Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Pliocene increase in thermohaline overturning: A precondition for the development of the modern equatorial Pacific cold tongue

Unraveling the processes responsible for Earth’s climate transition from an “El Nino–like state” during the warm early Pliocene into a modern‐like “La Nina–dominated state” currently challenges the scientific community. Recently, the Pliocene climate switch has been linked to oceanic thermocline shoaling at ∼3 million years ago along with Earth’s final transition into a bipolar icehouse world. Here we present Pliocene proxy data and climate model results, which suggest an earlier timing of the Pliocene climate switch and a different chain of forcing mechanisms. We show that the increase in North Atlantic meridional overturning circulation between 4.8 and 4.0 million years ago, initiated by the progressive closure of the Central American Seaway, triggered overall shoaling of the tropical thermocline. This preconditioned the turnaround from a warm eastern equatorial Pacific to the modern equatorial cold tongue state about 1 million years earlier than previously assumed. Since ∼3.6–3.5 million years ago, the intensification of Northern Hemisphere glaciation resulted in a strengthening of the trade winds, thereby amplifying upwelling and biogenic productivity at low latitudes.
Training Deformable Part Models with Decorrelated Features

In this paper, we show how to train a deformable part model (DPM) fast-typically in less than 20 minutes, or four times faster than the current fastest method-while maintaining high average precision on the PASCAL VOC datasets. At the core of our approach is "latent LDA," a novel generalization of linear discriminant analysis for learning latent variable models. Unlike latent SVM, latent LDA uses efficient closed-form updates and does not require an expensive search for hard negative examples. Our approach also acts as a springboard for a detailed experimental study of DPM training. We isolate and quantify the impact of key training factors for the first time (e.g., How important are discriminative SVM filters? How important is joint parameter estimation? How many negative images are needed for training? ). Our findings yield useful insights for researchers working with Markov random fields and part-based models, and have practical implications for speeding up tasks such as model selection.
Optimized Broadcasting and Multicasting Protocols in Cut-Through Routed Networks

This paper addresses the one-to-all broadcasting problem and the one-to-many broadcasting problem, usually simply called broadcasting and multicasting, respectively. Broadcasting is the information dissemination problem in which a node of a network sends the same piece of information to all the other nodes. Multicasting is a partial broadcasting in the sense that only a subset of nodes forms the destination set. Both operations have many applications in parallel and distributed computing. In this paper, we study these problems in both line model, and cut-through model. The former assumes long distance calls between nonneighboring processors. The latter strengthens the line model by taking into account the use of a routing function. Long distance calls are possible in circuit-switched and wormhole-routed networks, and also in many networks supporting optical facilities. In the line model, it is well known that one can compute in polynomial time a [log/sub 2/n]-round broadcast or multicast protocol for any arbitrary network. Unfortunately such a protocol is often inefficient from a practical point of view because it does not use the resources of the network in a balanced way. In this paper, we present a new algorithm to compute broadcast or multicast protocols. This algorithm applies under both line and cut-through models. Moreover, it returns protocols that efficiently use the bandwidth of the network. From a complexity point of view, we also show that most of the optimization problems relative to the maximization of the efficiency of broadcast or multicast protocols in terms of switching time or vertex load are NP-complete. We have, however, derived polynomial efficient solutions for tree-networks.
Design of a Recommender System for Participatory Media Built on a Tetherless Communication Infrastructure

We address the challenge of providing low-cost, universal access of useful information to people in different parts of the globe. We achieve this by following two strategies. First, we focus on the delivery of information through computerized devices and prototype new methods for making that delivery possible in a secure, low-cost, and universal manner. Second, we focus on the use of participatory media, such as blogs, in the context of news related content, and develop methods to recommend useful information that will be of interest to users. To achieve the first goal, we have designed a low-cost wireless system for Internet access in rural areas, and a smartphone-based system for the opportunistic use of WiFi connectivity to reduce the cost of data transfer on multi-NIC mobile devices. Included is a methodology for secure communication using identity based cryptography. For the second goal of identifying useful information, we make use of sociological theories regarding social networks in mass-media to develop a model of how participatory media can offer users effective news-related information. We then use this model to design a recommender system for participatory media content that pushes useful information to people in a personalized fashion. Our algorithms provide an order of magnitude better performance in terms of recommendation accuracy than other state-of-the-art recommender systems. Our work provides some fundamental insights into the design of low-cost communication systems and the provision of useful messages to users in participatory media through a multi-disciplinary approach. The result is a framework that efficiently and effectively delivers information to people in remote corners of the world.
Minority Acquisitions as a Strategy to Mitigate International Acquisition Risks

International acquisitions are attractive because they allow quick entry into foreign markets. However, they are also a strategy that is fraught with high risks. Given the size and growth of investments in international acquisitions, scholars have sought to identify ways to reduce the risks in international acquisitions. The objective of this study is to examine one variation of international acquisitions--international minority acquisitions--that has the potential to reduce these risks.
Shallow vs. Deep Sum-Product Networks

We investigate the representational power of sum-product networks (computation networks analogous to neural networks, but whose individual units compute either products or weighted sums), through a theoretical analysis that compares deep (multiple hidden layers) vs. shallow (one hidden layer) architectures. We prove there exist families of functions that can be represented much more efficiently with a deep network than with a shallow one, i.e. with substantially fewer hidden units. Such results were not available until now, and contribute to motivate recent research involving learning of deep sum-product networks, and more generally motivate research in Deep Learning.
Loss of desmocollin-2 confers a tumorigenic phenotype to colonic epithelial cells through activation of Akt/β-catenin signaling

Reduced expression of the desmosomal cadherin desmocollin‐2 (Dsc2) has been reported in colorectal carcinomas, suggesting that Dsc2 may play a role in the development and/or progression of these cancers. However, no studies to date have examined the mechanistic contribution of Dsc2‐deficiency to tumorigenesis. Here, we report that loss of Dsc2 promotes cell proliferation and enables tumor growth in vivo through the activation of Akt/β‐catenin signaling. We found that down‐regulation of Dsc2 in the non‐tumorigenic colonic epithelial cell line SK‐CO15 led to enhanced β‐catenin signaling, an increase in cell proliferation, acquisition of invasive properties, and expression of CD44, a known β‐catenin target gene that is upregulated in colon cancers. Consistent with the in vitro findings that loss of Dsc2 promotes cell transformation, cells lacking Dsc2 formed tumors in immunodeficient mice, an effect that was not observed for Dsc2‐expressing parental cell lines. Importantly, inhibition of Akt prevented the increase in β‐catenin signaling and proliferation following Dsc2 knockdown and attenuated the in vivo growth of Dsc2‐deficient cells. Taken together, our results provide evidence that loss of Dsc2 may directly contribute to the growth of colon cancer cells.
Hierarchical and Overlapping Co-Clustering of mRNA: miRNA Interactions

microRNAs (miRNAs) are an important class of regulatory factors controlling gene expressions at post-transcriptional level. Studies on interactions between different miRNAs and their target genes are of utmost importance to understand the role of miRNAs in the control of biological processes. This paper contributes to these studies by proposing a method for the extraction of co-clusters of miRNAs and messenger RNAs (mRNAs). Different from several already available co-clustering algorithms, our approach efficiently extracts a set of possibly overlapping, exhaustive and hierarchically organized co-clusters. The algorithm is well-suited for the task at hand since: i) mRNAs and miRNAs can be involved in different regulatory networks that may or may not be co-active under some conditions, ii) exhaustive co-clusters guarantee that possible co-regulations are not lost, iii) hierarchical browsing of co-clusters facilitates biologists in the interpretation of results. Results on synthetic and on real human miRNA:mRNA data show the effectiveness of the approach.
Twin-Schnorr: A Security Upgrade for the Schnorr Identity-Based Identification Scheme

Most identity-based identification (IBI) schemes proposed in recent literature are built using pairing operations. This decreases efficiency due to the high operation costs of pairings. Furthermore, most of these IBI schemes are proven to be secure against impersonation under active and concurrent attacks using interactive assumptions such as the one-more RSA inversion assumption or the one-more discrete logarithm assumption, translating to weaker security guarantees due to the interactive nature of these assumptions. The Schnorr-IBI scheme was first proposed through the Kurosawa-Heng transformation from the Schnorr signature. It remains one of the fastest yet most secure IBI schemes under impersonation against passive attacks due to its pairing-free design. However, when required to be secure against impersonators under active and concurrent attacks, it deteriorates greatly in terms of efficiency due to the protocol having to be repeated multiple times. In this paper, we upgrade the Schnorr-IBI scheme to be secure against impersonation under active and concurrent attacks using only the classical discrete logarithm assumption. This translates to a higher degree of security guarantee with only some minor increments in operational costs. Furthermore, because the scheme operates without pairings, it still retains its efficiency and superiority when compared to other pairing-based IBI schemes.
The role of kinesthetic reference frames in two-handed input performance

We present experimental work which explores how the match (or mismatch) between the input space of the hands and the output space of a graphical display influences two-handed input performance. During interaction with computers, a direct correspondence between the input and output spaces is often lacking. Not only are the hands disjoint from the display space, but the reference frames of the hands may in fact be disjoint from one another if two separate input devices (e.g. two mice) are used for two-handed input. In general, we refer to the workspace and origin within which the hands operate as kinesthetic reference frames. Our goal is to better understand how an interface designer's choice of kinesthetic reference frames influences a user's ability to coordinate two-handed movements, and to explore how the answer to this question may depend on the availability of visual feedback. Understanding this issue has implications for the design of two-handed interaction techniques and input devices, as well as for the reference principle of Guiard's Kinematic Chain model of human bimanual action. Our results suggest that the Guiard reference principle is robust with respect to variances in the kinesthetic reference frames as long as appropriate visual feedback is present.
LIFE Working Paper 03 – 023 “ Empirical Evidence on Corporate Governance in Europe : The Effects on Stock Returns , Firm Value and Performance ”

In this paper we analyze whether good corporate governance leads to higher common stock returns and enhances firm value in Europe. Throughout this study we use Deminor Corporate Governance Ratings for companies included in the FTSE Eurotop 300. Following the approach of Gompers et al. (2003) we build portfolios consisting of well-governed and poorly governed companies and compare their performance. We also examine the impact of corporate governance on firm valuation. Our results show a positive relationship between these variables and corporate governance. This relationship weakens substantially after adjusting for country differences. Finally, we analyze the relationship between corporate governance and firm performance, as approximated by Net-Profit-Margin (NPM) and Return-on-Equity (ROE). Surprisingly, and contrary to Gompers et al. (2003), we find a negative relationship between governance standards and these earnings based performance ratios for which we discuss possible implications.
Frequency and prime duration effects on repetition priming and orthographic priming with words and pseudowords

The present experiment studied how repetition priming effects and orthographic priming effects vary as a function of prime/target frequency and prime duration. In a lexical decision task, word targets were preceded either by an identical word, a different word, or an orthographically related pseudoword, and pseudoword targets were preceded by an identical pseudoword, a different pseudoword, or an orthographically related word. Primes were masked for one group of subjects but not for the other. The decrease in prime duration had no effect on repetition priming for high-frequency words, considerably reduced repetition priming for low-frequency words, and eliminated repetition priming for pseudowords. Non-masked pseudoword primes had an inhibitory effect on high-frequency, orthographically similar word targets and a facilitatory effect on low-frequency, orthographically similar word targets. In contrast, only facilitatory effects were obtained with masked pseudoword primes. Finally, with pseudoword targets, a facilitatory orthographic priming effect was observed with non-masked high-frequency word primes but not with non-masked low-frequency word primes or with masked primes. Possible interpretations of these results are discussed.
Improving Near-Miss Event Detection Rate at Railway Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near- miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.
Repetitive transcranial magnetic stimulation versus botulinum toxin injection in chronic migraine prophylaxis: a pilot randomized trial

Background Chronic migraine is a prevalent disabling disease, with major health-related burden and poor quality of life. Long-term use of preventive medications carries risk of side effects. Objectives The aim of this study was to compare repetitive transcranial magnetic stimulation (rTMS) to botulinum toxin-A (BTX-A) injection as preventive therapies for chronic migraine. Methods A pilot, randomized study was conducted on a small-scale sample of 29 Egyptian patients with chronic migraine, recruited from Kasr Al-Aini teaching hospital outpatient clinic and diagnosed according to ICHD-III (beta version). Patients were randomly assigned into two groups; 15 patients received BTX-A injection following the Phase III Research Evaluating Migraine Prophylaxis Therapy injection paradigm and 14 patients were subjected to 12 rTMS sessions delivered at high frequency (10 Hz) over the left motor cortex (MC, M1). All the patients were requested to have their 1-month headache calendar, and they were subjected to a baseline 25-item (beta version) Henry Ford Hospital Headache Disability Inventory (HDI), Headache Impact Test (HIT-6), and visual analogue scale assessment of headache intensity. The primary efficacy measures were headache frequency and severity; secondary measures were 25-item HDI, HIT-6, and number of acute medications. Follow-up visits were scheduled at weeks 4, 6, 8, 10, and 12 after baseline visit. Results A reduction in all outcome measures was achieved in both the groups. However, this improvement was more sustained in the BTX-A group, and both the therapies were well tolerated. Conclusion BTX-A injection and rTMS have favorable efficacy and safety profiles in chronic migraineurs. rTMS is of comparable efficacy to BTX-A injection in chronic migraine therapy, but with less sustained effect.
A New Adaptive Sampling Method for Scalable Learning

Scaling up data mining algorithms to handle huge data sets is an important issue in machine learning and knowledge discovery. Random sampling is often used to achieve better scalability in learning from massive amount of data. Adaptive samplingoffers advantages over traditional batch sampling methods in that adaptive sampling often uses much lower number of samples and thus better efficiency while assuring guaranteed level of estimation accuracy and confidence. In this paper, we present a new adaptive sampling method for estimating the mean of a Bernoulli variable, along with preliminary theoretical studies of th e method. We present empirical simulation results indicatin g that our method often use significantly lower sample size (i.e., the number of sampled instances) while maintaining competitive accuracy and confidence when compared with batch sampling method. We also briefly outline how to make use of this new sampling method to build a scalable ensemble learning algorithm by Boosting.
Progress in the development of a Reynolds-stress turbulence closure

The paper develops proposals for a model of turbulence in which the Reynolds stresses are determined from the solution of transport equations for these variables and for the turbulence energy dissipation rate ε. Particular attention is given to the approximation of the pressure-strain correlations; the forms adopted appear to give reasonably satisfactory partitioning of the stresses both near walls and in free shear flows. Numerical solutions of the model equations are presented for a selection of strained homogeneous shear flows and for two-dimensional inhomogeneous shear flows including the jet, the wake, the mixing layer and plane channel flow. In addition, it is shown that the closure does predict a very strong influence of secondary strain terms for flow over curved surfaces.
Exogenous carbon monoxide inhibits neutrophil infiltration in LPS-induced sepsis by interfering with FPR1 via p38 MAPK but not GRK2

Excessive neutrophil infiltration in vital organs is life-threatening to patients who suffer from sepsis. We identified a critical role of exogenous carbon monoxide (CO) in the inhibition of neutrophil infiltration during lipopolysaccharide (LPS)-induced sepsis. CO delivered from carbon monoxide-releasing molecule 2 (CORM-2) dramatically increased the survival rate of C57BL/6 mice subjected to LPS in vivo. CORM-2 significantly suppressed neutrophil infiltration in liver and lung as well as markers of inflammatory responses. Affymetrix GeneChip array analysis revealed that the increased expression of chemoattractant receptor formyl peptide receptor 1 (FPR1) may contribute to the excessive neutrophil infiltration. The under agarose migration assay demonstrated that LPS stimulation promoted migration to the ligand of FPR1, N-Formyl-Met-Leu-Phe (fMLP) but that CORM-2 treatment inhibited this promotion. Further studies demonstrated that CORM-2 internalized FPR1 by inhibiting p38 mitogen-activated protein kinase (MAPK) but not G protein-coupled receptor kinase 2 (GRK2), which may explain the inhibitory effect of CORM-2 on LPS-stimulated neutrophils. In summary, our study demonstrates that exogenous CO inhibits sepsis-induced neutrophil infiltration by interfering with FPR1 via p38 MAPK but not GRK2.
Crowd IQ: measuring the intelligence of crowdsourcing platforms

We measure crowdsourcing performance based on a standard IQ questionnaire, and examine Amazon's Mechanical Turk (AMT) performance under different conditions. These include variations of the payment amount offered, the way incorrect responses affect workers' reputations, threshold reputation scores of participating AMT workers, and the number of workers per task. We show that crowds composed of workers of high reputation achieve higher performance than low reputation crowds, and the effect of the amount of payment is non-monotone---both paying too much and too little affects performance. Furthermore, higher performance is achieved when the task is designed such that incorrect responses can decrease workers' reputation scores. Using majority vote to aggregate multiple responses to the same task can significantly improve performance, which can be further boosted by dynamically allocating workers to tasks in order to break ties.