that containerized environments create page translations that are extensively replicated across containers in the TLB and in page tables. The result is high TLB pressure and redundant kernel work during page table management. To remedy this situation, this paper proposes BabelFish, a novel architecture to share page translations across containers in the TLB and in page tables. We evaluate BabelFish with simulations of an 8-core processor running a set of Docker containers in an environment with conservative container co-location. On average, under BabelFish, 53% of the translations in containerized workloads and 93% of the translations in serverless workloads are shared. As a result, BabelFish reduces the mean and tail latency of containerized data-serving workloads by 11% and 18%, respectively. It also lowers the execution time of containerized compute workloads by 11%. Finally, it reduces serverless function bring-up time by 8% and execution time by 10%–55%.
WING AREAS, WING LOADINGS AND WING SPANS OF 66 SPECIES OF AFRICAN RAPTORS

Summary Mendelsohn, J. M. , Kemp, A. C, Biggs, H. C, Biggs, R. & Brown, C.J. 1989. Wing areas, wing loadings and wing spans of 66 species of African raptors. Ostrich 60:35-42. The paper provides data on the wing areas of 855 birds of 66 species and wing spans of 918 individuals of 58 species of African raptors. Two measures of wing loading were calculated for those individuals that were weighed. Wing, secondary and ulnar lengths are used to derive an index of wing area which explains 98,8% of the variation in the mean wing areas of 46 species. A regression, derived from this relationship, can be used to estimate wing areas from the three linear measurements, all of which can be taken on museum specimens. Similarly, an index, using the sum of wing and ulnar lengths accounts for 99,5% of the variation in the mean wing spans of 36 species. The wing dimensions of males and females, and adults and juveniles are compared in several species. For those species with adequate samples of measurements of wing area, bo.
CNNs on surfaces using rotation-equivariant features

This paper is concerned with a fundamental problem in geometric deep learning that arises in the construction of convolutional neural networks on surfaces. Due to curvature, the transport of filter kernels on surfaces results in a rotational ambiguity, which prevents a uniform alignment of these kernels on the surface. We propose a network architecture for surfaces that consists of vector-valued, rotation-equivariant features. The equivariance property makes it possible to locally align features, which were computed in arbitrary coordinate systems, when aggregating features in a convolution layer. The resulting network is agnostic to the choices of coordinate systems for the tangent spaces on the surface. We implement our approach for triangle meshes. Based on circular harmonic functions, we introduce convolution filters for meshes that are rotation-equivariant at the discrete level. We evaluate the resulting networks on shape correspondence and shape classifications tasks and compare their performance to other approaches.
Evolution of Topics in the Psychology Domain

Topic modeling is a set of statistical methods for modeling collections of discrete data such as text corpora. It is used as a text-mining tool to discover the hidden semantic structures in a text body. Latent Dirichlet Allocation, a particular method for topic modeling is a generative probabilistic model that models texts as a mixture of underlying topics. In this thesis, Latent Dirichlet Allocation is used on a large corpus of texts from the domain of psychology. A model with 100 topics is generated, and the resulting topics are labeled. The occurrence of the topics is analysed over a time span of 40 years. The resulting model could be used for further psychology domain specific research.
Pronto: A Multi-Sensor State Estimator for Legged Robots in Real-World Scenarios

In this paper, we present a modular and flexible state estimation framework for legged robots operating in real-world scenarios, where environmental conditions, such as occlusions, low light, rough terrain, and dynamic obstacles can severely impair estimation performance. At the core of the proposed estimation system, called Pronto, is an Extended Kalman Filter (EKF) that fuses IMU and Leg Odometry sensing for pose and velocity estimation. We also show how Pronto can integrate pose corrections from visual and LIDAR and odometry to correct pose drift in a loosely coupled manner. This allows it to have a real-time proprioceptive estimation thread running at high frequency (250–1,000 Hz) for use in the control loop while taking advantage of occasional (and often delayed) low frequency (1–15 Hz) updates from exteroceptive sources, such as cameras and LIDARs. To demonstrate the robustness and versatility of the approach, we have tested it on a variety of legged platforms, including two humanoid robots (the Boston Dynamics Atlas and NASA Valkyrie) and two dynamic quadruped robots (IIT HyQ and ANYbotics ANYmal) for more than 2 h of total runtime and 1.37 km of distance traveled. The tests were conducted in a number of different field scenarios under the conditions described above. The algorithms presented in this paper are made available to the research community as open-source ROS packages.
Bilingually Induced Clause Parser for Tree-based Translation

Tree-based machine translation models possess the property of long distance re-ordering by incorporating the syntactic annotations of parse trees from both or either side(s) of the bitext. However, with the increasing of sentence length, the parsing accuracy usually goes down, which will further drop the performance of tree-based machine translation. To alleviate it, we choose to translate clauses other than entire sentences, while the challenge is to split the source sentences appropriately. In this paper, we propose a novel approach to induce clause parser from word-aligned parallel corpora, and test its effectiveness on tree-to-string machine translation. Experiments on multi translation tasks show that our approach outperforms previous rule-based approaches which mainly depend on punctuations and pre-deﬁned rules. More importantly, our approach works much better than rule-based method on text without punctuations.
Alternative fall and winter feeding systems for spring calving beef cows

This study evaluated the effects of early (EW) and late (LW) weaning on calf post-weaning growth performance and carcass characteristics. It also quantified the effects of EW and LW in combination with three winter feeding strategies on cow growth, reproductive performance and cost of production. EW calves were weaned in late August, while LW calves were weaned 56 d later in late October. The three cow winter feeding strategies were: (1) traditional (TD), or straw fed ad libitum and barley (Hordeum vulgare L.) silage fed every day; (2) alternate day (AD), or straw fed ad libitum and equivalent amounts of barley silage fed every second day (AD); and (3) swath graze (SG), or swath grazing whole-plant barley, cut in the soft dough stage. The study was conducted over three production cycles (1997/1998, 1998/1999 and 1999/2000). EW calves weighing 213 kg and fed a backgrounding diet (82% barley silage: 18% concentrate) for 56 d, grew 0.36 kg d-1 (EW = 0.61 ± 0.02 kg d-1; LW = 0.97 ± 0.02 kg d-1; P < 0.001) slo.
Comparison of two Dimensional DTM and PDTM for solving Time-Dependent Emden-Fowler Type Equations

Abstract: In this paper, we present the comparative study of two dimensional differential transform method and the projected differential transform method for solving Time-dependent Emden-Fowler type equations. Infact, the exact solutions can be obtained by known form of the series solutions in both the methods. Several illustrative examples are given to demonstrate the effectiveness of both the methods.
Universiteit Leiden Opleiding Informatica Static Security Analysis Methods in Android Systems: a Comparison

Many Android applications are uploaded to app stores every day. A relatively small part of these applications, or apps, is malware. Several research teams developed tools to make automatic malware detection for apps possible, to keep up with the never-ending stream of uploaded apks (Android PacKages). Every tool seemed better than the last, some even claiming accuracy scores well over 90% . However, all of these designs were tested against test sets containing only self-written apks, or synthetic malicious apks, or otherwise statistically unsound samples. Many of these tools are open source. We propose a novel framework, named Paellego, to install Android static security analysis tools, and run them eﬃciently distributed, in equal environments, against a suitable dataset. This allows us to make a fair and statistically sound comparison of the best known and recent tools, on real ‘practical’ malware: Malware apps created not by researchers, but by actual malware creators, which were distributed by app stores. From the results, we conclude that Android static security analysis tools do show great promise to classify apks, but are not quite there yet.
Spatial analysis of dynamic movements of Vélo'v, Lyon's shared bicycle program

Public transportation systems like Lyon's bicycle community sharing program can be considered as a complex system composed of interconnected stations that exchange bicycles. Such system generates digital footprints that reveals the activity in the city over time and space and make possible their analyze. In this paper, the analysis deals with the spatial understanding and visualization of bicycle trips. We first study the activity in each station separately and then identify the main characteristics of the flow between stations.
Intrinsic Evaluation of Grammatical Information within Word Embeddings

This work presents a proof-of-concept study for a framework of intrinsic evaluation of continuous embeddings as used in NLP tasks. This evaluation method compares the geometry of such embeddings with ground-truth embeddings in a linguistically-inspired, discrete feature space. Using model distillation (Hin-ton et al., 2015) as a means of extracting morphological information from models with no explicit morphological awareness (e.g. word-atomic models), we train multiple learner networks which do model morpheme composition so as to compare the amount of grammatical information different models capture. We use Korean afﬁxes as a case-study, as they encode multiple types of linguistic information (phonological, syntactic, semantic, and pragmatic), and allow us to investigate speciﬁc types of linguistic generalizations models may or may not be sensitive to.
Negation-based Sentiment Analysis in Review

1 ABSTRACTION Many of communications, in an information era, are through texts (Message, Email, Review, etc.) and most of the time, they often convey a certain emotion. While communication through word of mouth makes it easy for people to understand others’ emotions on a subject ( as these emotions are usually shown in their tones, facial expressions, etc.), communication through texts is harder in emotion identification. Therefore, we can simplify them by having the machine doing that for us. In order to do so, we have to make sure that the accuracy of identifying emotions by the machine is high. And handling negation is one of the problems that are preventing the machine to do so. Sentiment Analysis is the identification and determination of the emotion within the context of the texts. While researchers have been successful at making the machine identify the emotion based on positive/negative keywords (such as like, hate, love, detest, etcâĂę), they are researching on an ongoing problem of how negation word (i.e not, don’t, won’t, didn’t, hardly, etc.) will affect the emotion of the text. A negation word, which consisted of many phrases, can either amplify or negate the emotion that was in the text. Therefore, knowing how to handle them correctly will increase the accuracy of sentiment analysis. My work is on handling the negation to improve Sentiment Analysis in the context of Reviews. Most, if not all, reviews usually convey an emotion toward a subject. My plan is to identify the emotion and the negation word in the reviews, establish a relationship between the two words, and based on the relationship, output an indication whether the review is positive or negative.
Capsid integrity quantitative PCR to determine virus infectivity in environmental and food applications; a systematic review

Capsid integrity quantitative PCR (qPCR), a molecular detection method for infectious viruses combining azo dye pretreatment with qPCR, has been widely used in recent years; however, variations in pretreatment conditions for various virus types can limit the efficacy of specific protocols. By identifying and critically synthesizing 42 recent peer-reviewed studies employing capsid integrity qPCR for viruses in the last decade (2009 to 2019) in the fields of food safety and environmental virology, we aimed to establish recommendations for the detection of infectious viruses. Intercalating dyes are effective measures of viability in PCR assays provided the viral capsid is damaged; viruses that have been inactivated by other causes, such as loss of attachment or genomic damage, are less well detected using this approach. Although optimizing specific protocols for each virus is recommended, we identify a framework for general assay conditions. These include concentrations of ethidium monoazide, propidium monoazide or its derivates between 10 and 200 uM; incubation on ice or at room temperature (20 to 25C) for 5 to 120 min; and dye activation using LED or high light (500 to 800 Watts) exposure for periods ranging from 5 to 20 min. These simple steps can benefit the investigation of infectious virus transmission in routine (water) monitoring settings and during viral outbreaks such as the current COVID-19 pandemic or endemic diseases like dengue fever.
Psychological Distress Among People Losing Work During the COVID-19 Pandemic in Australia

Introduction: This study estimated the extent of psychological distress among people losing work during the coronavirus disease of 2019 (COVID-19) pandemic in Australia, and examined associations between distress, nature of work loss and degree of social interaction. Methods: Data were from a baseline online survey of an inception cohort recruited in the weeks following the introduction of physical distancing and movement restrictions to contain the spread of COVID-19 in Australia. These restrictions resulted in widespread unemployment and working hour reduction. Psychological distress was measured using the Kessler-6 scale. Data on nature of work loss, social interactions, demographic, job and occupational characteristics were also collected. Regression modelling was conducted to determine the relationship between work loss, social interactions and psychological distress, accounting for confounders. Results: Among the 551 study participants 31% reported severe psychological distress, 35% in those with job loss and 28% in those still employed but working less. Those who had significantly greater odds of high psychological distress were younger, female, had lost their job and had lower social interactions. The relationship between job loss and distress became non-significant when financial stress, and occupation were included in the regression model, but the protective effect of higher social interactions remained significant. Discussion: There was a high prevalence of psychological distress in people losing work during the COVID-19 pandemic. Age, gender, job loss and social interactions were strongly associated with distress. Interventions that promote social interaction may help to reduce distress during among people losing work during the COVID-19 pandemic.
Decision-Making and Multisensory Combination Under Time Stress

Using a video game platform, we examined how vision-based decision making was affected by a concurrent, potentially conflicting auditory stimulus. Electroencephalographic responses showed that by 150 milliseconds of stimulus onset, the brain had detected the conflict between visual and auditory stimuli. Systematically reducing the intertrial interval (ITI), which subjects described as stressful, undermined decision making. Subjects’ arterial pulse variance decreased along with ITI, signaling increased parasympathetic influence on the heart. When successive trials required a shift in processing mode, short ITIs significantly boosted one trial’s influence on the next, suggesting that stress reduces cognitive flexibility. Finally, our study demonstrates the heart’s and the brain’s important influence on decision making.
Deep Learning Model Reuse and Composition in Knowledge Centric Networking

Machine learning has inadvertently pioneered the transition of big data into big knowledge. Machine learning models absorb and incorporate knowledge from large scale data through training and can be regarded as a representation of the knowledge learnt. There are multitude of use cases where this acquired knowledge can be used to enhance future applications or speed up the training of new models. Yet, the efficient sharing, exploitation and reusability of this knowledge remains a challenge. In this paper we propose a framework for deep learning models that facilitates the reuse of model architectures, transfer coefficients between models for knowledge composition and updates, and apply compression and pruning techniques for efficient storage and communication. We discuss the framework and its application in the context of Knowledge Centric Networking (KCN) and demonstrate the framework potential through various experiments, i.e. when knowledge has to be updated to accommodate new (raw) data or to reduce complexity.