Miracle-dz/UniFAR-FTUs huggingface dataset:
MustaCHE: A Multiple Clustering Hierarchies Explorer

In this demonstration paper we introduce MustaCHE ( Multiple Clustering Hierarchies Explorer ), a tool that allows analysis and exploration of multiple clustering hierarchies in an interactive and visual manner. A known issue in the context of density-based clustering is how to set parameters. Typically one has to resort to trial-and-error, and its potential pitfalls, which may possibly include not finding existing clusters at all. In a previous work we have devised a very efficient technique to generate clustering hierarchies using HDBSCAN* w.r. t . a range of its clustering parameter, mpts . However, finding the "best" mpts value is still an open problem. In order to mitigate this issue we developed MustaCHE, a tool that allows a user to visualize several different density-based cluster hierarchies of a dataset w.r. t . a large range of mpts values. The user can then explore hierarchies individually and, at the same time, see how they compare to the other hierarchies. The simultaneous visualization of multiple clustering hierarchies provided by MustaCHE makes it feasible (and easy) for a user to gain a deeper understanding of the data and how its cluster structures behave under different parameter settings.
A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

Creating human-informative signal processing systems for the underwater acoustic environment that do not generate operator cognitive saturation and overload is a major challenge. To alleviate cognitive operator overload, we present a visual analytics methodology in which multiple beam-formed sonar returns are mapped to an optimized 2-D visual representation, which preserves the relevant data structure. This representation alerts the operator as to which beams are likely to contain anomalous information by modeling a latent distribution of information for each beam. Sonar operators therefore focus their attention only on the surprising events. In addition to the principled visualization of high-dimensional uncertain data, the system quantifies anomalous information using a Fisher Information measure. Central to this process is the novel use of both signal and noise observation modeling to characterize the sensor information. A demonstration of detecting exceptionally low signal-to-noise ratio targets embedded in real-world 33-beam passive sonar data is presented.
A Survey on Approaches to Text Mining using Information Extraction

Information extraction is an important text mining problem and has been extensively studied in areas such as natural language processing, information retrieval and Web mining. Information Extraction (IE) techniques aim to extract the names of entities and objects from text and to identify the roles that they play in event descriptions. IE systems generally focus on a specific domain or topic, searching only for information that is relevant to a user’s interests. Information extraction technology focuses on only the relevant parts of the text and ignores the rest. Information Extraction is the mapping of natural language texts (such as newswire reports, newspaper and journal articles, electronic mail, World Wide Web pages, any textual database, etc.) into predefined, structured representation, or templates, which, when filled, represent an extract of key information from the original text. The information concerns entities of interest in the application domain (e.g. companies or persons), or relations between such entities, usually in the form of events in which the entities take part (e.g. company takeovers, management successions etc.). Once extracted, the information can then be stored in databases to be queried, data mined, summarized in natural language, etc. In this paper various steps involved in information extraction is discussed. In this paper we describe Named entity recognition (NER) which is one of the most common uses of information extraction technology. Named entity recognition aims at finding names of entities such as people, organizations and locations while Relation extraction is the task of finding the semantic relations between entities from text. KeywordsAutomatic Content Extraction, corpus , Named entity recognition ,semantic, tokenizer.
opulation structure of albacore ( Thunnus alalunga ) in the Northwestern acific Ocean inferred from mitochondrial DNA eorgiana

Albacore (Thunnus alalunga) is a highly migratory cosmopolitan fish commonly distributed throughout all oceans. It is an important commercial species in the world fisheries. In the present study, population structure of albacore in the Northwestern Pacific Ocean was investigated using mitochondrial DNA sequence data analysis. A total of 175 individuals were sampled from three regions in the Northwest-
2D Scene Sketch-Based 3D Scene Retrieval

Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval schemes. Currently, there is a lot of research in sketch-based 3D model retrieval, which usually targets the problem of retrieving a list of candidate 3D models using a single sketch as input. 2D scene sketch-based 3D scene retrieval is a brand new research topic in the ﬁeld of 3D object retrieval. Unlike traditional sketch-based 3D model retrieval which ideally assumes that a query sketch contains only a single object, this is a new 3D model retrieval topic within the context of a 2D scene sketch which contains several objects that may overlap with each other and thus be occluded and also have relative location conﬁgurations. It is challenging due to the semantic gap existing between the iconic 2D representation of sketches and more accurate 3D representation of 3D models. But it also has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR Entertainment. Therefore, this research topic deserves our further exploration. To promote this interesting research, we organize this SHREC track and build the ﬁrst 2D scene sketch-based 3D scene retrieval benchmark by collecting 3D scenes from Google 3D Warehouse and utilizing our previously proposed 2D scene sketch dataset Scene250. The objective of this track is to evaluate the performance of different 2D scene sketch-based 3D scene retrieval algorithms using a 2D sketch query dataset and a 3D Warehouse model dataset. The benchmark contains 250 scene sketches and 1000 3D scene models, and both are equally classiﬁed into 10 classes. In this track, six groups from ﬁve countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only 3 groups have successfully submitted 8 runs. The retrieval performance of submitted results has been evaluated using 7 commonly used retrieval performance metrics. We also conduct a thorough analysis and discussion on those methods, and suggest several future research directions to tackle this research problem. We wish this publicly available [YLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and advance the research of 2D scene sketch-based 3D scene retrieval and its applications.
Fiscal policy and spatial inequality in Latin America and beyond Working paper

We study the theoretical and empirical links between fiscal policy and spatial inequality, with a non-exclusive focus on Latin American countries. We outline the two main dimensions of fiscal policy vis-à-vis economic inequality, and show how these can be used to analyze specific policy measures. We examine why fiscal policies so often fail to have the ameliorative effects that theory predicts on spatial inequality, and explore ways to make policy tools more effective. We explore the relation between fiscal policies and spatial inequalities in three case studies: Mexico, Brazil and Argentina. Lastly, we examine the effects of decentralization on spatial inequality in Bolivia. 1 Development Studies Institute and STICERD, London School of Economics, Houghton Street, London WC2A 2AE, UK. 2 Development Studies Institute, London School of Economics, Houghton Street, London WC2A 2AE, UK.
FUSION OF THERMAL INFRARED HYPERSPECTRAL AND VIS RGB DATA USING GUIDED FILTER AND SUPERVISED FUSION GRAPH

Nowadays, advanced technology in remote sensing allows us to get multi-sensor and multi-resolution data from the same region. Fusion of these data sources for classification remains challenging problems. We proposed a novel framework for fusion of low spatial-resolution Thermal Infrared (TI) hyperspectral (HS) and high spatial-resolution RGB data. First, we do super-resolution on TI HS data by using RGB image and guided filter in principal component analysis (PCA) domain. Then, we couple feature extraction and data fusion of spectral features (from TI HS data) and spatial features (morphological features generated on RGB data) through supervised fusion graph. Finally, the fused features are used by SVM classifier to generate the final classification map. Experimental results on the classification of fusing real TI HS and RGB images demonstrate the effectiveness of the proposed method both visually and quantitatively.
Extraction and Determination of Antioxidants, Polyphenols, Flavonoids and Antioxidant Activity in some Plants

The methanolic extracts of the leaves and stem of some plants (Calotropis procera, Juniperus procera, Lantana camara and Olea europaea) were screened for polyphenols, flavoides and their free radical scavenging properties using ascorbic acid as standard antioxidant. Free radical scavenging activity was evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical. Generally leaves of Calotropis procera and Lantana Camara, which are the highest records of polyphenol, 943.90 and 917.60 mg/100 mL of polyphenols, respectively while leaves of Calotropis procera and Juniperus procera are highest in flavonoids, which contain 55.60 and 53.43 mg/100 g, respectively. The leaves of Juniperus procera and Olea europaea showed the highest antioxidant activity, 12.66 and 11 μg/mL, respectively, where as methanolic extract from the stem of Lantana Camara and Calotropis procera showed weak antioxidant activity, with values of 6.0 and 4.33 μg/mL. The study reveals that the use of the leaves of these plants would exert several beneficial effects by virtue of their antioxidant activity.
ENTROPY-DISSIPATIVE DISCRETIZATION OF NONLINEAR DIFFUSION EQUATIONS AND DISCRETE BECKNER INEQUALITIES ∗

The time decay of fully discrete finite-volume approximations of porous-medium and fast-diffusion equations with Neumann or periodic boundary conditions is proved in the entropy sense. The algebraic or exponential decay rates are computed explicitly. In particular, the numerical scheme dissipates all zeroth-order entropies which are dissipated by the continuous equation. The proofs are based on novel continuous and discrete generalized Beckner inequalities. Furthermore, the exponential decay of some first-order entropies is proved in the continuous and discrete case using systematic integration by parts. Numerical experiments in one and two space dimensions illustrate the theoretical results and indicate that some restrictions on the parameters seem to be only technical.
ON DECOUPLED QUANTIFICATION OF ENERGY DISSIPATION MECHANISMS IN TOROIDAL RING GYROSCOPES

We present a study on the quantification of the energy dissipation mechanisms in Toroidal Ring Gyroscopes (TRG). Our approach is based on suppression of the viscous air damping and controlling thermoelastic dissipation (TED), thus exposing the anchor loss of the device. During testing, energy dissipative factors, such as viscous air damping, TED, and surface loss, were minimized by venting the encapsulation, pumping down to ultrahigh vacuum (UHV), and sweeping the temperature down to 123K, so that anchor loss became the dominant factor that influenced the overall quality factor of the device. The anchor loss limit of the tested TRG was measured to be 1.35 million, and this value was on the same level as predicted by PML method. This study demonstrates the use of a methodology for analysis of energy dissipation mechanisms in MEMS devices and the possibility of quantifying the contribution of each mechanism independently.
From idea to acoustics and back again: the creation and analysis of information in music

The information in musical signals – including recordings, written music, mechanical or electronic storage files and the signal in the auditory nerve – are compared as we trace the information chain that links the minds of composer, performer and listener. The (uncompressed) information content of music increases during stages such as theme, development, orchestration and performance. The analysis of performed music by the ear and brain of a listener may reverse the process: several stages of processing simplify or analyse the content in steps that resemble, in reverse, those used to produce the music. Musical signals have a low algorithmic entropy, and are thus readily compressed. For instance, pitch implies periodicity, which implies redundancy. Physiological analyses of these signals use these and other structures to produce relatively compact codings. At another level, the algorithms whereby themes are developed, harmonised and orchestrated by composers resemble, in reverse, the means whereby complete scores may be coded more compactly and thus understood and remembered. Features used to convey information in music (transients, spectra, pitch and timing) are also used to convey information in speech, which is unsurprising, given the shared hard- and soft-ware used in production and analysis. The coding, however, is different, which may give insight into the way music is understood and appreciated.
Comparison Between Two Generic 3d Building Reconstruction Approaches - Point Cloud Based VS. Image Processing Based

Abstract. This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.
Complete Valorization of Olive Mill Wastewater through an Integrated Process for Poly-3-hydroxybutyrate Production

An economical and environmental sustainability of bioplastic production is dependent on the use of low cost and waste C-sources as raw materials. OMW (Olive Mill Wastewater) with its high organic load represents a dangerous polluting waste. Herein the authors present an integrated process for the simultaneous recovery of polyphenols, high value natural antioxidants, production of PHAs (polyhydroxyalkanotes), thermoplastic bio-polymers, in particular of PHB (poly-3-hydroxybutyrate) starting from OMW. The combination of membrane filtration and bacterial digestion of OMW resulted in very high yields of polyphenols (32.5 g/L) and PHB (31.4 mg/L·h) if compared with the state of the art. These results make the technical approach described here effective for reducing the polluting effect of OMW and maximizing the valuable product yield. Moreover the process is readily suitable for an industrial scale PHB production from OMW.
Switched-Impedance Control of Surgical Robots in Teleoperated Beating-Heart Surgery

A novel switched-impedance control method is proposed and implemented for telerobotic beating-heart surgery. Differing from cardiopulmonary-bypass-based arrested-heart surgery, beating-heart surgery creates challenges for the human operator (surgeon) due to the heart’s fast motions and, in the case of a teleoperated surgical robot, the oscillatory haptic feedback to the operator. This paper designs two switched reference impedance models for the master and slave robots to achieve both motion compensation and nonoscillatory force feedback during slave–heart interaction. By changing the parameters of the impedance models, different performances for both robots are obtained: (a) when the slave robot does not make contact with the beating heart, the slave robot closely follows the motion of the master robot as in a regular teleoperation system, (b) when contact occurs, the slave robot automatically compensates for the fast motions of the beating heart while the human operator perceives the nonoscillatory component of the slave–heart interaction forces, creating the feeling of making contact with an idle heart for the human operator. The proposed method is validated through simulations and experiments.
New architectures for presenting search results based on Web search engines users experience

Introduction. The Internet is a dynamic environment which is continuously being updated. Search engines have been, currently are and in all probability will continue to be the most popular systems in this information cosmos. Method. In this work, special attention has been paid to the series of changes made to search engines up to this point, which are currently in common usage; we also consider the objectives set for an immediate future in which, undoubtedly, searches will be increasingly attuned to user needs. Analysis and Results. Since they originated, these information recovery systems have seen developments in their search algorithms and interfaces for presentation of results whilst their users' information seeking behaviour has changed substantially, making it possible to distinguish up to three generations (the latest of these is still being developed). Changes in users' habits and routines when interacting with these systems show a road map which is increasingly concerned with improving the searching experience. Conclusions. Therefore, we are dealing not only with technological development, but also with an evolving process with regard to the use of information, in turn leading to a new Internet search paradigm.
Chemical defenses in the sea hare Aplysia parvula: importance of diet and sequestration of algal secondary metabolites

Marine algae produce a variety of secondary metabolites that function as herbivore deterrents. Algal metabolites, however, often fail to deter damage by some herbivores such as meso- grazers that both live and feed on their host alga. In addition, the degree to which intraspecific chem- ical variation in an alga affects a mesograzer's feeding behavior and its ability to deter predators is poorly understood. The red alga Portieria hornemannii contains the secondary metabolites apa- kaochtodene A and B, which have been shown to vary in concentration among sites on Guam and act as significant deterrents to fish feeding. On Guam, the sea hare Aplysia parvula preferred and grew best when fed its algal host P. hornemannii. However, high concentrations of P. hornemannii crude extract and the pure compounds apakaochtodene A and B acted as feeding deterrents to A. parvula. Despite differences among sites in the levels of apakaochtodenes A and B, A. parvula showed no sig- nificant preference for P. hornemannii from any one location. Aplysia parvula found on P. horneman- nii sequestered apakaochtodenes, and both whole animals and body parts were unpalatable to reef fishes. Sea hares found on the red alga Acanthophora spicifera, which contains no unpalatable sec- ondary metabolites, had no apakaochtodene compounds and were eaten by fishes. This observation is consistent with the hypothesis that diet-derived algal metabolites in sea hares play a role in deter- ring predation.
Renegotiation of Concession Contracts: Empirical Evidence for Public Transport Infrastructure in Peru

This paper identifies the determinants of renegotiation of concession contracts in transport infrastructure for public use in Peru. Based on the literature of concession contracts, this paper uses a probit model to estimate the probability of renegotiating these contracts. The results indicated that the probability of renegotiate these concession contracts depends on the number of bidders who participated in the bidding process for each concession, the competition factor (decision variable) that determines the winner of the bid; the bidder's previous experience on other concession contracts; the percentage of land given to the operator at the beginning of the concession; the regulatory mechanism; the degree of autonomy of the regulator (in terms of experience and financial resources), and the exchange rate.
ON THE ‘SPIRIT OF LFG’ IN CURRENT COMPUTATIONAL LINGUISTICS

In this position paper, I take a look at some of the key “design pri ciples” of LFG and draw some parallels to developments in resea ch on Natural Language Processing (NLP) and computational linguist ics over the past few years. A number of recent trends and findings in NLP resear ch seem to have precedents in earlier LFG work in ways that have not rece iv d much attention so far.
Ten Years of WebTables

In 2008, we wrote about WebTables, an effort to exploit the large and diverse set of structured databases casually published online in the form of HTML tables. The past decade has seen a flurry of research and commercial activities around the WebTables project itself, as well as the broad topic of informal online structured data. In this paper, we 1 will review the WebTables project, and try to place it in the broader context of the decade of work that followed. We will also show how the progress over the past ten years sets up an exciting agenda for the future, and will draw upon many corners of the data management community.
Bundled Object Context for Referring Expressions

Referring expressions are natural language descriptions of objects within a given scene. Context is of crucial importance for a referring expression, as the description not only depicts the properties of the object but also involves the relationships of the referred object with other ones. Most of previous work uses either the whole image or one particular contextual object as the context. However, the context of these approaches is holistic and insufficient, as a referring expression often describes relationships of multiple objects in an image. To leverage rich context information from all objects in an image, in this paper, we propose a novel scheme that is composed of a visual context long short-term memory (LSTM) module and a sentence LSTM module to model bundled object context for referring expressions. All contextual objects are arranged with their spatial locations and progressively fed into the visual context LSTM module to acquire and aggregate the context features. Then the concatenation of the learned context features and the features of the referred object are put into the sentence LSTM module to learn the probability of a referring expression. The feedback connections and internal gating mechanism of the LSTM cells enable our model to selectively propagate relevant contextual information through the whole network. Experiments on three benchmark datasets show that our methods can achieve promising results compared to state-of-the-art methods. Moreover, visualization of the internal states of the visual context LSTM cells also shows that our method can automatically select the pertinent context objects.