Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing EVALD – Software Applications for Automatic Evaluation of Discourse in Czech

In the paper, we introduce two software applications for automatic evaluation of coherence in Czech texts called EVALD – Evaluator of Discourse. The first one – EVALD 1.0 – evaluates texts written by native speakers of Czech on a five-step scale commonly used at Czech schools (grade 1 is the best, grade 5 is the worst). The second application is EVALD 1.0 for Foreigners assessing texts by non-native speakers of Czech using six-step scale (A1–C2) according to CEFR. Both appli-cations are available online at https://lindat.mff. cuni.cz/services/evald-foreign/.
Bilingual lexicon extraction from comparable corpora for closely related languages

In this paper we present a knowledge-light approach to extract a bilingual lexicon for closely related languages from comparable corpora. While in most related work an existing dictionary is used to translate context vectors, we take advantage of the similarities between languages instead and build a seed lexicon from words that are identical in both languages and then further extend it with context-based cognates and translations of the most frequent words. We also use cognates for reranking translation candidates obtained via context similarity and extract translation equivalents for all content words, not just nouns as in most related work. The results are very encouraging, suggesting that other similar languages could benefit from the same approach. By enlarging the seed lexicon with cognates and translations of the most frequent words and by cognate-based reranking of translation candidates we were able to improve the average baseline precision from 0.592 to 0.797 on the mean reciprocal rank for the ten top-ranking translation candidates for nouns, verbs and adjectives with a 46% recall on the gold standard of 1000 random entries from a traditional dictionary.
Thermodynamics of organic chemical hydration: QSPR models using physicochemical HYBOT descriptors

Stable and predictive quantitative structure–property relationship (QSPR) models of thermodynamics of chemical hydration (changes in Gibbs energy, ΔG air/water, enthalpy, ΔH air/water and entropy ΔS air/water) were obtained on the basis of physicochemical descriptors calculated by the HYBOT program. The structurally diverse training set (n = 151) and test set (n = 37) included 13 mono-functional chemical classes. The applied HYBOT descriptors comprise molecular polarizability α (as a volume-related term), the sum of partial negative charges on all atoms in a molecule ∑Q – (as an electrostatic term) and the sum of H-bond acceptor and donor factors ∑C a and ∑C d (as H-bond terms). Final equations for changes in Gibbs energy and enthalpy provided good statistical criteria and standard deviations on the level of errors of experimental determinations. All four above-mentioned terms essentially contribute to hydration enthalpy and each of them increases negative values of enthalpy. Hydration Gibbs energy predominantly depends on hydrogen bonding between solute and water molecules. Steric and electrostatic terms act in opposite directions and partly compensate each other. Changes in entropy correlate with increasing H-bond acceptor ability, whereas the other three descriptors exhibit inverse correlations.
DAG-based Long Short-Term Memory for Neural Word Segmentation

Neural word segmentation has attracted more and more research interests for its ability to alleviate the effort of feature engineering and utilize the external resource by the pre-trained character or word embeddings. In this paper, we propose a new neural model to incorporate the word-level information for Chinese word segmentation. Unlike the previous word-based models, our model still adopts the framework of character-based sequence labeling, which has advantages on both effectiveness and efficiency at the inference stage. To utilize the word-level information, we also propose a new long short-term memory (LSTM) architecture over directed acyclic graph (DAG). Experimental results demonstrate that our model leads to better performances than the baseline models.
Cell Migration and Aggregation in the Developing Telencephalon: Pulse-Labeling Chick Embryos with Bromodeoxyuridine

Previous studies had concluded that the avian telencephalon develops according to an outside-in schedule of neurogenesis, with relatively little migration of young neuroblasts past older cells. These previous studies had, however, been based on the “cumulative labeling” method, which is less accurate than the “pulse-labeling” method typically used in mammals. In the present study, we pulse-labeled chick embryos by injecting low doses of the thymidine analog bromodeoxyuridine (BrdU) directly into the circulatory system of chick embryos at 6 d of incubation. The brains of these embryos were then examined for anti-BrdU-labeled cells at postinjection survival times from 30 min to 10 d. Comparisons across different survival times, as well as with cases in which BrdU was injected on day 7, suggested that our effective pulse duration is <24 hr. This was confirmed by injecting tritiated thymidine 24 hr after the BrdU and seeing no double-labeled cells. Several deviations from the previously reported pattern of telencephalic neurogenesis were also noted. Most importantly, the cells born on day 6 in the avian Wulst, the likely homolog of mammalian neocortex, end up homogeneously distributed throughout the Wulst, which suggests that many of them are migrating past older cells. Furthermore, the cells born on day 6 in the ventral hyperstriatum and dorsal neostriatum gradually (over the course of 2–3 d) aggregate into distinct multicellular clusters, which suggests that isochronic cells in these regions adhere preferentially to one another. Finally, the data reveal a proliferative subventricular zone similar to that observed in the ganglionic eminences of mammalian embryos.
Creating personalized jigsaw puzzles

Designing aesthetically pleasing and challenging jigsaw puzzles is considered an art that requires considerable skill and expertise. We propose a tool that allows novice users to create customized jigsaw puzzles based on the image content and a user-defined curve. A popular design choice among puzzle makers, called color line cutting, is to cut the puzzle along the main contours in an image, making the puzzle both aesthetically interesting and challenging to solve. At the same time, the puzzle maker has to make sure that puzzle pieces interlock so that they do not disassemble easily. Our method automatically optimizes for puzzle cuts that follow the main contours in the image and match the user-defined curve. We handle the tradeoff between color line cutting and interlocking, and we introduce a linear formulation for the interlocking constraint. We propose a novel method for eliminating self-intersections and ensuring a minimum width in our output curves. Our method satisfies these necessary fabrication constraints in order to make valid puzzles that can be easily realized with present-day laser cutters.
A PCANet based method for Vehicle Make Recognition

This paper proposed a new Vehicle Make Recognition (VMR) method using the PCANet features extracted from vehicle front view images. The PCANet architecture processes every input vehicle image through only three very simple data processing components: cascaded principle component analysis (PCA), binary hashing, and block-wise histograms, and generates a sparse vector as the feature representation. Then, a linear SVM classifier collects the PCANet features to train a model for the classification. For the evaluation of the proposed method, we built two large training datasets: a sedan dataset and a SUV dataset respectively. The sedan training dataset consists of 4188 sedan images corresponding to 22 different vehicle makes, and the SUV training dataset contains 2165 SUV images corresponding to 21 vehicle makes. Moreover, every make includes nearly all the models with different styles and colors in recent years. Compare this method with other two state-of-the-art VMR classification methods, we conclude that our approach outperforms other approaches and achieves high accuracies of 95.48% and 95.84% on our datasets, with a recognition speed of 0.4s per image.
Technical Report: Image Captioning with Semantically Similar Images

This report presents our submission to the MS COCO Captioning Challenge 2015. The method uses Convolutional Neural Network activations as an embedding to find semantically similar images. From these images, the most typical caption is selected based on unigram frequencies. Although the method received low scores with automated evaluation metrics and in human assessed average correctness, it is competitive in the ratio of captions which pass the Turing test and which are assessed as better or equal to human captions.
Diagnosis and treatment of multiple sclerosis in pediatric and adolescent patients: current status and future therapies

Pediatric-onset multiple sclerosis (MS) comprises approximately 3%–5% of cases of MS in North America. Recent years have seen significant advances in the diagnosis and treatment of this condition, including the introduction of proposed diagnostic criteria for pediatric demyelinating disorders, and a growing body of knowledge regarding treatment options. This article reviews current approaches to the diagnosis and management of pediatric MS.
Hierarchical Phrase-Based Translation with Weighted Finite-State Transducers and Shallow-n Grammars

In this article we describe HiFST, a lattice-based decoder for hierarchical phrase-based translation and alignment. The decoder is implemented with standard Weighted Finite-State Transducer (WFST) operations as an alternative to the well-known cube pruning procedure. We find that the use of WFSTs rather than k-best lists requires less pruning in translation search, resulting in fewer search errors, better parameter optimization, and improved translation performance. The direct generation of translation lattices in the target language can improve subsequent rescoring procedures, yielding further gains when applying long-span language models and Minimum Bayes Risk decoding. We also provide insights as to how to control the size of the search space defined by hierarchical rules. We show that shallow-n grammars, low-level rule catenation, and other search constraints can help to match the power of the translation system to specific language pairs.
Analysis of Slow-Wave Instability in Rectangularly Corrugated Cylindrical Waveguide

Slow-wave instability in a rectangularly corrugated cylindrical waveguide is analyzed. Fields inside corrugation is expressed by a sum of space harmonics satisfying the boundary conditions at the corrugation wall. The corrugation fields are matched to periodic fields outside the corrugation at the boundary of the two regions. Infimtesimally thin annular electron beam is considered including the three-dimensional perturbations. Effects of space harmonics in the corrugation along with the corrugation depth are examined. Slow cyclotron instability in addition to Cherenkov instability occurs when perturbed motions and modulations of infinitely thin surface are included.
Do Self-Perceptions of Emotional Intelligence Predict Health-Related Quality of Life? A Case Study in Hospital Managers in Greece

The aim of this study was to examine HRQoL outcome and EI of managers of Health Organizations. We collected data from 120 general managers of Greek public hospitals who completed the Assessing Emotions Scale (AES) and the SF-36 Health Survey. The results showed that male managers generally exhibited better HRQoL and slightly worse EI than females, although differences were not significant throughout. The three EI factors of the AES addressing appraisal, optimism/regulation and utilization of emotions correlated from 0.18 to 0.39 with sub-dimensions of HRQoL mostly related to mental -rather than physical- aspects of health, and were also significant predictors of HRQoL. There was a noteworthy gender difference in the manner in which EI predicted HRQoL and this suggests more testing. Overall, this study might enrich the potential for EI studies in Greece as well as to contribute to the international literature.
Molybdenum cofactor: A key component of Mycobacterium tuberculosis pathogenesis?

Abstract Mycobacterium tuberculosis (Mtb) and other members of the Mtb complex possess an expanded complement of genes for the biosynthesis of molybdenum cofactor (MoCo), a tricyclic pterin molecule that is covalently attached to molybdate. This cofactor allows the redox properties of molybdenum to be harnessed by enzymes in order to catalyze redox reactions in carbon, nitrogen and sulfur metabolism. In this article, we summarize recent advances in elucidating the MoCo biosynthetic pathway in Mtb and highlight the evidence implicating the biosynthesis of this cofactor, as well as the enzymes that depend upon it for activity, in Mtb pathogenesis.
Dataflow analysis for concurrent programs using datarace detection

Dataflow analyses for concurrent programs differ from their single-threaded counterparts in that they must account for shared memory locations being overwritten by concurrent threads. Existing dataflow analysis techniques for concurrent programs typically fall at either end of a spectrum: at one end, the analysis conservatively kills facts about all data that might possibly be shared by multiple threads; at the other end, a precise thread-interleaving analysis determines which data may be shared, and thus which dataflow facts must be invalidated. The former approach can suffer from imprecision, whereas the latter does not scale. We present RADAR, a framework that automatically converts a dataflow analysis for sequential programs into one that is correct for concurrent programs. RADAR uses a race detection engine to kill the dataflow facts, generated and propagated by the sequential analysis, that become invalid due to concurrent writes. Our approach of factoring all reasoning about concurrency into a race detection engine yields two benefits. First, to obtain analyses for code using new concurrency constructs, one need only design a suitable race detection engine for the constructs. Second, it gives analysis designers an easy way to tune the scalability and precision of the overall analysis by only modifying the race detection engine. We describe the RADAR framework and its implementation using a pre-existing race detection engine. We show how RADAR was used to generate a concurrent version of a null-pointer dereference analysis, and we analyze the result of running the generated concurrent analysis on several benchmarks.
Reducing CPU and network overhead for small I/O requests in network storage protocols over raw Ethernet

Small I/O requests are important for a large number of modern workloads in the data center. Traditionally, storage systems have been able to achieve low I/O rates for small I/O operations because of hard disk drive (HDD) limitations that are capable of about 100-150 IOPS (I/O operations per second) per spindle. Therefore, the host CPU processing capacity and network link throughput have been relatively abundant for providing these low rates. With new storage device technologies, such as NAND Flash Solid State Drives (SSDs) and non-volatile memory (NVM), it is becoming common to design storage systems that are able to support millions of small IOPS. At these rates, however, both server CPU and network protocol are emerging as the main bottlenecks for achieving large rates for small I/O requests. Most storage systems in datacenters deliver I/O operations over some network protocol. Although there has been extensive work in low-latency and high-throughput networks, such as Infiniband, Ethernet has dominated the datacenter. In this work we examine how networked storage protocols over raw Ethernet can achieve low, host CPU overhead and increase network link efficiency for small I/O requests. We first analyze in detail the latency and overhead of a networked storage protocol directly over Ethernet and we point out the main inefficiencies. Then, we examine how storage protocols can take advantage of context switch elimination and adaptive batching to reduce CPU and network overhead. Our results show that raw Ethernet is appropriate for supporting fast storage systems. For 4kB requests we reduce server CPU overhead by up to 45%, we improve link utilization by up to 56%, achieving more than 88% of the theoretical link throughput. Effectively, our techniques serve 56% more I/O operations over a 10Gbits/s link than a baseline protocol that does not include our optimizations at the same CPU utilization. Overall, to the best of our knowledge, this is the first work to present a system that is able to achieve 14μs host CPU overhead on both initiator and target for small networked I/Os over raw Ethernet without hardware support. In addition, our approach is able to achieve 287K 4kB IOPS out of the 315K IOPS that are theoretically possible over a 1.2GBytes/s link.
k-hop compound metric based clustering scheme for ad hoc networks

This paper focuses on the design of a novel approach for dynamic k-hop clustering architecture, which is called k-hop compound metric based clustering (KCMBC). KCMBC is a fast convergent and load balancing clustering approach that is able to demonstrate significant improvement on the network performance, in terms of scalability and stability for large-scale ad hoc networks. On the other hand, since KCMBC has taken into account the host mobility, the clusters constructed by KCMBC are more stable than the other schemes. Simulation results show that the clusters created by using the KCMBC approach have modest but more uniform cluster size. Moreover, cluster-head duration can be increased by KCMBC significantly.
Variability of flip-flop timing at sub-threshold voltages

The design of sub-threshold circuits is especially challenging due to the massive impact of process variations. These variabilities also heavily affect circuit timing, a problem only considered concerning combinational gates so far. In this paper the effects of process variations on flip-flop timing at sub-threshold voltages are analyzed based on extensive monte-carlo simulations. The results show that the usual timing-optimal definition of timing parameters needs to be replaced by a reliability-driven approach. The model is validated for sub- and near-threshold supply voltages and an approach for energy-optimal sizing is presented.
Fast and accurate event-driven simulation of mixed-signal systems with data supplementation

This paper presents a methodology of simulating behaviors of complex mixed-signal systems such as phase-locked loops (PLLs) and high-speed I/O interfaces on an event-driven HDL simulator like SystemVerilog. Continuous-time signal events as well as voltage and timing noises are accurately modeled without relying on fine time steps, based on a technique called data supplementation. That is, rather than representing a signal just as a series of time-value pairs, additional information is annotated using the struct construct in SystemVerilog. Prototype models for a 2-GHz PLL and a 2-Gbps high-speed series I/O system including a 2.9dB-loss channel at 1GHz demonstrate 50× and 80× faster simulation speeds, respectively, with the same or better accuracy compared with the conventional time-step based models.
A Comparative Study of Model Compensation Methods for Robust Speech Recognition in Noisy Conditions

Background noise can cause severe degradation of performance for speech recognition systems. Robustness towards background noise can be achieved by applying model-based compensation approaches. For systems that use MFCC features the relationship between noise, speech, and resulting noisecorrupted speech is non-linear, and an important aspect of model-based approaches is how to approximate this relationship. To investigate how accurate such approximations need to be in order to achieve good recognition performance, we apply three different techniques. These are evaluated on a spoken digit recognition task with artificially added noise.
Data Mining Applied to Acoustic Bird Species Recognition

In this paper we explore the application of data mining techniques to the problem of acoustic recognition of bird species. Most bird song analysis tools produce a large amount of spectral and temporal attributes from the acoustic signal. The identification of distinctive features has become critical in resource constrained applications such as habitat monitoring by sensor networks. Reducing computational requirements makes it affordable to run a classifier on devices with power consumption constraints, such as nodes in a sensor network. Experimental results demonstrate that considerable dimensionality reduction can be achieved without significant loss in classification efficiency
Extracting the Minkowski Sum Boundary from the Reduced Convolution ∗

We propose a new method for computing the 2-d Minkowski sum of non-convex polygons. Our method is convolution based. The main idea is to use the reduced convolution and ﬁlter the boundary by using the topological properties of the Minkowski sum. The main beneﬁt of this proposed approach is from the fact that, in most cases, the complexity of the complete convolution is much higher than the complexity of the ﬁnal Minkowski sum boundary. Therefore, the traditional approach often wastes a large portion of the computation on computing the arrangement induced by the complete convolution that is later on thrown away. Our method is designed to speciﬁcally avoid this waste of computation. We demonstrate both theoretically and experimentally that the proposed method handles signiﬁcantly less number of line segments using only reduced convolution than the traditional approaches that use the complete convolution.
Initial Study of Electrospinning PCL/PLLA Blends

The process of electrospinning is considered one of the most promising methods for the fabrication of poly- mer nanofibers. This essentially consists of applying a high electric field, which causes stretching of the polymer which exits through a capillary. Among the numerous applications of this process, electrospinning allows the fabrication of semiconductor and conductive nanofibers from mixtures or solutions, which have great potential for applications in sensors and the fabrication of scaffolds for cell growth. The aim of this work was to analyze the properties of the blend, produces by the electrospinning of the PCL and PLLA solu- tion, with the focus to generate a promissory scaffold. PCL is a semi-crystalline aliphatic polymer that has a slower degradation rate 12 - 24 months. It has a low glass transition temperature at –60? C, a melting tem-perature at about 60? C, and a high thermal stability. Properties of PLA depend on the component isomers, processing temperature, annealing time and molecular weight. Thus were used PCL, from Aldrich, with Mw of 80,000 g/mol, and PLLA, sintered in laboratory, with Mw of 240,000 g/mol, were dissolved in chloroform (CHCl3, Merck) and acetone (Synth) by stirring for 6 hours. The solution was electrospinning for 1 hour us- ing the equipment made in the laboratory, the voltage used was 13 kv, the rate of 0.5 ml/h and an approxi- mate distance from the tip of the needle to the collector of 12 cm. The morphology of the samples was ob- served by images made with scanning electron microscopy (SEM) and also was analyzed by FT-IR and DSC.