Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Interests from Mobility and Social Interactions

In recent years there has been an explosion in the availability of data sets about colocation between individuals and connectivity with speciﬁc network infrastructure access points, from which user location can be inferred. These traces are usually collected through mobile devices equipped with short-range radio interfaces, such as Bluetooth. Their potential is enormous as user movement data can be mapped onto the geographical space and the social interactions of individuals can be extrapolated from the colocation data. Quite interestingly, some of these data sets also contain a description of user proﬁles, such as the interests of the person, his/her age and gender and so on. In this paper we show that mobility and colocation information (i.e., social interactions) can be used to infer user interests by applying standard machine learning techniques. We evaluate a supervised and a semi-supervised technique using two different data sets containing information of interactions amongst people at conferences. We assume different degrees of available information for the inference problem and show that we are able to predict people’s interests with good accuracy also when only a small amount of information about user interests is available. While correlation of user interests with movement and proximity has already been investigated in social network research, this is the ﬁrst work that uses machine learning to show this quantitatively.
Collaborative Online Multitask Learning

We study the problem of online multitask learning for solving multiple related classification tasks in parallel, aiming at classifying every sequence of data received by each task accurately and efficiently. One practical example of online multitask learning is the micro-blog sentiment detection on a group of users, which classifies micro-blog posts generated by each user into emotional or non-emotional categories. This particular online learning task is challenging for a number of reasons. First of all, to meet the critical requirements of online applications, a highly efficient and scalable classification solution that can make immediate predictions with low learning cost is needed. This requirement leaves conventional batch learning algorithms out of consideration. Second, classical classification methods, be it batch or online, often encounter a dilemma when applied to a group of tasks, i.e., on one hand, a single classification model trained on the entire collection of data from all tasks may fail to capture characteristics of individual task; on the other hand, a model trained independently on individual tasks may suffer from insufficient training data. To overcome these challenges, in this paper, we propose a collaborative online multitask learning method, which learns a global model over the entire data of all tasks. At the same time, individual models for multiple related tasks are jointly inferred by leveraging the global model through a collaborative online learning approach. We illustrate the efficacy of the proposed technique on a synthetic dataset. We also evaluate it on three real-life problems-spam email filtering, bioinformatics data classification, and micro-blog sentiment detection. Experimental results show that our method is effective and scalable at the online classification of multiple related tasks.
A Self-Organising Model of Thermoregulatory Huddling

Endotherms such as rats and mice huddle together to keep warm. The huddle is considered to be an example of a self-organising system, because complex properties of the collective group behaviour are thought to emerge spontaneously through simple interactions between individuals. Groups of rodent pups display two such emergent properties. First, huddling undergoes a ‘phase transition’, such that pups start to aggregate rapidly as the temperature of the environment falls below a critical temperature. Second, the huddle maintains a constant ‘pup flow’, where cooler pups at the periphery continually displace warmer pups at the centre. We set out to test whether these complex group behaviours can emerge spontaneously from local interactions between individuals. We designed a model using a minimal set of assumptions about how individual pups interact, by simply turning towards heat sources, and show in computer simulations that the model reproduces the first emergent property—the phase transition. However, this minimal model tends to produce an unnatural behaviour where several smaller aggregates emerge rather than one large huddle. We found that an extension of the minimal model to include heat exchange between pups allows the group to maintain one large huddle but eradicates the phase transition, whereas inclusion of an additional homeostatic term recovers the phase transition for large huddles. As an unanticipated consequence, the extended model also naturally gave rise to the second observed emergent property—a continuous pup flow. The model therefore serves as a minimal description of huddling as a self-organising system, and as an existence proof that group-level huddling dynamics emerge spontaneously through simple interactions between individuals. We derive a specific testable prediction: Increasing the capacity of the individual to generate or conserve heat will increase the range of ambient temperatures over which adaptive thermoregulatory huddling will emerge.
ABOI: a novel strategy to mitigate the blocking due to outdated information in OCS/OBS network

The blocking probability in all-optical networks caused by out of date information from the network has been studied basically in the OCS paradigm. This paper presents a performance evaluation study that considers the problem of outdated information in hybrid optical networks (OCS/OBS). The ABOI is proposed to mitigate this problem. In our simulations, the ABOI strategy decreased the blocking probability due to outdated information from the network.
Verifying properties of well-founded linked lists

We describe a novel method for verifying programs that manipulate linked lists, based on two new predicates that characterize reachability of heap cells. These predicates allow reasoning about both acyclic and cyclic lists uniformly with equal ease. The crucial insight behind our approach is that a circular list invariably contains a distinguished head cell that provides a handle on the list. This observation suggests a programming methodology that requires the heap of the program at each step to be well-founded, i.e., for any field f in the program, every sequence u.f, u.f. f,. contains at least one head cell. We believe that our methodology captures the most common idiom of programming with linked data structures. We enforce our methodology by automatically instrumenting the program with updates to two auxiliary variables representing these predicates and adding assertions in terms of these auxiliary variables.To prove program properties and the instrumented assertions, we provide a first-order axiomatization of our two predicates. We also introduce a novel induction principle made possible by the well-foundedness of the heap. We use our induction principle to derive from two basic axioms a small set of additional first-order axioms that are useful for proving the correctness of several programs.We have implemented our method in a tool and used it to verify the correctness of a variety of nontrivial programs manipulating both acyclic and cyclic singly-linked lists and doubly-linked lists. We also demonstrate the use of indexed predicate abstraction to automatically synthesize loop invariants for these examples.
Aalborg Universitet Experimental Study of Stable Surfaces for Anti-Slug Control in Multi-phase Flow

The severe slugging flow is alw oil & gas production, especially for th based production. The slugging flow c potential problems, such as those relev safety, fatigue as well as capability phenomenon in multi-phase flow dynami avoided or eliminated by proper facility of operational conditions. Based on a tes can emulate a pipeline-riser or a gas-lift in a scaled-down manner, this paper exp the correlations of key operational para slugging flows. These correlations are re obtained stable surface in the parameter natural extension of the bifurcation p production opportunity without comprom is also studied. Relevant studies have al the capability, performance and effic control can be dramatically improve surfaces can be experimentally determine
A Reflective Photonic Crystal Fiber Temperature Sensor Probe Based on Infiltration with Liquid Mixtures

In this paper, a reflective photonic crystal fiber (PCF) sensor probe for temperature measurement has been demonstrated both theoretically and experimentally. The performance of the device depends on the intensity modulation of the optical signal by liquid mixtures infiltrated into the air holes of commercial LMA-8 PCFs. The effective mode field area and the confinement loss of the probe are both proved highly temperature-dependent based on the finite element method (FEM). The experimental results show that the reflected power exhibits a linear response with a temperature sensitivity of about 1 dB/°C. The sensor probe presents a tunable temperature sensitive range due to the concentration of the mixture components. Further research illustrates that with appropriate mixtures of liquids, the probe could be developed as a cryogenic temperature sensor. The temperature sensitivity is about 0.75 dB/°C. Such a configuration is promising for a portable, low-power and all-in-fiber device for temperature or refractive index monitoring in chemical or biosensing applications.
Memory Based Metadata Server for Cluster File Systems

In high performance computing environment, the metadata servers of distributed file system become critical to impact overall system performance. An approach of memory based metadata server is proposed, instead of the disk based approach. We present a metadata management system with matrix organization, non-overhead reliable mechanism and static scalability method, which is design to efficiently utilize large memory and provide high performance. We examine and demonstrate the performance, overhead of reliability and scalability in a test bed environment of 28 machines. The result shows that the performance of our system is higher than other traditional distributed file system, the reliability can be achieved with little overhead and the metadata servers can be linear scaling.
Africa's Development in Historical Perspective: Gender and Missionary Influence in Colonial Africa

Introduction Increasingly, evidence has emerged showing that the historical presence of European missionaries is an important factor affecting economic development across the globe today. The causal mechanism that has received the most attention, and the most empirical support, is education. The presence of Christian missionaries, particularly Protestant missionaries, has been shown to be strongly correlated with increased educational attainment, and the effects appear to persist for many generations (Bai and Kung 2011; Grier 1997; Woodberry 2004, 2009). The education-promoting influence of Protestant missionaries is not surprising given the evidence that the rise of Protestantism within Europe had long-term education (and economic growth) promoting effects (Becker and Woessmann 2009, 2008, 2010; Schaltegger and Torgler 2009). This study provides evidence for the long-term impact of colonial missionary activity within Africa. The analysis uses data on the location of Protestant and Catholic missions from a map titled “Ethnographic Survey of Africa: Showing the Tribes and Languages; also the Stations of Missionary Societies” published by William Roome (1924). This information, combined with data on the locations of ethnic groups from Murdock (1959), is used to calculate estimates of the exposure of African ethnic groups to missionary activity.
High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.
A framework for vision based bearing only 3D SLAM

This paper presents a framework for 3D vision based bearing only SLAM using a single camera, an interesting setup for many real applications due to its low cost. The focus in is on the management of the features to achieve real-time performance in extraction, matching and loop detection. For matching image features to map landmarks a modified, rotationally variant SIFT descriptor is used in combination with a Harris-Laplace detector. To reduce the complexity in the map estimation while maintaining matching performance only a few, high quality, image features are used for map landmarks. The rest of the features are used for matching. The framework has been combined with an EKF implementation for SLAM. Experiments performed in indoor environments are presented. These experiments demonstrate the validity and effectiveness of the approach. In particular they show how the robot is able to successfully match current image features to the map when revisiting an area
Combining Bayesian and Support Vector Machines Learning to automatically complete Syntactical Information for HPSG-like Formalisms

Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to the automatic acquisition of verb subcategorization frames for Modern Greek. We have made use of minimal linguistic resources, such as basic morphological tagging and phrase chunking, to demonstrate that verb subcategorization, which is of great significance for developing robust natural language human computer interaction systems, could be achieved using large corpora, without having any general-purpose syntactic parser at all. Moreover, by taking advantage of the plethora in unlabeled data found in text corpora in addition to some available labeled examples, we overcome the expensive task of annotating the whole set of training data and the performance of the subcategorization frames learner is increased. We argue that a classifier generated from BBN and SVM is well suited for learning to identify verb subcategorization frames. Empirical results will support this claim. Performance has been methodically evaluated using two different corpora, one balanced and one domain-specific in order to determine the unbiased behavior of the trained models. Limited training data are proved to endow with satisfactory results. We have been able to achieve precision exceeding 90% on the identification of subcategorization frames which were not known beforehand. The obtained valid frames have been used to fill out the subcategorization field of verb entries in an HPSG-like lexicon using the LKB grammar development environment.
Adjusting storm-influenced wind observations for barotropic hurricane track prediction.

SANBAR, a filtered barotropic prediction model designed by Sanders and Burpee (1968), relies on an analysis of the wind field averaged through the depth of the troposphere for making hurricane track predictions. The previous and present methods of interpreting storm-influenced rawinsonde observations are discussed; and a new procedure, which modifies the influenced data, is introduced for use when storms are within 300 nm of land. It is expected that this procedure will improve the SANBAR forecasts, since the large-scale flow in the storm-influenced region would no longer be constrained to be uniform. Fifty cases from nine tropical storms (1958-1975) were studied. Observations within 85 nm of the storm center were found to be overly sensitive and had to be neglected. This procedure appears capable of specifying the initial storm-track velocity about as well as present subjective practise. It should prove especially useful when erratic tracks occur close to landfall. Thesis Supervisor: Frederick Sanders Professor of Meteorology Title:
Generation of Helper Plasmids Encoding Mutant Adeno-associated Virus Type 2 Capsid Proteins with Increased Resistance against Proteasomal Degradation

Objective(s): Adeno-associated virus type 2 (AAV2) vectors are widely used for both experimental and clinical gene therapy. A recent research has shown that the performance of these vectors can be greatly improved by substitution of specific surface-exposed tyrosine residues with phenylalanines. In this study, a fast and simple method is presented to generate AAV2 vector helper plasmids encoding capsid proteins with single, double or triple Y→F mutations. Materials and Methods: A one-step, high-fidelity polymerase chain reaction (PCR) cloning procedure involving the use of two partially overlapping primers to amplify a circular DNA template was applied to produce AAV2 cap genes encoding VP1 mutants with Y→F substitutions in residues 444, 500 or 730. The resulting constructs were used to make the different double and triple mutant by another round of PCR (Y444500F mutant), subcloning (Y444730F and Y500730F mutants) or a combination of both techniques (Y444500730F mutant). Results: Nucleotide sequence analysis revealed successful introduction of the desired mutations in the AAV2 cap gene and showed the absence of any unintended mutations in the DNA fragments used to assemble the final set of AAV2 vector helper plasmids. The correctness of these plasmids was further confirmed by restriction mapping. Conclusion: PCR-based, single-step site-directed mutagenesis of circular DNA templates is a highly efficient and cost-effective method to generate AAV2 vector helper plasmids encoding mutant Cap proteins for the production of vector particles with increased gene transfer efficiency.
Exact high level WCET analysis of synchronous programs by symbolic state space exploration

In this paper a novel approach to high-level (i.e. architecture independent) worst case execution time (WCET) analysis is presented that automatically computes exact bounds for all inputs. To this end, we make use of the distinction between micro and macro steps as usually done by synchronous languages. As macro steps must not contain loops, a later low-level WCET analysis (architecture dependent) is simplified to a large extent. Checking exact execution times for all inputs is a complex task that can nevertheless be efficiently done when implicit state space representations are used. With our tools, it is not only possible to compute path information by exploring all computations, but also to verify given path information.
HYPOCHONDRIASIS AND FEAR OF DEATH

Although fear of death has been linked to hypochondriasis, the relationship of this fear to the disorder has received little study. To address this deficiency, we administered a fear of death scale along with measures of hypochondriasis, including the Whiteley Index and Somatic Symptom Inventory, to 162 general medical outpatients. Partial correlations, controlling for age, between the fear of death scale and both the Whiteley Index and Somatic Symptom Inventory were strongly positive. A factor analysis of the fear of death scale yielded three dimensions—fear of dying, loss of meaning, and fear of separation—that were also highly correlated with hypochondriasis. Fear of death and hypochondriasis showed comparable relationships to age and gender as well as to personality dimensions measured by the NEO Five-Factor Inventory. Fear of death appears to be an integral part of hypochondriasis. Its presence lends support to three models of hypochondriasis—the perceptual, existential, and interpersonal—that correspond to the dimensions of fear of death.
Long short-term memory based convolutional recurrent neural networks for large vocabulary speech recognition

Long short-term memory (LSTM) recurrent neural networks (RNNs) have been shown to give state-of-the-art performance on many speech recognition tasks, as they are able to provide the learned dynamically changing contextual window of all sequence history. On the other hand, the convolutional neural networks (CNNs) have brought significant improvements to deep feed-forward neural networks (FFNNs), as they are able to better reduce spectral variation in the input signal. In this paper, a network architecture called as convolutional recurrent neural network (CRNN) is proposed by combining the CNN and LSTM RNN. In the proposed CRNNs, each speech frame, without adjacent context frames, is organized as a number of local feature patches along the frequency axis, and then a LSTM network is performed on each feature patch along the time axis. We train and compare FFNNs, LSTM RNNs and the proposed LSTM CRNNs at various number of configurations. Experimental results show that the LSTM CRNNs can exceed state-of-the-art speech recognition performance.
Autophagy, lithium, and amyotrophic lateral sclerosis

In this article we provide an overview of the intersection between amyotrophic lateral sclerosis (ALS) and the autophagy pathway and discuss the potential protective effects of lithium through mechanisms that recruit autophagy and other effects. The autophagy pathway is recruited during motor neuron (MN) death both in vitro and in vivo. Despite a few controversial issues concerning the significance (detrimental/protective) of autophagy in ALS, recent findings indicate a protective role. Lithium in low doses is a well‐known autophagy inducer that clears misfolded proteins and altered mitochondria from MNs. Moreover, lithium preserves mitochondria and sustains their genesis. This effect is replicated by rapamycin, which is an autophagy inducer but with a different mechanism from lithium. Lithium also increases the number of Renshaw cells that are affected early during the progression of experimental ALS. Again, lithium has been reported to decrease glial proliferation in the ALS spinal cord and induces sprouting in corticospinal fibers. Muscle Nerve 40: 173–194, 2009