Miracle-dz/UniFAR-FTUs huggingface dataset:
Ancient bones in honor of the goddess Sinlaku: New findings on the island of Kosrae, Eastern Micronesia

Abstract: Human remains were found on the island of Kosrae in a region formerly known as a place of worship for the ancient goddess Sinlaku. These burials were found to be quite unusual in comparison to previously documented burial patterns on the island. They provided the opportunity to study the changing ritual practices on the island around 300 BP (AD 1650). Historic ethnographic accounts by the German explorer, Sarfert, from 1908–1910 provide context for religious beliefs that are otherwise extremely difficult to assess within an archaeological context. The oral history of the island tells of the arrival of a foreign group to the island that may align with the onset of these new burial patterns. Abstract: Se hallaron restos humanos en la isla de Kosrae, en una región antiguamente conocida como un lugar de adoración de la diosa Sinlaku. En comparación con patrones funerarios documentados con anterioridad, estos enterratorios eran inusuales. Otorgaron la oportunidad de estudiar las cambiantes prácticas rituales de la isla alrededor del 300 AC (1650 DC). Los informes histórico-etnográficos del explorador Alemán Sarfert, en los años 1908–1910, sitúan el contexto de las creencias religiosas, que de otra manera serían extremadamente dificil de evaluar dentro de un contexto arqueológico. La historia oral de la isla habla sobre el arribo de un grupo extranjero que podría ajustarse con la aparición de estos nuevos maneras de enterramiento.
Synthesizing hardware from sketches

This paper proposes to adapt sketching, a software synthesis technique, to hardware development. In sketching, the designer develops an incomplete hardware description, providing the "insight" into the design. The synthesizer completes the design to match an executable specification. This style of synthesis liberates the designer from tedious and error-prone details-such as timing delays, wiring in combinational circuits and initialization of lookup tables-while allowing him to control low-level aspects of the design. The main benefit will be a reduction of the time-to-market without impairing system performance.
Addressing the Dark Side of Vision Research: Storage

Data access is swiftly becoming a bottleneck in visual data processing, providing an opportunity to influence the way visual data is treated in the storage system. To foster this discussion, we identify two key areas where storage research can strongly influence visual processing run-times: efficient metadata storage and new storage formats for visual data. We propose a storage architecture designed for efficient visual data access that exploits next generation hardware and give preliminary results showing how it enables efficient vision analytics.
Learning Stochastic Models for Basketball Substitutions from Play-by-Play Data

Using play-by-play data from all 2014-15 regular season NBA games, we build a generative model that accounts for substitutions of one lineup by another together with the plus/minus rate of each lineup. The substitution model consists of a continuous-time Markov chain with transition rates inferred from data. We compare different linear and nonlinear regression techniques for constructing the lineup plus/minus rate model. We use our model to simulate the NBA playoffs; the test error rate computed in this way is 20%, meaning that we correctly predict the winners of 12 of the 15 playoff series. Finally, we outline several ways in which the model can be improved.
An overview of privacy preserving data mining

datasets in order to generate their sanitized counterparts that can be safely disclosed to untrusted third parties. The goal of this category of approaches is to enable data miners to obtain accurate results when they are not provided with the real data. As a part of this category, we highlight Secure Multiparty Com pu ta tion methodologies that have been proposed to enable a number of data holders to collectively mine their data without having to reveal their datasets to each other. On the other hand, the second category deals with techniques that prohibit the disclosure of sensitive knowledge patterns derived through the application of data mining algorithms, as well as techniques for downgrading the effectiveness of the classifiers in classification tasks, such that they do not reveal sensitive knowledge. In what follows, we further investigate each of these two categories of approaches.
Exploring Automatic Word Sense Disambiguation with Decision Lists and the Web

The most effective paradigm for word sense disambiguation, supervised learning, seems to be stuck because of the knowledge acquisition bottleneck. In this paper we take an in-depth study of the performance of decision lists on two publicly available corpora and an additional corpus automatically acquired from the Web, using the fine-grained highly polysemous senses in WordNet. Decision lists are shown a versatile state-of-the-art technique. The experiments reveal, among other facts, that SemCor can be an acceptable (0.7 precision for polysemous words) starting point for an all-words system. The results on the DSO corpus show that for some highly polysemous words 0.7 precision seems to be the current state-of-the-art limit. On the other hand, independently constructed hand-tagged corpora are not mutually useful, and a corpus automatically acquired from the Web is shown to fail.
A Context-Sensitive Homograph Disambiguation in Thai Text-to-Speech Synthesis

Homograph ambiguity is an original issue in Text-to-Speech (TTS). To disambiguate homograph, several efficient approaches have been proposed such as part-of-speech (POS) n-gram, Bayesian classifier, decision tree, and Bayesian-hybrid approaches. These methods need words or/and POS tags surrounding the question homographs in disambiguation. Some languages such as Thai, Chinese, and Japanese have no word-boundary delimiter. Therefore before solving homograph ambiguity, we need to identify word boundaries. In this paper, we propose a unique framework that solves both word segmentation and homograph ambiguity problems altogether. Our model employs both local and long-distance contexts, which are automatically extracted by a machine learning technique called Winnow.
Exploiting multiple views to support visual exploration and mining

It is no exaggeration that the quantity and complexity of data still present a formidable challenge to the effective and efficient acquisition of knowledge. On the other hand, results obtained from the application of mining techniques are often in a form that is difficult to be understood by humans who are accustomed to perceiving information through the visual channel. Multiple views present a special opportunity that can be leveraged in order to address such challenges and visually support the entire exploration and mining process. This paper acknowledges that special visual opportunity by describing how our visual data mining system, VidaMine, exploits multiple views in supporting the user in the exploration and mining process.
The 21 August 2017 Ischia (Italy) Earthquake Source Model Inferred From Seismological, GPS, and DInSAR Measurements

The causative source of the first damaging earthquake instrumentally recorded in the Island of Ischia, occurred on 21 August 2017, has been studied through a multiparametric geophysical approach. In order to investigate the source geometry and kinematics we exploit seismological, Global Positioning System, and Sentinel‐1 and COSMO‐SkyMed differential interferometric synthetic aperture radar coseismic measurements. Our results indicate that the retrieved solutions from the geodetic data modeling and the seismological data are plausible; in particular, the best fit solution consists of an E‐W striking, south dipping normal fault, with its center located at a depth of 800 m. Moreover, the retrieved causative fault is consistent with the rheological stratification of the crust in this zone. This study allows us to improve the knowledge of the volcano‐tectonic processes occurring on the Island, which is crucial for a better assessment of the seismic risk in the area.
A distributed movement control algorithm in wireless ad hoc networks

In wireless ad hoc networks, in order to prolong network life time and improve network capacity, it is necessary to reduce large transmission power of nodes. Due to the random distribution of nodes, traditional topology control algorithms fail to solve this problem completely. So we propose a distributed movement control algorithm to solve this problem. Target locations and redundant nodes are identified first based on localized minimum spanning trees(LMST) and localized uncut-vertex nodes (LUV) respectively. After moving nodes candidates are found out from redundant nodes, they finally move to target locations directly. Simulation results show that the LMST-LUV movement control algorithm can reduce the maximum communication radius and improve the performance of topology control dramatically.
EXPERIMENTAL COMPARISON OF DISCRIMINATIVE LEARNING APPROACHES FOR CHINESE WORD SEGMENTATION

Natural language processing tasks assume that the input is tokenized into individual words. In languages like Chinese, however, such tokens are not available in the written form. This thesis explores the use of machine learning to segment Chinese sentences into word tokens. We conduct a detailed experimental comparison between various methods for word segmentation. We have built two Chinese word segmentation systems and evaluated them on standard data sets. The state of the art in this area involves the use of character-level features where the best segmentation is found using conditional random fields (CRF). The first system we implemented uses a majority voting approach among different CRF models and dictionarybased matching, and it outperforms the individual methods. The second system uses novel global features for word segmentation. Feature weights are trained using the averaged perceptron algorithm. By adding global features, performance is significantly improved compared to character-level CRF models.
Architecture of a Highly Scalable Data Warehouse Appliance Integrated to Mainframe Database Systems

Main memory processing and data compression are valuable techniques to address the new challenges of data warehousing regarding scalability, large data volumes, near realtime response times, and the tight connection to OLTP. The IBM Smart Analytics Optimizer (ISAOPT) is a data warehouse appliance that implements a main memory database system for OLAP workloads using a cluster-based architecture. It is tightly integrated with IBM DB2 for z/OS (DB2) to speed up complex queries issued against DB2. In this paper, we focus on autonomic cluster management, high availability, and incremental update mechanisms for data maintenance in ISAOPT.
Joint inversion of the first overtone and fundamental mode for deep imaging at the Valhall oil field using ambient noise

Surface waves derived from ambient noise data are composed of fundamental and higher modes. The first overtone is sensitive to structure from the surface down to a greater depth than the fundamental mode. We use 6.5 hr of continuous recording of noise on 2320 ocean bottom cable sensors from the Valhall Life of Field Seismic and we compute the intersensor cross-correlation functions for the vertical and radial components. We observe that the vertical component is dominated by the fundamental mode whereas on the radial component, the first overtone is stronger than the fundamental mode. Forward modelling demonstrates that a few hundred metres of low velocity sediments along with the water layer plays an important role for the generation of stronger first overtone signal on radial component. When we invert only the fundamental mode phase velocity data, the S-wave velocity model has vertical resolution down to 600 m depth. Combining the fundamental mode and the first overtone enables to image deeper structure down to 1 km depth, highlighting the presence of a low velocity zone.
Fast distributed random walks

Performing random walks in networks is a fundamental primitive that has found applications in many areas of computer science, including distributed computing. In this paper, we focus on the problem of performing random walks efficiently in a distributed network. Given bandwidth constraints, the goal is to minimize the number of rounds required to obtain a random walk sample. All previous algorithms that compute a random walk sample of length ℓ as a subroutine always do so naively, i.e., in <i>O</i>(ℓ) rounds. The main contribution of this paper is a fast distributed algorithm for performing random walks. We show that a random walk sample of length ℓ can be computed in <i>Õ</i>(ℓ<sup>2/3</sup> <i>D</i><sup>1/3</sup>) rounds on an undirected unweighted network, where <i>D</i> is the diameter of the network. <sup>1</sup> When ℓ = Ω(<i>D</i> log <i>n</i>), this is an improvement over the naive <i>O</i>(ℓ) bound. (We show that Ω(min{<i>D</i>, ℓ}) is a lower bound and hence in general we cannot have a running time faster than the diameter of the graph. ) We also show that our algorithm can be applied to speedup the more general Metropolis-Hastings sampling. We extend our algorithms to perform a large number, <i>k</i>, of random walks efficiently. We show how <i>k</i> destinations can be sampled in <i>Õ</i>((<i>k</i>ℓ)<sup>2/3</sup> <i>D</i><sup>1/3</sup>) rounds if <i>k</i> ≤ ℓ<sup>2</sup> and <i>Õ</i>((<i>k</i>ℓ)<sup>1/2</sup>) rounds otherwise. We also present faster algorithms for performing random walks of length larger than (or equal to) the mixing time of the underlying graph. Our techniques can be useful in speeding up distributed algorithms for a variety of applications that use random walks as a subroutine.
MS&E 336 Lecture 11: The multiplicative weights algorithm

This lecture is based on the corresponding paper of Freund and Schapire [2], though with some differences in notation and analysis. We introduce and study the multiplicative weights (MW) algorithm, which is an external regret minimizing (i.e., Hannan consistent) algorithm for playing a game. The same algorithm has been analyzed in various forms, particularly in the study of online learning; see the references in [2]. Indeed, as we will observe in the subsequent lecture, the multiplicative weights algorithm is in fact a special case of stochastic fictitious play. Our focus in this lecture is on establishing Hannan consistency of the algorithm. Throughout the lecture we use the same notation as in Lecture 10, but restrict attention to twoplayer games. (Note that by grouping all players other than player 1 into “player 2”, any game can be viewed as a two player game for the purposes of establishing Hannan consistency of the multiplicative weights algorithm.)
A latency-based object placement approach in content distribution networks

Content distribution networks (CDNs) are increasingly being used to disseminate data in today's Internet. The growing interest in CDNs is motivated by a common problem across disciplines: how does one reduce the load on the origin server and the traffic on the Internet, and ultimately improve response time to users? In this direction, crucial data management issues should be addressed. A very important issue is the optimal placement of the outsourced content to CDN's servers. Taking into account that this problem is NP complete, a heuristic method should be developed. All the approaches developed so far assume the existence of adequate popularity statistics. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. This paper develops a network-adaptive, non-parameterized technique to place the outsourced content to CDN's servers, which requires no a-priori knowledge of request statistics. We place the outsourced objects to these servers with respect to the network latency that each object produces. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed technique can yield up to 25% reduction in user-perceived latency, compared with other heuristic schemes which have knowledge of the content popularity.
A Novel Voltage Control Strategy for Three Phase Grid Connected System with Distributed Generation Inverters

-In this paper Ancillary service for Distributed Generation (DG) systems becomes a challenging issue to smartly integrate renewableenergy sources into the grid. Voltage control is one of these ancillary services which can ride through and support the voltage under grid faults. Grid codes from the transmission system operators describe the behaviour of the energy source, regulating voltage limits and reactive power injection to remain connected and supports the grid under fault. On the basis that different kinds of voltage sags require different voltage support strategies, a flexible control scheme for three phase grid-connected inverter is proposed here. For the three phase balanced voltage sags, the inverter should inject reactive power in order to raise the voltage in all phases. In one-phase or two-phase faults, the main concern of the DG inverter is to equalize voltages by reducing the negative symmetric sequence and clear the phase jump. Due to system limitations, a balance between these two extreme policies is mandatory. Thus, over voltage and under voltage can be avoided, and the proposed control scheme prevents disconnection while achieving the desired voltage support service. The main a contribution of this work is the introduction of a control algorithm for reference current generation that provides flexible voltage support under grid faults.
Fluvastatin for prevention of cardiac events following successful first percutaneous coronary intervention: a randomized controlled trial.

CONTEXT Percutaneous coronary intervention (PCI) is associated with excellent short-term improvements in ischemic symptoms, yet only three fifths of PCI patients at 5 years and one third of patients at 10 years remain free of major adverse cardiac events (MACE). OBJECTIVE To determine whether treatment with fluvastatin reduces MACE in patients who have undergone PCI. DESIGN AND SETTING Randomized, double-blind, placebo-controlled trial conducted at 77 referral centers in Europe, Canada, and Brazil. PATIENTS A total of 1677 patients (aged 18-80 years) recruited between April 1996 and October 1998 with stable or unstable angina or silent ischemia following successful completion of their first PCI who had baseline total cholesterol levels between 135 and 270 mg/dL (3.5-7.0 mmol/L), with fasting triglyceride levels of less than 400 mg/dL (4.5 mmol/L). INTERVENTIONS Patients were randomly assigned to receive treatment with fluvastatin, 80 mg/d (n = 844), or matching placebo (n = 833) at hospital discharge for 3 to 4 years. MAIN OUTCOME MEASURE Survival time free of MACE, defined as cardiac death, nonfatal myocardial infarction, or reintervention procedure, compared between the treatment and placebo groups. RESULTS Median time between PCI and first dose of study medication was 2.0 days, and median follow-up was 3.9 years. MACE-free survival time was significantly longer in the fluvastatin group (P =.01). One hundred eighty-one (21.4%) of 844 patients in the fluvastatin group and 222 (26.7%) of 833 patients in the placebo group had at least 1 MACE (relative risk [RR], 0.78; 95% confidence interval [CI], 0.64-0.95; P =.01). This result was independent of baseline total cholesterol levels (above [RR, 0.76; 95% CI, 0.56-1.04] vs below [RR, 0.77; 95% CI, 0.57-1.02] the median). In subgroup analysis, the risk of MACE was reduced in patients with diabetes (n = 202; RR, 0.53; 95% CI, 0.29-0.97; P =.04) and in those with multivessel disease (n = 614; RR, 0.66; 95% CI, 0.48-0.91; P =.01) who received fluvastatin compared with those who received placebo. There were no instances of creatine phosphokinase elevations 10 or more times the upper limit of normal or rhabdomyolysis in the fluvastatin group. CONCLUSION Fluvastatin treatment in patients with average cholesterol levels undergoing their first successful PCI significantly reduces the risk of major adverse cardiac events.
Up-Regulated Expression of AOS-LOXa and Increased Eicosanoid Synthesis in Response to Coral Wounding

In octocorals, a catalase–like allene oxide synthase (AOS) and an 8R-lipoxygenase (LOX) gene are fused together encoding for a single AOS-LOX fusion protein. Although the AOS-LOX pathway is central to the arachidonate metabolism in corals, its biological function in coral homeostasis is unclear. Using an acute incision wound model in the soft coral Capnella imbricata, we here test whether LOX pathway, similar to its role in plants, can contribute to the coral damage response and regeneration. Analysis of metabolites formed from exogenous arachidonate before and after fixed time intervals following wounding indicated a significant increase in AOS-LOX activity in response to mechanical injury. Two AOS-LOX isoforms, AOS-LOXa and AOS-LOXb, were cloned and expressed in bacterial expression system as active fusion proteins. Transcription levels of corresponding genes were measured in normal and stressed coral by qPCR. After wounding, AOS-LOXa was markedly up-regulated in both, the tissue adjacent to the incision and distal parts of a coral colony (with the maximum reached at 1 h and 6 h post wounding, respectively), while AOS-LOXb was stable. According to mRNA expression analysis, combined with detection of eicosanoid product formation for the first time, the AOS-LOX was identified as an early stress response gene which is induced by mechanical injury in coral.
A Fast Polar Code List Decoder Architecture Based on Sphere Decoding

Polar codes are a recently discovered family of capacity-achieving error-correcting codes. Among the proposed decoding algorithms, successive-cancellation list decoding guarantees the best error-correction performance with codes of moderate lengths, but it yields low throughput. Speed-up techniques have been proposed in the past: most of them rely on approximations that degrade the error-correction capability of the algorithm. We propose a speed-up technique for successive-cancellation list decoding of polar codes that is exact for list size of 2, while its approximations bring negligible error-correction performance degradation (<;0.05 dB) for other list sizes. A decoder architecture is designed: the proposed technique increases the throughput of a factor of 3.16×, at the cost of 14.2% in area occupation.
Economic Impacts of Climate Change on Agriculture: Empirical Evidence from the ARDL Approach for Turkey

Purpose- The agricultural sector is one of the sectors most sensitive to climate change. This sector is directly affected by temperature and precipitation, which is an input in agricultural production. The main objective of this study is to evaluate the effects of climate change in agricultural production in Turkey. Methodology- The data cover the period 1961-2013. In this study, economic effects of climate change on agriculture were analyzed for Turkey using a time series approach. Findings- The increase in precipitation affects agricultural GDP positively, while the increase in temperature has a negative effect on agricultural GDP. Conclusion- In order to minimize the adverse effects of climate change in Turkey, which is one of the largest countries in the world in terms of agricultural land, it is important to establish policies, strategies, plans and programs to combat climate change.