Miracle-dz/UniFAR-FTUs huggingface dataset:
Geoparsing biodiversity heritage library collections: A preliminary exploration

A short pilot study was conducted to provide recommendations on methods and workflows for extracting geographic references from the text of Biodiversity Heritage Library collections and disambiguating these references. An initial survey of the literature was conducted, and a variety of possible techniques and software were subsequently explored for natural language processing, machine learning, document annotation, and map visualization. A test corpus was evaluated, and preliminary findings identify challenges for a full-scale effort towards automated geoparsing, including: varying OCR quality, diversity of the corpus, historical context, and ambiguity of geographic references. The project background, approaches, and preliminary assessment are described here.
Lifestyle modification in the management of the metabolic syndrome: achievements and challenges

Lifestyle modification based on behavior therapy is the most important and effective strategy to manage the metabolic syndrome. Modern lifestyle modification therapy combines specific recommendations on diet and exercise with behavioral and cognitive strategies. The intervention may be delivered face-to-face or in groups, or in groups combined with individual sessions. The main challenge of treatment is helping patients maintain healthy behavior changes in the long term. In the last few years, several strategies have been evaluated to improve the long-term effect of lifestyle modification. Promising results have been achieved by combining lifestyle modification with pharmacotherapy, using meals replacement, setting higher physical activity goals, and long-term care. The key role of cognitive processes in the success/failure of weight loss and maintenance suggests that new cognitive procedures and strategies should be included in the traditional lifestyle modification interventions, in order to help patients build a mind-set favoring long-term lifestyle changes. These new strategies raise optimistic expectations for an effective treatment of metabolic syndrome with lifestyle modifications, provided public health programs to change the environment where patients live support them.
Mining Frequent Patterns from Very High Dimensional Data : A Top-Down Row Enumeration Approach

Data sets of very high dimensionality, such as microarray data, pose great challenges on efficient processing to most existing data mining algorithms. Recently, there comes a row-enumeration method that performs a bottom-up search of row combination space to find corresponding frequent patterns. Due to a limited number of rows in microarray data, this method is more efficient than column enumerationbased algorithms. However, the bottom-up search strategy cannot take an advantage of user-specified minimum support threshold to effectively prune search space, and therefore leads to long runtime and much memory overhead. In this paper we propose a new search strategy, top-down mining, integrated with a novel rowenumeration tree, which makes full use of the pruning power of the minimum support threshold to cut down search space dramatically. Using this kind of searching strategy, we design an algorithm, TD-Close, to find a complete set of frequent closed patterns from very high dimensional data. Furthermore, an effective closeness-checking method is also developed that avoids scanning the dataset multiple times. Our performance study shows that the TD-Close algorithm outperforms substantially both Carpenter, a bottom-up searching algorithm, and FPclose, a column enumeration-based frequent closed pattern mining algorithm.
Phylogeography and genetic population structure of the spadenose shark (Scoliodon macrorhynchos) from the Chinese coast

Abstract The population structure of Scoliodon macrorhynchos from the Chinese coast was investigated using the mitochondrial control region. All 19 mtDNA haplotypes from 219 sequences were identified. Relatively high average haplotype diversity (0.797) and relatively low average nucleotide diversity (0.0013) were found together with a recent and sudden population expansion. Analysis of the mismatch distributions, neutrality tests and Bayesian skyline plot showed a pattern consistent with a recent population expansion event that may have taken place during the last glacial maximum (LGM). The analysis of molecular variance (AMOVA) showed the low genetic differentiation between the populations, which may be a general feature of sharks living in coastal areas. The phylogenetic and cluster analysis of the mtDNA indicates that two putative groups (K = 2) existed in S. macrorhynchos, showing that the Taiwan Strait acted as a biogeographic barrier during major drops in the sea level in the late Pliocene epoch.
An Efficient Distribution of Labor in a Two Stage Robust Interpretation Process

Although Minimum Distance Parsing (MDP) offers a theoretically attractive solution to the problem of extragrammaticality, it is often computationally infeasible in large scale practical applications. In this paper we present an alternative approach where the labor is distributed between a more restrictive partial parser and a repair module. Though two stage approaches have grown in popularity in recent years because of their efficiency, they have done so at the cost of requiring hand coded repair heuristics. In contrast, our two stage approach does not require any hand coded knowledge sources dedicated to repair, thus making it possible to achieve a similar run time advantage over MDP without losing the quality of domain independence.
Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems

A careful analysis of conditioning in the Sleeping Beauty problem is done, using the formal model for reasoning about knowledge and probability developed by Halpern and Tuttle. While the Sleeping Beauty problem has been viewed as revealing problems with conditioning in the presence of imperfect recall, the analysis done here reveals that the problems are not so much due to imperfect recall as to asynchrony. The implications of this analysis for van Fraassen's Reflection Principle and Savage's Sure-Thing Principle are considered.
Deciphering “Immaturity-Stemness” in Human Epidermal Stem Cells at the Levels of Protein-Coding and Non-Coding Genomes: A Prospective Computational Approach

The epidermis hosts populations of epithelial stem cells endowed with well-documented renewal and regenerative functions. This tissue thus constitutes a model for exploring the molecular characteristics of stem cells, which remain to date partially characterized at the molecular level in human skin. Our group has investigated the regulatory functions of the KLF4/TGFB1 and the MAD4/MAX/MYC signaling pathways in the control of the immaturity-stemness versus differentiation fate of keratinocyte stem and precursor cells from human interfollicular epidermis. We described that down-modulation of either KLF4 or MXD4/MAD4 using RNA interference tools promoted an augmented stemness cellular status; an effect which was associated with significant transcriptional changes, as assessed by RNA-sequencing. Here, we have implemented a computational approach aimed at integrating the level of the coding genome, comprising the transcripts encoding conventional proteins, and the non-coding genome, with a focus on long non-coding RNAs (lncRNAs). In addition, datasets of micro-RNAs (miRNAs) with validated functions were interrogated in view of identifying miRNAs that could make the link between protein-coding and non-coding transcripts. Putative regulons comprising both coding and long non-coding transcripts were built, which are expected to contain original pro-stemness candidate effectors available for functional validation approaches. In summary, interpretation of our basic functional data together with in silico biomodeling gave rise to a prospective picture of the complex constellation of transcripts regulating the keratinocyte stemness status.
A unified approach to statistical language modeling for Chinese

The paper presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigrams to Chinese is challenging because: (1) there is no standard definition of words in Chinese, (2) word boundaries are not marked by spaces, and (3) there is a dearth of training data. Our unified approach automatically and consistently gathers a high-quality training data set from the Web, creates a high-quality lexicon, and segments the training data using this lexicon, all using a maximum likelihood principle, which is consistent with the trigram training. We show that each of the methods leads to improvements over standard SLM, and that the combined method yields the best pinyin conversion result reported.
Isolevuglandins Promote Mitochondrial Dysfunction and Electrophysiologic Abnormalities in Atrial Cardiomyocytes

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, yet the cellular and molecular mechanisms underlying the AF substrate remain unclear. Isolevuglandins (IsoLGs) are highly reactive lipid dicarbonyl products that mediate oxidative stress-related injury. In murine hypertension, the lipid dicarbonyl scavenger 2-hydroxybenzylamine (2-HOBA) reduced IsoLGs and AF susceptibility. We hypothesized that IsoLGs mediate detrimental pathophysiologic effects in atrial cardiomyocytes that promote the AF substrate. Using Seahorse XFp extracellular flux analysis and a luminescence assay, IsoLG exposure suppressed intracellular ATP production in atrial HL-1 cardiomyocytes. IsoLGs caused mitochondrial dysfunction, with reduced mitochondrial membrane potential, increased mitochondrial reactive oxygen species (ROS) with protein carbonylation, and mitochondrial DNA damage. Moreover, they generated cytosolic preamyloid oligomers previously shown to cause similar detrimental effects in atrial cells. In mouse atrial and HL-1 cells, patch clamp experiments demonstrated that IsoLGs rapidly altered action potentials (AP), implying a direct effect independent of oligomer formation by reducing the maximum Phase 0 upstroke slope and shortening AP duration due to ionic current modifications. IsoLG-mediated mitochondrial and electrophysiologic abnormalities were blunted or totally prevented by 2-HOBA. These findings identify IsoLGs as novel mediators of oxidative stress-dependent atrial pathophysiology and support the investigation of dicarbonyl scavengers as a novel therapeutic approach to prevent AF.
An expressiveness study of priority in process calculi

Priority is a frequently used feature of many computational systems. In this paper we study the expressiveness of two process algebras enriched with different priority mechanisms. In particular, we consider a finite (that is, recursion-free) fragment of asynchronous CCS with global priority (FAP, for short) and Phillips' CPG (CCS with local priority), and contrast their expressive power with that of two non-prioritised calculi, namely the π-calculus and its broadcast-based version, called bπ. We prove, by means of leader-election-based separation results, that, under certain conditions, there exists no encoding of FAP in π-Calculus or CPG. Moreover, we single out another problem in distributed computing, which we call the last man standing problem (LMS for short), that better reveals the gap between the two prioritised calculi above and the two non-prioritised ones, by proving that there exists no parallel-preserving encoding of the prioritised calculi in the non-prioritised calculi retaining any sincere (complete but partially correct, that is, admitting divergence or premature termination) semantics.
On ASGS framework: general requirements and an example of implementation

In the paper we propose a general, abstract framework for Automatic Secret Generation and Sharing (ASGS) that should be independent of underlying Secret Sharing Scheme (SSS). ASGS allows to prevent the Dealer from knowing the secret. The Basic Property Conjecture (BPC) forms the base of the framework. Due to the level of abstraction, results are portable into the realm of quantum computing.Two situations are discussed. First concerns simultaneous generation and sharing of the random, prior nonexistent secret. Such a secret remains unknown until it is reconstructed. Next, we propose the framework for automatic sharing of a known secret. In this case the Dealer does not know the secret and the secret Owner does not know the shares. We present opportunities for joining ASGS with other extended capabilities, with special emphasis on PVSS and pre-positioned secret sharing. Finally, we illustrate framework with practical implementation.
Cost Sensitive and Preprocessing for Classification with Imbalanced Data-sets: Similar Behaviour and Potential Hybridizations

The scenario of classification with imbalanced data-sets has supposed a serious challenge for researchers along the last years. The main handicap is related to the large number of real applications in which one of the classes of the problem has a few number of examples in comparison with the other class, making it harder to be correctly learnt and, what is most important, this minority class is usually the one with the highest interest. In order to address this problem, two main methodologies have been proposed for stressing the significance of the minority class and for achieving a good discrimination for both classes, namely preprocessing of instances and cost-sensitive learning. The former rebalances the instances of both classes by replicating or creating new instances of the minority class (oversampling) or by removing some instances of the majority class (undersampling); whereas the latter assumes higher misclassification costs with samples in the minority class and seek to minimize the high cost errors. Both solutions have shown to be valid for dealing with the class imbalance problem but, to the best of our knowledge, no comparison between both approaches have ever been performed. In this work, we carry out a full exhaustive analysis on this two methodologies, also including a hybrid procedure that tries to combine the best of these models. We will show, by means of a statistical comparative analysis developed with a large collection of more than 60 imbalanced data-sets, that we cannot highlight an unique approach among the rest, and we will discuss as a potential research line the use of hybridizations for achieving better solutions to the imbalanced data-set problem.
Study of statistical robust closed set speaker identification with feature and score-based fusion

In this paper, the statistical combination of Power Normalization Cepstral Coefficient (PNCC) and Mel Frequency Cepstral Coefficient (MFCC) features in robust closed set speaker identification is studied. Feature normalization and warping together with late score-based fusion are also exploited to improve performance in the presence of channel and noise effects. In addition, combinations of score and feature-based approaches are considered with early and/or late fusion; these systems use different feature dimensions (16, 32). A 4th order G.712 type IIR filter is employed to represent handset degradation in the channel. Simulation studies based on the TIMIT database confirm the improvement in Speaker Identification Accuracy (SIA) through the combination of PNCC and MFCC features in the presence of handset and Additive White Gaussian Noise (AWGN) effects.
Speech spectral envelope estimation through explicit control of peak evolution in time

This work proposes a new approach to estimating the speech spectral envelope that is adapted for applications requiring time-varying spectral modifications, such as Voice Conversion. In particular, we represent the spectral envelope as a sum of peaks that evolve smoothly in time, within a phoneme. Our representation provides a flexible model for the spectral envelope that pertains relevantly to human speech production and perception. We highlight important properties of the proposed spectral envelope estimation, as applied to natural speech, and compare results with those from a more traditional frame-by-frame cepstrum-based analysis. Subjective evaluations and comparisons of synthesized speech quality, as well as implications of this work in future research are also discussed.
Generation of Efficient Codes for Realizing Boolean Functions in Nanotechnologies

We address the challenge of implementing reliable computation of Boolean functions in future nanocircuit fabrics. Such fabrics are projected to have very high defect rates. We overcome this limitation by using a combination of cheap but unreliable nanodevices and reliable but expensive CMOS devices. In our approach, defect tolerance is achieved through a novel coding of Boolean functions; specifically, we exploit the dont cares of Boolean functions encountered in multi-level Boolean logic networks for constructing better codes. We show that compared to direct application of existing coding techniques, the coding overhead in terms of extra bits can be reduced, on average by 23%, and savings can go up to 34%. We demonstrate that by incorporating efficient coding techniques more than a 40% average yield improvement is possible in case of 1% and 0.1% defect rates. With 0.1% defect density, the savings can be up to 90%.
Annual distributions and sources of Arctic aerosol components, aerosol optical depth, and aerosol absorption

Radiative forcing by aerosols and tropospheric ozone could play a significant role in recent Arctic warming. These species are in general poorly accounted for in climate models. We use the GEOS‐Chem global chemical transport model to construct a 3‐D representation of Arctic aerosols and ozone that is consistent with observations and can be used in climate simulations. We focus on 2008, when extensive observations were made from different platforms as part of the International Polar Year. Comparison to aircraft, surface, and ship cruise observations suggests that GEOS‐Chem provides in general a successful year‐round simulation of Arctic black carbon (BC), organic carbon (OC), sulfate, and dust aerosol. BC has major fuel combustion and boreal fire sources, OC is mainly from fires, sulfate has a mix of anthropogenic and natural sources, and dust is mostly from the Sahara. The model is successful in simulating aerosol optical depth (AOD) observations from Aerosol Robotics Network stations in the Arctic; the sharp drop from spring to summer appears driven in part by the smaller size of sulfate aerosol in summer. The anthropogenic contribution to Arctic AOD is a factor of 4 larger in spring than in summer and is mainly sulfate. Simulation of absorbing aerosol optical depth (AAOD) indicates that non‐BC aerosol (OC and dust) contributed 24% of Arctic AAOD at 550 nm and 37% of absorbing mass deposited to the snow pack in 2008. Open fires contributed half of AAOD at 550 nm and half of deposition to the snowpack.
An Optimal Bifactor Approximation Algorithm for the Metric Uncapacitated Facility Location Problem

We consider the metric uncapacitated facility location problem(UFL). In this paper we modify the (1 + 2/e)-approximation algorithm of Chudak and Shmoys to obtain a new (1.6774,1.3738)- approximation algorithm for the UFL problem. Our linear programing rounding algorithm is the first one that touches the approximability limit curve $(\gamma_f, 1+2e^{-\gamma_f})$ established by Jain et al. As a consequence, we obtain the first optimal approximation algorithm for instances dominated by connection costs. Our new algorithm - when combined with a (1.11,1.7764)-approxima- tion algorithm proposed by Jain, Mahdian and Saberi, and later analyzed by Mahdian, Ye and Zhang - gives a 1.5-approximation algorithm for the metric UFL problem. This algorithm improves over the previously best known 1.52-approximation algorithm by Mahdian, Ye and Zhang, and it cuts the gap with the approximability lower bound by 1/3. The algorithm is also used to improve the approximation ratio for the 3-level version of the problem.
Multirate Anypath Routing in Wireless Mesh Networks

In this paper, we present a new routing paradigm that generalizes opportunistic routing in wireless mesh networks. In multirate anypath routing, each node uses both a set of next hops and a selected transmission rate to reach a destination. Using this rate, a packet is broadcast to the nodes in the set and one of them forwards the packet on to the destination. To date, there is no theory capable of jointly optimizing both the set of next hops and the transmission rate used by each node. We bridge this gap by introducing a polynomial-time algorithm to this problem and provide the proof of its optimality. The proposed algorithm runs in the same running time as regular shortest-path algorithms and is therefore suitable for deployment in link-state routing protocols. We conducted experiments in a 802.11b testbed network, and our results show that multirate anypath routing performs on average 80% and up to 6.4 times better than anypath routing with a fixed rate of 11 Mbps. If the rate is fixed at 1 Mbps instead, performance improves by up to one order of magnitude. I. INTRODUCTION The high loss rate and dynamic quality of links make routing in wireless mesh networks extremely challenging (1). Anypath routing 1 has been recently proposed as a way to circumvent these shortcomings by using multiple next hops for each destination (3)-(6). Each packet is broadcast to a forwarding set composed of several neighbors, and the packet must be retransmitted only if none of the neighbors in the set receive it. Therefore, while the link to a given neighbor is down or performing poorly, another nearby neighbor may receive the packet and forward it on. This is in contrast to single-path routing where only one neighbor is assigned as the next hop for each destination. In this case, if the link to this neighbor is not performing well, a packet may be lost even though other neighbors may have overheard it. Existing work on anypath routing has focused on wireless networks that use a single transmission rate. This approach, albeit straightforward, presents two major drawbacks. First, using a single rate over the entire network underutilizes available bandwidth resources. Some links may perform well at a higher rate, while others may only work at a lower rate. Secondly and most importantly, the network may become disconnected at a higher bit rate. We provide experimental measurements from a 802.11b testbed which show that this phenomenon is not uncommon in practice. The key problem is that higher transmission rates have a shorter radio range, which reduces network density and connectivity. As the bit rate in- creases, links becomes lossier and the network eventually gets disconnected. Therefore, in order to guarantee connectivity, single-rate anypath routing must be limited to low rates. In multirate anypath routing, these problems do not exist; however, we face different challenges. First, we must find 1 We use the term anypath rather than opportunistic routing, since oppor- tunistic routing is an overloaded term also used for opportunistic contacts (2).
AUTOMATIC SPEECH RECOGNITION-BASED INFORMATION CENTER FOR INDONESIAN LANGUAGE

This paper discusses an implementation of CMU Sphinx-4 in an automatic speech recognition-based information center (ASRIC) for Indonesian language. The ASRIC uses a vector space model (VSM) to improve the performance of statistical language model and to recognize the user utterance more flexibly. Testing to an Indonesian speaker shows that VSM is capable of reducing the query error rate in acceptable response time. The VSM also make ASRIC more flexible to recognize user’s spoken sentences which misses some keywords, contains random ordered words, or consists of unimportant words.