Miracle-dz/UniFAR-FTUs huggingface dataset:
PySBD: Pragmatic Sentence Boundary Disambiguation

We present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages. We aim to provide a realistic segmenter which can provide logical sentences even when the format and domain of the input text is unknown. In our work, we adapt the Golden Rules Set (a language specific set of sentence boundary exemplars) originally implemented as a ruby gem pragmatic segmenter which we ported to Python with additional improvements and functionality. PySBD passes 97.92% of the Golden Rule Set examplars for English, an improvement of 25% over the next best open source Python tool.
Extraction of Discrete Spectra Modes from Video Data Using a Deep Convolutional Koopman Network

Recent deep learning extensions in Koopman theory have enabled compact, interpretable representations of nonlinear dynamical systems which are amenable to linear analysis. Deep Koopman networks attempt to learn the Koopman eigenfunctions which capture the coordinate transformation to globally linearize system dynamics. These eigenfunctions can be linked to underlying system modes which govern the dynamical behavior of the system. While many related techniques have demonstrated their efficacy on canonical systems and their associated state variables, in this work the system dynamics are observed optically (i.e. in video format). We demonstrate the ability of a deep convolutional Koopman network (CKN) in automatically identifying independent modes for dynamical systems with discrete spectra. Practically, this affords flexibility in system data collection as the data are easily obtainable observable variables. The learned models are able to successfully and robustly identify the underlying modes governing the system, even with a redundantly large embedding space. Modal disaggregation is encouraged using a simple masking procedure. All of the systems analyzed in this work use an identical network architecture.
Diverse Data Selection under Fairness Constraints

Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection, while offering fairness guarantees. In particular, we offer the first study that augments the Max-Min diversification objective with fairness constraints. More specifically, given a universe $U$ of $n$ elements that can be partitioned into $m$ disjoint groups, we aim to retrieve a $k$-sized subset that maximizes the pairwise minimum distance within the set (diversity) and contains a pre-specified $k_i$ number of elements from each group $i$ (fairness). We show that this problem is NP-complete even in metric spaces, and we propose three novel algorithms, linear in $n$, that provide strong theoretical approximation guarantees for different values of $m$ and $k$. Finally, we extend our algorithms and analysis to the case where groups can be overlapping.
cDNA Cloning of Paramyosin from Several Kinds of Squid Mantle Muscle

Paramyosin is a rod-shaped muscle protein found exclusively in invertebrates, with α-helices coiled around each other to form a coiled-coil structure. Marine organisms in which the primary structure of paramyosin has been determined are mollusks, including abalone (Haliotis discus), mussels (Mytilus galloprovincialis), octopus (Octopus bimaculoides), and oyster (Crassostrea gigas). In contrast, the primary structure of squid paramyosin, which is of particular interest, has yet to be reported. In the present study, cDNA cloning of paramyosins from four squid species, the neon flying squid (Ommastrephes bartramii), the Humboldt squid (Dosidicus gigas), the golden cuttlefish (Sepia esculenta), and the clawed armhook squid (Gonatus onyx), was performed to determine the following: the 2605-bp O. bartramii paramyosin gene containing a 2574-bp open reading frame (ORF), the 2691-bp D. gigas paramyosin gene containing a 2640-bp ORF, the 2631-bp S. esculenta paramyosin gene containing a 2574 ORF, and the 2609-bp G. onyx paramyosin gene containing a 2574-bp ORF. The primary structure of the four squid paramyosins was found to contain heptad repeats and an ACD (assembly competence domain), which are characteristic of a coiled coil. A phylogenetic analysis was performed with paramyosin sequences from species including the four squid species examined in this study, the results of which indicated that the four squid paramyosins form a group independent from the paramyosins of other species, to which octopus paramyosins are closest.
Learning Parameter Distributions to Detect Concept Drift in Data Streams

Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and robust identification of concept drift can be difficult, as we never have access to the true distribution of streaming data. In this work, we propose a novel framework for the detection of real concept drift, called ERICS. By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters. To this end, we adopt common measures from information theory. The proposed framework is completely model-agnostic. By choosing an appropriate base model, ERICS is also capable to detect concept drift at the input level, which is a significant advantage over existing approaches. An evaluation on several synthetic and real-world data sets suggests that the proposed framework identifies concept drift more effectively and precisely than various existing works.
LoRa Performance Analysis with Superposed Signal Decoding

This letter considers the use of successive interference cancellation (SIC) to decode superposed signals in Long Range (LoRa) networks. We build over a known stochastic geometry model for LoRa networks and include the effect of recovering colliding packets through SIC. We derive closed-form expressions for the successful decoding of packets using SIC taking path loss, fading, noise, and interference into account, while we validate the model by means of Monte Carlo simulations. Results show that SIC-enabled LoRa networks improve worst-case reliability by up to 34%. We show that, for at least one test scenario, SIC increases by 159% the number of served users with the same worst-case reliability level.
Image-based Automated Species Identification: Can Virtual Data Augmentation Overcome Problems of Insufficient Sampling?

Automated species identification and delimitation is challenging, particularly in rare and thus often scarcely sampled species, which do not allow sufficient discrimination of infraspecific versus interspecific variation. Typical problems arising from either low or exaggerated interspecific morphological differentiation are best met by automated methods of machine learning that learn efficient and effective species identification from training samples. However, limited infraspecific sampling remains a key challenge also in machine learning. In this study, we assessed whether a data augmentation approach may help to overcome the problem of scarce training data in automated visual species identification. The stepwise augmentation of data comprised image rotation as well as visual and virtual augmentation. The visual data augmentation applies classic approaches of data augmentation and generation of artificial images using a Generative Adversarial Networks (GAN) approach. Descriptive feature vectors are derived from bottleneck features of a VGG-16 convolutional neural network (CNN) that are then stepwise reduced in dimensionality using Global Average Pooling and PCA to prevent overfitting. Finally, data augmentation employs synthetic additional sampling in feature space by an oversampling algorithm in vector space (SMOTE). Applied on four different image datasets, which include scarab beetle genitalia (Pleophylla, Schizonycha) as well as wing patterns of bees (Osmia) and cattleheart butterflies (Parides), our augmentation approach outperformed a deep learning baseline approach by means of resulting identification accuracy with non-augmented data as well as a traditional 2D morphometric approach (Procrustes analysis of scarab beetle genitalia).
Optimierung von Unternehmensbewertungen durch automatisierte Wissensidentifikation, -extraktion und -integration

Zusammenfassung Unternehmensbewertungen in der Biotech-Branche, Pharmazie und Medizintechnik stellen eine anspruchsvolle Aufgabe dar, insbesondere bei Berücksichtigung der einzigartigen Risiken, denen Biotech-Startups beim Eintritt in neue Märkte ausgesetzt sind. Unternehmen, die auf globale Bewertungsdienstleistungen spezialisiert sind, kombinieren daher Bewertungsmodelle und Erfahrungen aus der Vergangenheit mit heterogenen Metriken und Indikatoren, die Einblicke in die Leistung eines Unternehmens geben. Dieser Beitrag veranschaulicht, wie automatisierte Wissensidentifikation, -extraktion und -integration genutzt werden können, um (i) zusätzliche Indikatoren zu ermitteln, die Einblicke in den Erfolg eines Unternehmens in der Produktentwicklung geben und um (ii) arbeitsintensive Datensammelprozesse zur Unternehmensbewertung zu unterstützen.
Intelligent Reflecting Surface-Assisted Bistatic Backscatter Networks: Joint Beamforming and Reflection Design

Bistatic backscatter communication (BackCom) allows passive tags to transmit over extended ranges, but at the cost of having carrier emitters either transmitting at high powers or being deployed very close to tags. In this paper, we examine how the presence of an intelligent reflecting surface (IRS) could benefit the bistatic BackCom system. We study the transmit power minimization problem at the carrier emitter, where its transmit beamforming vector is jointly optimized with the IRS phase shifts, whilst guaranteeing a required BackCom performance. A unique feature in this system setup is the multiple IRS reflections experienced by signals traveling from the carrier emitter to the reader, which renders the optimization problem highly nonconvex. Therefore, we propose algorithms based on the minorization-maximization and alternating optimization techniques to obtain approximate solutions for the joint design. We also propose low-complexity algorithms based on successive optimization of individual phase shifts. Our results reveal considerable transmit power savings in both single-tag and multi-tag systems, even with moderate IRS sizes, which may be translated to significant range improvements using the original transmit power or a reduction of the reliance of tags on carrier emitters located at close range.
Parametric non-interference in timed automata

We consider a notion of non-interference for timed automata (TAs) that allows to quantify the frequency of an attack; that is, we infer values of the minimal time between two consecutive actions of the attacker, so that (s)he disturbs the set of reachable locations. We also synthesize valuations for the timing constants of the TA (seen as parameters) guaranteeing non-interference. We show that this can reduce to reachability synthesis in parametric timed automata. We apply our method to a model of the Fischer mutual exclusion protocol and obtain preliminary results.
6G in the sky: On‐demand intelligence at the edge of 3D networks (Invited paper)

Sixth generation will exploit satellite, aerial, and terrestrial platforms jointly to improve radio access capability and unlock the support of on‐demand edge cloud services in three‐dimensional (3D) space, by incorporating mobile edge computing (MEC) functionalities on aerial platforms and low‐orbit satellites. This will extend the MEC support to devices and network elements in the sky and forge a space‐borne MEC, enabling intelligent, personalized, and distributed on‐demand services. End users will experience the impression of being surrounded by a distributed computer, fulfilling their requests with apparently zero latency. In this paper, we consider an architecture that provides communication, computation, and caching (C3) services on demand, anytime, and everywhere in 3D space, integrating conventional ground (terrestrial) base stations and flying (non‐terrestrial) nodes. Given the complexity of the overall network, the C3 resources and management of aerial devices need to be jointly orchestrated via artificial intelligence‐based algorithms, exploiting virtualized network functions dynamically deployed in a distributed manner across terrestrial and non‐terrestrial nodes.
A contagious disease of salmon, possibly of virus origin

A disease; possibly of virus origin, is described which affected bluebacksalmon amI kokanee fingerlings (Oncor"'1/nchtlS nerka) , but did not infect fingerlings of the chinook salmon (0. tshawytscha.) , silver salmon (0. kilJU.tc"') , or cutthroat trout (Ba~mQ c~a.rki c~arkl). The fis! J. first became infected when they were 1 to 1.5 inches long and about 3 months old. Diseased fish were lethargic. Hemorrhagic areas often appeared at the base of the fins and in the isthmus. The stomach was distended with a milky :fluid; the intestine appeared reddened and contained a straw-colored, watery fluid often tinged with blood. The bile appeared. normal although the gall bladder was distended. Hyperemic areas frequently were present in the air bladder, IJeritoneum, and fat. Spinal deformities developed among some of the fish that survived the. early stages of the epidemic. Therapeutic measures consisting of sulfonamides, antibiotics, and external disinfectants failed' to 'control the disease. Rigid sanitary measures limited spread of the infection in some cases. UNITED STATES DEPARTMENT OF THE INTERIOR, Douglas McKay, Secretary FISH AND WILDLIFE SERVICE, Albert M. Day, Director A CONTAGIOUS DISEASE OF SALMON POSSIBLY OF VIRUS ORIGIN By R. R. Rucker, W. J. Whipple, J. R. Parvin
DeepWiPHY: Deep Learning-Based Receiver Design and Dataset for IEEE 802.11ax Systems

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network’s architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.
Isolation and Characterization of Bacterial Leaf Blight of Rice (Xanthomonas oryzae pv. oryzae) Isolates from Southern India

Bacterial leaf blight (BLB) of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is a major biotic constraint in rice cultivation and is wide spread in Asia, including India. A total of 54 isolates were collected from different rice growing zones of southern India. All the isolates were obtained and subjected to pathogenicity test on BPT-5204 that varied significantly in terms of disease severity among each other and further confirmed withcolony morphological features and biochemical tests. Colony size of the isolates ranged from 1.2 mm to 4.0 mm with light yellow to yellow and creamy yellow colour with circular to irregular margin. Most of the isolates produced raised, slimy colonies but few isolates produced flattened and slimy colonies. All the isolates were subjected to different biochemical tests and were found to be positive for 3 per cent KOH test, gelatin liquefaction, catalase test, while, negative for starch hydrolysis and oxidase tests. Five isolates namely, Xoo7, Xoo16, Xoo22, Xoo27 and Xoo50 found negative for H2S production test.
Biotechnology of Schlumbergera and Rhipsalidopsis

Biotechnological tools have been successfully used with several plant species for improving existing cultivars and creating new ones. However, the application of such tools has been limited with ornamental plant species such as cactus. Cacti are known to possess a very slow growth rate and many of them are recalcitrant compared to other cultivated plant species thus creating problems in culturing or improving them. Some early work showed promise in establishing in vitro cultures with limited multiplication rates, but research findings recently indicate that Schlumbergera (Christmas cactus) and Rhipsalidopsis (Easter cactus) can be multiplied more rapidly via both axillary and adventitious shoots, and also via callus and somatic embryogenesis. These studies have discovered key factors that could enhance an understanding of morphogenesis in cacti. Studies were made on endogenous phytohormones and enzymes to elucidate the differences between the recalcitrant cultures and those with increased regenerative capacity of these plants. Recent studies have also shown that Agrobacterium-mediated genetic transformation is possible and a transformation efficiency of about 23% in Rhipsalidopsis is reported. This transformation efficiency was based on transgenic shoots expressing the activity of the reporter �-glucuronidase gene (GUS) indicated by blue staining of the regenerated shoots. Transgenic shoots regenerated in these studies through callus phase. Transformation was confirmed in the selected transgenic callus lines by GUS staining (for uidA gene), ELISA analysis and Southern blot hybridization (for nptII gene). The stability of transgenic calli was confirmed through their continuous sub-culturing on media containing the selectable marker kanamycin followed by testing for kanamycin resistance, GUS analysis and Southern hybridization. The regeneration system adopted for transformation was reliable as shown by the stable regeneration of calli and shoots growing on media both on selection and control media. However, due to the slow growth rate of these plants, no attempts were made to verify transformation in the greenhouse-grown plants.
Brevibacillin, an Antimicrobial Lipopeptide Discovered from Genus Brevibacillus: Structural Elucidation, Mode of Action, Fermentation and Application in Commercial Apple Juice

A new environmental bacterial strain exhibited strong antimicrobial characteristics against methicillin-resistant Staphylococcus aureus, vancomycin-resistant strains of Enterococcus faecalis and Lactobacillus plantarum, and other Gram-positive bacteria. The producer strain, designated OSY-I1, was determined to be Brevibacillus laterosporus via morphological, biochemical and genetic analyses. The antimicrobial agent was extracted from cells of OSY-I1 with isopropanol, purified by highperformance liquid chromatography, and structurally analyzed using mass spectrometry (MS) and nuclear magnetic resonance (NMR). The MS and NMR results, taken together, uncovered a linear lipopeptide consisting of 13 amino acids and an N-terminal C6-fatty acid chain (FA) chain, 2-hydroxy-3-methylpentanoic acid. The lipopeptide (FADhb Leu Orn Ile Ile Val Lys Val Val Lys Tyr Leu – valinol, where Dhb is ,didehydrobutyric acid and valinol is 2-amino-3-methyl-1-butanol) has a molecular mass of 1583.0794 Da and contains three modified amino acid residues; ,didehydrobutyric acid, ornithine, and valinol. The compound, designated brevibacillin, was determined to be a member of a cationic lipopeptide antibiotic family. In addition to its potency against drug-resistant bacteria, brevibacillin also exhibited low MICs (18 μg/ml) against selected foodborne pathogenic and spoilage bacteria, such as Listeria monocytogenes, Bacillus cereus and Alicyclobacillus acidoterrestris. Purified
Condition and mortality of western painted turtle collected as bycatch in modified fyke nets during fish population assessments

ABSTRACT Information concerning turtle bycatch and its possible ramifications during biological sampling in freshwater systems is limited. Having such information will enable fisheries researchers and managers to modify fish sampling procedures to potentially minimize any impacts on turtle populations. Therefore, our objective was to utilize reflex response to characterize stress and mortality of western painted turtles (Chrysemys picta bellii) captured as bycatch in modified fyke nets during fish population assessments. Reflex-response-based condition classification (i.e. good, poor, unresponsive) data were collected from May to September during fish population assessments in 38 lakes and seven impoundments in northeast South Dakota from 2012 to 2014. We evaluated the relationship between observed condition and water temperature during two time periods, by turtle gender and size. Turtle bycatch mortality was assessed from June to September during fish population assessments in 14 lakes and three impoundments during 2013 and 2014. Delayed (i.e. overnight) mortality was determined for poor and unresponsive condition classifications and the relationship between observed mortality and water temperature for two time periods, by turtle gender and size was evaluated. Turtle condition decreased as water temperature increased. Condition was poorer during the May and June time period than August and September. Female turtles were in significantly worse condition than males. Condition was not related to turtle size. We observed varying mortality rates across condition classifications and the total estimated delayed mortality rate of known sex turtles was 36.4%. Mortality increased as temperature increased. Delayed mortality rates varied between the two sampling periods with higher mortality rates during the May and June time period. Female turtles had higher mortality rates than males. Delayed mortality did not vary by turtle size. Understanding the negative effects on bycatch during biological sampling is important to provide justification for the development and implementation of measures to protect non-target species.
Characterizing the Contribution of First and Second Heart Field-derived Cells in Zebrafish Cardiac Development

Vertebrate heart development occurs through contribution from two spatio-temporally distinct progenitor populations termed the First Heart Field (FHF) and Second Heart Field (SHF). In the mammalian heart, the FHF gives rise to the left ventricle and parts of both atria, whereas the SHF contributes to the right ventricle, inflow and outflow tracts, and portions of the atria. Here I employed a cmlc2 driven CreERT and switch reporter lines to lineage trace the two populations in the adult zebrafish heart. Late differentiating cells, as displayed through EGFP-positive cells consistently made a significant contribution to the cortical layer of the adult heart, which has been shown to be a major source of new cardiomyocytes following cardiac injury. Cells derived from the proposed SHF population give rise to the primordial layer, as well as the trabeculae in the ventricular base. The cortical and primordial layers are partially derived from the SHF progenitor population.