Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple genes encode the translation elongation factor EF-1 gamma in Saccharomyces cerevisiae

A gene encoding a yeast homologue of translation elongation factor 1 gamma (EF-1 gamma), TEF3, was isolated as a gene dosage extragenic suppressor of the cold-sensitive phenotype of the Saccharomyces cerevisiae drs2 mutant. The drs2 mutant is deficient in the assembly of 40S ribosomal subunits. We have identified a second gene, TEF4, that encodes a protein highly related to both the Tef3p protein (Tef3p), and EF-1 gamma isolated from other organisms. In contrast to TEF3, the TEF4 gene contains an intron. Gene disruptions showed that neither gene is required for mitotic growth. Haploid spores containing disruptions of both genes are viable and have no defects in ribosomal subunit composition or polyribosomes. Unlike TEF3, extra copies of TEF4 do not suppress the cold-sensitive 40S ribosomal subunit deficiency of a drs2 strain. Low-stringency genomic Southern hybridization analysis indicates there may be additional yeast genes related to TEF3 and TEF4.
Multi sensor system for pedestrian tracking and activity recognition in indoor environments

The widespread use of mobile devices and the rise of global navigation satellite systems GNSS have allowed mobile tracking applications to become very valuable in outdoor environments. However, tracking pedestrians in indoor environments is still a very challenging topic. This paper presents a system for pedestrian tracking and activity recognition in indoor environments using exclusively common off-the-shelf sensors embedded in smartphones. The proposed system combines the detection of biomechanical patterns of the human body while accomplishing basic activities along with identifiable signatures that certain indoor locations introduce on sensing data. The system was implemented and tested on Android-based mobile phones. The system detects and counts steps on a flat surface or stairs, tracks users' changes of direction and altitude, and recognises the proposed human activities with accuracy. All combined this leads to a total tracking accuracy of 91.06% in common human motion indoor displacements.
A Statistical Discriminant Model for Face Interpretation and Reconstruction

Multivariate statistical approaches have played an important role of recognising face images and characterizing their differences. In this paper, we introduce the idea of using a two-stage separating hyper-plane, here called statistical discriminant model (SDM), to interpret and reconstruct face images. Analogously to the well-known active appearance model proposed by Cootes et. al, SDM requires a previous alignment of all the images to a common template to minimise variations that are not necessarily related to differences between the faces. However, instead of using landmarks or annotations on the images, SDM is based on the idea of using PCA to reduce the dimensionality of the original images and a maximum uncertainty linear classifier (MLDA) to characterise the most discriminant changes between the groups of images. The experimental results based on frontal face images indicate that the SDM approach provides an intuitive interpretation of the differences between groups, reconstructing characteristics that are very subjective in human beings, such as beauty and happiness
Walter and Andrée De Nottbeck Foundation Scientific Reports Natural Succession and Human-induced Changes in the Soft-bottom Macrovegetation of Shallow Brackish Bays on the Southern Coast of Finland Riggert Munsterhjelm Preface Panta Rhei Natural Succession and Human-induced Changes in the Soft-botto

HELSINKI 2005 2 This thesis is based on the following papers, which are referred to by their Roman numerals: I Munsterhjelm, R. 1997: The aquatic macrophyte vegetation of flads and gloes, S coast of Finland. – Acta Bot. Fennica 157: 1–68. II Munsterhjelm, R. : What does the vegetation map tell us? – A methodological study and a visual analysis of the macroscopic water vegetation of shallow brackish water bays in SW Finland. (Submitted to Memoranda Societatis pro Fauna et Flora Fennica.) The decline of a charophyte – occurrence dynamics of Chara tomentosa L. at the southern coast of Finland. There are two ancient statements that well illustrate the problems involved in our attempts to develop a configuration of reality, in particular when approaching it from an ecological point of view. One is Chinese: " in nature and in life, everything depends on everything ". This illustrates the difficulty for the researcher confronted with the complexity of nature at the stage when he first chooses the subject of his research. The other statement is Greek. It illustrates the insight a field researcher achieves having faced the variability of nature and finally is forced to submit to " the only unalterable principle of nature " , a principle according to which everything is involved in a process of moving or floating around: " panta rhei " (Heracleitus c. 540 – c. 480 B.C.). We must believe in these expressions if we truly are the logical thinkers we consider ourselves to be. This also means that the aspects of the nature we study and the methods we use are products of our own highly selective choices, which are dependent on our educational background and individual sub-jectivity. Logically, our understanding of the environment is subject to limitations, and the real reasons for study are always questionable. Our choices are only defendable from a practical and organisational point of view. Facing these restrictions and the uncertainty considering the research theme I was given in 1978 by my professor in botany Hans Luther, I decided to use a purely descriptive, phenomenological approach. I was supposed to investigate the flora and vegetation of the flads, shallow and sheltered brackish water environments on the border between sea and land. My initial grasp on the theme was natural because these habitats were almost completely unclassified. Only a few ecological descriptors are usually used for understanding the function of ecosystems, …
A unified graph query layer for multiple databases

There is increasing demand to store and query data with an inherent graph structure. Examples of such data include those from online social networks, the semantic web and from navigational queries on spatial data such as maps. Unfortunately, traditional relational databases have fallen short where such graph structured data is concerned. This has led to the development of specialised graph databases such as Neo4j. However, traditional databases continue to have a wide usage base and have desirable properties such as the capacity to support a high volume of transactions while offering ACID semantics. In this paper we argue that it is in fact possible to unify different database paradigms together in the case of graph structured data through the use of a common query language and data loader that we have named Crackle (a wordplay on Gra(ph)QL). Crackle provides an expressive and powerful query library in Clojure (a functional LISP dialect for JVMs). It also provides a data loader that is capable of interfacing transparently with various data sources such as PostgreSQL databases and the Redis key-value store. Crackle shields programmers from the backend database by allowing them to write queries in Clojure. Additionally, its graph-focused prefetchers are capable of closing the hitherto large gap between a PostgreSQL database and a specialised graph database such as Neo4j from as much 326 (with a SQL query) to as low as 6 (when using Crackle). We also include a detailed performance analysis that identifies ways to further reduce this gap with Crackle. This brings into question the performance argument for specialised graph databases such as Neo4j by providing comparable performance on supposedly legacy data sources.
NUMERICAL MICROMAGNETICS: A REVIEW

Numerical simulation has become an important tool in the study of both static and dynamic issues in ferromagnetic materials.We present a review of some of the recen tadvances in numerical Micromagnetics. Key words: Micromagnetics, Landau-Lifshitz equation AMS subject classifications: 65M06, 65Z05, 35Q60
Example-based pose estimation in monocular images using compact fourier descriptors

Automatically estimating human poses from visual input is useful but challenging due to variations in image space and the high dimensionality of the pose space. In this paper, we assume that a human silhouette can be extracted from monocular visual input. We compare the recovery performance of Fourier descriptors with a number of coefficients between 8 and 128, and two different sampling methods. An examplebased approach is taken to recover upper body poses from the descriptors. We test the robustness of our approach by investigating how shape deformations due to changes in body dimensions, viewpoint and noise affect the recovery of the pose. The average error per joint is approximately 16-17° for equidistant sampling and slightly higher for extreme point sampling. Increasing the number of descriptors does not have any influence on the performance. Noise and small changes in viewpoint have only a very small effect on the recovery performance but we obtain higher error scores when recovering poses using silhouettes from a person with different body dimensions.
The Lifecyle of a Youtube Video: Phases, Content and Popularity

This paper proposes a new representation to explain and predict popularity evolution in social media. Recent work on social networks has led to insights about the popularity of a digital item. For example, both the content and the network matters, and gaining early popularity is critical. However, these observations did not paint a full picture of popularity evolution; some open questions include: what kind of popularity trends exist among different types of videos, and will an unpopular video become popular? To this end, we propose a novel phase representation that extends the well-known endogenous growth and exogenous shock model (Crane and Sornette 2008). We further propose efficient algorithms to simultaneously estimate and segment power-law shaped phases from historical popularity data. With the extracted phases, we found that videos go through not one, but multiple stages of popularity increase or decrease over many months. On a dataset containing the 2-year history of over 172,000 YouTube videos, we observe that phases are directly related to content type and popularity change, e.g., nearly 3/4 of the top 5% popular videos have 3 or more phases, more than 60% news videos are dominated by one long power-law decay, and 75% of videos that made a significant jump to become the most popular videos have been in increasing phases. Finally, we leverage this phase representation to predict future viewcount gain and found that using phase information reduces the average prediction error over the state-of-the-art for videos of all phase shapes.
Accurate 3D bone segmentation in challenging CT images: Bottom-up parsing and contextualized optimization

In full or arbitrary field-of-view (FOV) 3D CT imaging, obtaining an accurate per-voxel segmentation for complete large and small bones remains an unsolved and challenging problem. The difficulty lies in the notable variation in appearance and position observed among cortical bones, marrow and pathologies. To approach this problem, several studies have employed active shape models and atlas models. In this paper, we argue that a bottom-up approach, defined by classifying and grouping supervoxels, is another viable technique. Moreover, it can be integrated into a conditional random field (CRF) representation. Our approach consists of the following steps: first, an input CT volume is decomposed into supervoxels, in order to ensure very high bone boundary recall. Supervoxels are generated via a robust process of conservative region partitioning and recursive region merging. In order to maximize sparsity and classification efficiency, we use a Bayesian sparse linear classifier to compute and optimize middle-level image features. Next, we disambiguate the CRF unary potentials via contextualized optimization by pooling over selective supervoxel pairs. Finally, we adopt a pairwise support vector machine (SVM) model to learn the CRF pairwise potential in a fully supervised manner. We evaluate our method quantitatively on 137 low-resolution, low-contrast CT volumes with severe imaging noise, among which various bone pathologies are represented. Our system proves to be efficient; it achieves a clinically significant segmentation accuracy level (Dice Coefficient 98.2%).
Journal of Graph Algorithms and Applications Fixed-location Circular Arc Drawing of Planar Graphs * †

In this paper we consider the problem of drawing a planar graph using circular arcs as edges, given a one-to-one mapping between the vertices of the graph and a set of points in the plane. If for every edge we have only two possible circular arcs, then a simple reduction to 2SAT yields an O(n 2) algorithm to find out if a drawing with no crossings can be realized, where n is the number of vertices in the graph. We present an improved O(n 7/4 polylog n) time algorithm for this problem. For the special case where the possible circular arcs for each edge are of the same length, we present an even more efficient algorithm that runs in O(n 3/2 polylog n) time. We also consider two related optimization versions of the problem. First we show that minimizing the number of crossings is NP-hard. Second we show that maximizing the number of edges that can be realized without crossings is also NP-hard. Finally, we show that if we have three or more possible circular arcs per edge, deciding whether a drawing with no crossings can be realized is NP-hard.
Brain Tumor Detection Based on Bilateral Symmetry Information

Advances in computing technology have allowed researchers across many fields of endeavor to collect and maintain vast amounts of observational statistical data such as clinical data, biological patient data, data regarding access of web sites , financial data, and the like. Brain Magnetic Resonance Imaging (MRI) segmentation is a complex problem in the field of medical imaging despite various presented methods. MR image of human brain can be divided into several sub-regions especially soft tissues such as gray matter, white matter and cerebrospinal fluid. Although edge information is the main clue in image segmentation, it can’t get a better result in analysis the content of images without combining other information. The segmentation of brain tissue in the magnetic resonance imaging (MRI) is very important for detecting the existence and outlines of tumors. In this thesis , an algorithm about segmentation based on the symmetry character of brain MRI image is presented. Our goal is to detect the position and boundary of tumors automatically. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.
Relay Selection Based Space-Time Coding for Two-Way Wireless Relay Networks Using Digital Network Coding

In this paper, we propose a novel decode-and-forward dual-relay selection technique based on Alamouti space time coding for twoway wireless relay networks (TWRNs). The two- and the threephase TWRN protocol are used in the first and second version of the proposed technique, respectively, to exchange the information symbols of the communicating terminals. In order not to waste any power for transmitting information symbols known at any terminal and hence to improve the achievable coding gain, the proposed technique uses the concept of digital network coding in which the transmitted symbols of both terminals are combined at the relays into a symbol of the same constellation. To further improve the reliability of the communication and offer additional coding gain, Alamouti space time coding is incorporated in our technique. Simulation results show a substantially improved bit error rate (BER) performance of the proposed techniques as compared to the the state-of-the art techniques.
Caretaker: A Social Game for Studying Trust Dynamics

We present a new strategy for studying trust determination, especially in the context of dynamic trust. We created a game in which the players start with neutral trust for each other, have assigned roles ("good'' or "bad'') and an incentive to deduce the roles of the other players, and are encouraged to take part in social and economic interactions with each other, thereby gathering data with which to make trust determinations. By running user tests, we show that a game with these components generates useful data with which the players' decisions regarding trust for each other can be observed and better understood. We show that players are more likely to increase their self-reported trust for each other after engaging in an economic transaction, and, surprisingly, their trust for each other increases by a larger margin when the transaction was asymmetric. We also present an analysis of the chat that the players participated in, via an in-game chat system. We show that the two most popular topics of discussion are trade requests and game administration, and suggest possible future work in comparing the players' chat actions to their trust for each other.
High-performance MAC for high-capacity wireless LANs

The next-generation wireless technologies, e.g., 802.11n and 802.15. 3a, offer a physical-layer speed at least an-order-of-magnitude higher than the current standards. However, direct application of current MACs leads to high protocol overhead and significant throughput degradation. In this paper, we propose ADCA, a high-performance MAC that works with high-capacity physical layer. ADCA exploits two ideas of adaptive batch transmission and opportunistic selection of high-rate hosts to simultaneously reduce the overhead and improve the aggregate throughput. It opportunistically favors high-rate hosts by providing higher access probability and more access time, while ensuring each low-rate host certain minimum amount of channel access time. Simulations show that the ADCA design increases the throughput by 112% and reduces the average delay by 55% compared with the legacy DCF. It delivers more than 100 Mbps MAC-layer throughput as compared with 35 Mbps offered by the legacy MAC
Endothelial inflammation: the role of differential expression of N-deacetylase/N-sulphotransferase enzymes in alteration of the immunological properties of heparan sulphate

Heparan sulphate N-deacetylase/N-sulphotransferase (NDST) enzymes catalyse the reaction that initiates sulphation and subsequent modification of the oligosaccharide, heparan sulphate (HS). The extent and distribution of sulphate substitution on HS plays a vital role in regulation of the binding of a range of proteins, including IFN-γ, several interleukins and most chemokines. In this study, the expression of NDST transcripts was found to be non-uniform between a range of cell types, suggesting that different cells produce characteristic HS species. It was found that stimulation of the HMEC-1 microvascular endothelial cell line with the pro-inflammatory cytokines IFN-γ and TNF-α caused a transient decrease in the level of NDST-1 and -2 transcripts after 4 hours (P<0.05 and P<0.01 respectively), but the expression of NDST-1 increased above control levels after 16 hours (P<0.01). The change in NDST expression was concurrent with an increase in the abundance of sulphated HS epitopes on the cell surface; this was not caused by variation in the expression of proteoglycans or by changes in the rate of GAG turnover. Cytokine-stimulated endothelial cells also showed an increase in their potential to bind RANTES (CCL5); this was abrogated by chlorate blockade of sulphotransferase activity or by heparitinase cleavage of cell surface HS. Monolayers of cytokine-stimulated HMEC-1 also supported an enhanced leukocyte chemotactic response towards RANTES. This study demonstrated that pro-inflammatory cytokines can increase NDST expression leading to increased sulphation of HS and a corresponding increase in sequestration of functional RANTES at the apical surface of endothelial cells. This may enhance leukocyte extravasation at sites of inflammation.
ITU-T EV-VBR: A robust 8-32 kbit/s scalable coder for error prone telecommunications channels

This paper presents ITU-T Embedded Variable Bit-Rate (EV-VBR) codec being standardized by Question 9 of Study Group 16 (Q9/16) as recommendation G.718. The codec provides a scalable solution for compression of 16 kHz sampled speech and audio signals at rates between 8 kbit/s and 32 kbit/s, robust to significant rates of frame erasures or packet losses. It comprises 5 layers where higher layer bitstreams can be discarded without affecting the lower layer decoding. The core layer takes advantage of signal-classification based CELP encoding. The second layer reduces the coding error from the first layer by means of additional pitch contribution and another algebraic codebook. The higher layers encode the weighted error signal from lower layers using MDCT transform coding. Several technologies are used to encode the MDCT coefficients for best performance both for speech and music. The codec performance is demonstrated with selected results from ITU-T Characterization test.
Perverse incentives under the Clean Development Mechanism ( CDM ) : an evaluation of HFC-23 destruction projects

The Clean Development Mechanism (CDM) under the Kyoto Protocol allows industrialised countries to use credits from greenhouse gas (GHG) abatement projects in developing countries. A key requirement of the CDM is that the emission reductions be real, measurable and additional. This article uses data from registered projects to evaluate the extent to which these objectives are met by projects that reduce hydrofluorocarbon-23 (HFC-23) emissions in the production of hydro¬chloro¬fluoro¬carbon-22 (HCFC-22). The data shows that HCFC-22 plants produced significantly less HFC-23 during periods when no emission credits could be claimed compared to periods when HFC-23 destruction could be credited under the CDM. Moreover, the total amount of HCFC-22 produced appears to be determined mainly by CDM rules. This suggests that the claimed emission reductions may partially not be real and that the CDM provides perverse incentives to generate more HFC-23. The accelerated phase-out of HCFCs under the Montreal Protocol on Substances that Deplete the Ozone Layer could worsen this situation. To address these issues an ambitious emission benchmark for the baseline HFC-23 emissions is proposed.
Prospective Optimization

Human performance approaches that of an ideal observer and optimal actor in some perceptual and motor tasks. These optimal abilities depend on the capacity of the cerebral cortex to store an immense amount of information and to flexibly make rapid decisions. However, behavior only approaches these limits after a long period of learning while the cerebral cortex interacts with the basal ganglia, an ancient part of the vertebrate brain that is responsible for learning sequences of actions directed toward achieving goals. Progress has been made in understanding the algorithms used by the brain during reinforcement learning, which is an online approximation of dynamic programming. Humans also make plans that depend on past experience by simulating different scenarios, which is called prospective optimization. The same brain structures in the cortex and basal ganglia that are active online during optimal behavior are also active offline during prospective optimization. The emergence of general principles and algorithms for goal-directed behavior has consequences for the development of autonomous devices in engineering applications.