Miracle-dz/UniFAR-FTUs huggingface dataset:
Extractive Timeline Summarization based on Unsupervised Techniques

With the increasing importance of internet during time, a huge amount of news article about several arguments are published in several websites: users that are interested in a particular event could use an automatic system that presents to him the most important aspects of the event and/or its evolution across time. These two problems are addressed by: • Multi-document Summarization, with the aim of condensing news from several articles in a complete and non-redundant summary • Temporal Summarization, with the aim of keeping the user informed about the developing of an event,providing non-redundand and significant updates as soon as new articles arrives (or at prefixed time intervals) • Timeline Summarization, with the aim of providing the end-user with the history of a concluded event along a timeline that highlights the most significant dates for the event and the most significant happenings for each date. We analyzed the State-of-the-Art in text, temporal and timeline summarization presenting several algorithms that have been proposed in the past years: the aim of this thesis is to create a framework that performs timeline summarizzation following a pipeline (date selection,date summarization,timeline visualization), extracting, from an input set of document about a specific topic, the most important dates and then applying text summarization to select the most important aspects (sentences) for each date. The aim is to explore the performance of several text summarization algorithms used in the date summarizzation step (in which a summary is extracted, for each date, from the input sentences associated to that date), comparing at the end the results obtained by testing the several algorithms on CRISIS and T17 datasets and evaluating the output timelines using some ROUGE variants. To ease the exploration of the generated summaries, we developed a Web application tailored to date summary visualization. It provides a visual extract of the summary content together with a representative image crawled by Google. It constitutes the last block of the pipeline,aimed at providing end-user with a more friendly tool to explore the timeline generated by the system.
SIMULATIONS OF HOLE INJECTION IN DIAMOND DETECTORS∗

We present simulations of a semiconductor beam detector using the code VSim. The 3D simulations involve the movement and scattering of electrons and holes in the semiconductor, voltages which may be applied to external contacts, and self-consistent electrostatic fields inside the device. Electrons may experience a Schottky barrier when attempting to move from the semiconductor into a metal contact. The strong field near the contact, due to trapped electrons, can result in hole injection into the semiconductor due to transmission of electrons from the valence band of the semiconductor into the metal contact. Injected holes are transported in the applied field leading to current through the detector. We compare our simulation results with experimental results from a prototype diamond X-ray detector.
Path analysis under generalized assortative mating

Assortative mating is generalized to include social homogamy and phenotypic homogamy as two special cases. This generalization, called mixed homogamy, enables tests of hypotheses on the nature of assortment. Cultural inheritance is also extended to include two components: transmitted from parental environments, and non-transmitted sibship environment. Familial correlations are derived for a variety of relationships under mixed homogamy.
Efficacy and Safety of a Drug-Free, Barrier-Forming Nasal Spray for Allergic Rhinitis: Randomized, Open-Label, Crossover Noninferiority Trial

Introduction: Symptoms of allergic rhinitis can be reduced by nonpharmacological nasal sprays that create a barrier between allergens and the nasal mucosa. A new nasal spray (AM-301) containing the clay mineral bentonite was tested for its ability to reduce symptoms of grass pollen. Methods: This open-label, crossover, noninferiority trial compared the efficacy and safety of AM-301 to that of hydroxypropyl methylcellulose (HPMC; Nasaleze® Allergy Blocker), an established barrier method. Adults with seasonal allergic rhinitis were exposed to Dactylis glomerata pollen, in a controlled setting, the Fraunhofer allergen challenge chamber, first without protection and then protected by HPMC or AM-301 (7 days apart). Efficacy was assessed from total nasal symptom score (TNSS), nasal secretion weight, and subjective rating. The primary endpoint was the difference, between AM-301 and HPMC, in least square mean change in TNSS over a 4-h exposure to allergen. Results: The study enrolled 36 persons, and 35 completed all study visits. The mean TNSS was 5.91 (SD = 1.45) during unprotected exposure, 5.20 (SD = 1.70) during protection with HPMC, and 4.82 (SD = 1.74) during protection with AM-301. The difference in least square means between the two treatments was −0.39 (95% CI: −0.89 to 0.10), establishing the noninferiority of AM-301. No difference in mean weight of nasal secretions was observed between the treatments. Efficacy was rated as good or very good for AM-301 by 31% and for HPMC by 14% of subjects. Sixteen subjects reported adverse events with a relationship to AM-301 or HPMC; most adverse events were mild, and none was serious. Discussion/Conclusion: AM-301 demonstrated noninferiority toward HPMC in the primary endpoint and was perceived better in subjective secondary endpoints. Both barrier-forming products had a persisting protective effect over 4 h and were safe.
Controlling Memory Footprint of Stateful Streaming Graph Processing

With growing interest in efﬁciently analyzing dynamic graphs, streaming graph processing systems rely on stateful iterative models where they track the intermediate state as execution progresses in order to incrementally adjust the results upon graph mutation. We observe that the intermediate state tracked by these stateful iterative models signiﬁcantly increases the memory footprint of these systems, which limits their scalability on large graphs. In this paper, we develop memory-efﬁcient stateful iterative models that demand much less memory capacity to efﬁciently process streaming graphs and deliver the same results as provided by existing stateful iterative models. First, we propose a Selective Stateful Iterative Model where the memory footprint is controlled by selecting a small portion of the intermediate state to be maintained throughout execution. Then, we propose a Minimal Stateful Iterative Model that further reduces the memory footprint by exploiting key properties of graph algorithms. We develop incremental processing strategies for both of our models in order to correctly compute the effects of graph mutations on the ﬁnal results even when intermediate states are not available. Evaluation shows our memory-efﬁcient models are effective in limiting the memory footprint while still retaining most of the performance beneﬁts of traditional stateful iterative models, hence being able to scale on larger graphs that could not be handled by the traditional models.
The importance of sesquiterpene oxidation products for secondary organic aerosol formation in a springtime hemiboreal forest

Abstract. Secondary organic aerosols (SOAs) formed from biogenic volatile organic compounds (BVOCs) constitute a significant fraction of atmospheric particulate matter and have been recognized to significantly affect the climate and air quality. Atmospheric SOA particulate mass yields and chemical composition result from a complex mixture of oxidation products originating from a diversity of BVOCs. Many laboratory and field experiments have studied SOA particle formation and growth in the recent years. However, a large uncertainty still remains regarding the contribution of BVOCs to SOA. In particular, organic compounds formed from sesquiterpenes have not been thoroughly investigated, and their contribution to SOA remains poorly characterized. In this study, a Filter Inlet for Gases and Aerosols (FIGAERO) combined with a high-resolution time-of-flight chemical ionization mass spectrometer (CIMS), with iodide ionization, was used for the simultaneous measurement of gas-phase and particle-phase oxygenated compounds. The aim of the study was to evaluate the relative contribution of sesquiterpene oxidation products to SOA in a springtime hemiboreal forest environment. Our results revealed that monoterpene and sesquiterpene oxidation products were the main contributors to SOA particles. The chemical composition of SOA particles was compared for times when either monoterpene or sesquiterpene oxidation products were dominant and possible key oxidation products for SOA particle formation were identified for both situations. Surprisingly, sesquiterpene oxidation products were the predominant fraction in the particle phase in some periods, while their gas-phase concentrations remained much lower than those of monoterpene products. This can be explained by favorable and effective partitioning of sesquiterpene products into the particle phase. The SOA particle volatility determined from measured thermograms increased when the concentration of sesquiterpene oxidation products in SOA particles was higher than that of monoterpenes. Overall, this study demonstrates that sesquiterpenes may have an important role in atmospheric SOA formation and oxidation chemistry, in particular during the spring recovery period.
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

We first introduce the required notions and notation. We note that even though these results mainly refer to simplicial complexes, they also apply to graphs because any graph is also a simplicial complex. Definition 20 (Simplicial Colouring). A simplicial colouring is a map c that maps a simplicial complex K and one of its simplices σ to a colour from a fixed colour palette. We denote this colour by cσ .
Clinical Outcomes Based on Oswestry Disability Index After Lumbar Disc Ablation and Disc Prolapse in Eastern Province, Saudi Arabia

Zaid Abdullatif AlZaid1*, Abduaziz Mohammed Alhawas1*, Khalid Alhumam2, Ibrahim Sallout1, Abdulrahman AlRowili1, Razan Je’aan1, Mohammed AlMutawa3, Mohammed S Alarbash4, Raghad Ibrahim Al-johani5, Bashair Khalid Al-Otaibi5, Sadeem Abdullah Al-Bulaihed6, Ibrahim Al Yaeesh7 and Abdullah Al-Othman8 1Orthopedic resident, King Fahd Hospital of the University, AlKhobar, Saudi Arabia 2Orthopedic resident, King Abdulaziz Hospital, Al Mubarraz, Saudi Arabia 3Orthopedic resident, Dammam Medical Complex, Dammam, Saudi Arabia 4Orthopedic resident, King Fahd Hospital Al-Hofuf, Saudi Arabia 5Medical student, Imam Abdulrahman Bin Faisal University, AlKhobar, Saudi Arabia 6Medical student, Alfaisal University, Al-Riyadh, Saudi Arabia 7Medical Intern, King Faisal University, Al-Hofuf, Saudi Arabia 8Professor, King Fahd Hospital of the University, AlKhobar, Saudi Arabia *Corresponding Author: Zaid Abdullatif AlZaid and Abduaziz Mohammed Alhawas, Orthopedic Resident, King Fahd Hospital of the University, AlKhobar, Saudi Arabia. Received: February 06, 2021; Published: March 18, 202
Effect of Detoxified Nano Sulfur Supplementation on the Growth, Nutrient Digestibility, Meat Quality, Excreta Microbes, Gas Emissions, and Blood Profiles of Broilers

A 35-day experiment was conducted to evaluate the effects of the supplementation of mineral detoxified sulfur dispersion ((DSD); Patent No.: 10-1997773) on the growth performance, meat quality, excreta microbiota, gas emissions, nutrient digestibility, and blood profiles of broilers. In total, 720 one-day-old ROSS 308 broilers, with an initial body weight of 41.9±0.8 g, were divided into two (2) treatment groups with 20 replicate pens/groups composed of 18 birds per pen. Treatments consisted of 1) CON (the control), normal drinking water and 2) TRT (the treatment group), CON+0.001% DSD (1000:1 dilution ratio). Average daily feed intake (ADFI) and feed conversion ratio (FCR) increased in the TRT group (P<0.05) between days 1 to 7 and days 7 to 21 of the experimental period. Similarly, body weight gain (BWG) showed a significant increase (P<0.05) in the DSD-supplemented group throughout in the length of the experiment. With regard to meat quality, redness (a*) was higher, while drip loss was lower, on the 7th day in the DSD group. Furthermore, DSD supplementation increased (P<0.05) Lactobacillus excreta but decreased E. coli concentrations in the TRT group compared to the CON group. Notably, nutrient digestibility, excreta gas emission, and blood profiles did not show any significant differences (P>0.05). DSD supplementation, administered through drinking water, has a positive impact on the growth performance, meat quality, and excreta microbiota of broiler chickens.
Stylizing Vector Animation Using Natural Dynamics of Artistic Media

In this thesis, we present a novel approach to the automatic stylization of vector animation with the appearance of artistic media, primarily focused on, but not limited to, watercolor. The main goal is to preserve the visual characteristics of the given media’s dynamic behavior as much as possible. We provide a quick overview of previously published methods for style transfer and artistic stylization of still images and video. Subsequently, we describe the patch-based approach to example-based stylization in detail. We also focus on several important concepts related to this approach and solutions to common problems. Finally, we formulate our approach to the given task based on those concepts and evaluate its results. Details of our implementation and its optimization are included. We conclude that despite several shortcomings also mentioned in this thesis, our method produces compelling results in line with the set goal.
Variable tree rooting strategies are key to model distribution , 1 productivity and evapotranspiration of tropical evergreen forests 2

1 Potsdam Institute for Climate Impact Research, Potsdam, 14473, Germany 6 Universidad de Buenos Aires Consejo Nacional de Investigaciones Científicas y Técnicas, Centro de Investigaciones del 7 Mar y la Atmósfera (CIMA/UBA-CONICET), Buenos Aires, Argentina. 8 Institut Franco-Argentin d’Etudes sur le Climat et ses Impacts, Unité Mixte Internationale (UMI-IFAECI/CNRS9 CONICET-UBA), Argentina 10 Czech University of Life Sciences Prague, Department of Water Resources and Environmental Modeling, 165 00 Praha 6 – 11 Suchdol, Czech Republic 12 Palacký University Olomouc, Department of Ecology and Environmental Sciences, 78371 Olomouc, Czech Republic 13 University of Potsdam, Potsdam, 14469, Germany 14 Federal University of Santa Catarina (UFSC), Campus Universitário Reitor João David Ferreira Lima Trindade – 15 Florianópolis – SC, CEP: 88040-900, Santa Catarina, Brazil 16 University of Campinas (UNICAMP) Cidade Universitária "Zeferino Vaz" CEP 13083-970, Campinas-SP, Sao Paulo, 17 Brazil 18 Correspondence to: Boris Sakschewski (boris.sakschewski@pik-potsdam.de) 19 20 Abstract. A variety of modelling studies have suggested tree rooting depth as a key variable to explain evapotranspiration 21
CS265/CME309: Randomized Algorithms and Probabilistic Analysis Lecture #18: A taste of pseudorandomness: extractors and expanders

A pseudorandom object, informally, is a deterministic (or not-completely-random) object that behaves like a random one. Pseudorandom objects are useful for, among other things, derandomization. For this whole class, we’ve seen lots of examples of randomized algorithms. But there are many reasons that we might prefer a deterministic algorithm. First, maybe we would rather not leave things to chance. Second, maybe our algorithm has good guarantees when it gets to use uniformly random bits, but where do those bits come from? Unless we’ve got something quantum going on, we probably don’t have access to truly uniformly random bits. Thus, it’s often of interest to derandomize randomized algorithms. That is, we would like to adapt our randomized algorithms to use less randomness.1 In this lecture, we’ll see two pseudorandom objects that can be used to “derandomize” randomized algorithms, extractors and expanders. One could teach a whole course about these objects, and we only have one lecture. So we’re just going to scratch the surface, to give you a flavor of what this area is like. (Check out CS352 for more! )
Gas-particle partitioning of polyol tracers in the western Yangtze 1 River Delta , China : Absorptive or Henry ’ s law partitioning ? 2 3

Gas-particle partitioning of polyol tracers in the western Yangtze 1 River Delta, China: Absorptive or Henry’s law partitioning? 2 3 Chao Qin, Yafeng Gou, Yuhang Wang, Yuhao Mao, Hong Liao, Qin’geng Wang, 4 Mingjie Xie 5 6 7 a Colleges of Resources and Environmental Sciences, Nanjing Agricultural University, 8 Nanjing 210095, China 9 b Collaborative Innovation Center of Atmospheric Environment and Equipment 10 Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and 11 Pollution Control, School of Environmental Science and Engineering, Nanjing 12 University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, 13 China 14 c School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, 15 GA 30332 16 d State Key Laboratory of Pollution Control and Resources Reuse, School of the 17 Environment, Nanjing University, Nanjing 210023, China 18 19
Thyroid Hormone Receptor Beta as Tumor Suppressor: Untapped Potential in Treatment and Diagnostics in Solid Tumors

Simple Summary Dysregulation of the thyroid hormone receptor beta (TRβ) is characteristic of many solid and endocrine-related tumors. Despite a recognized role as a tumor suppressor, the mechanisms by which TRβ regulates tumor growth are not yet clear. As a transcription factor that responds to changes in thyroid hormone levels, TRβ plays a key role in regulating many cell signaling nodes that are important for maintenance of normal cell identity and tumor progression. This review will address the need for a deeper understanding of TRβ tumor suppressor mechanisms to inform the development of more effective thyroid cancer diagnostics and therapies. Abstract There is compelling evidence that the nuclear receptor TRβ, a member of the thyroid hormone receptor (TR) family, is a tumor suppressor in thyroid, breast, and other solid tumors. Cell-based and animal studies reveal that the liganded TRβ induces apoptosis, reduces an aggressive phenotype, decreases stem cell populations, and slows tumor growth through modulation of a complex interplay of transcriptional networks. TRβ-driven tumor suppressive transcriptomic signatures include repression of known drivers of proliferation such as PI3K/Akt pathway, activation of novel signaling such as JAK1/STAT1, and metabolic reprogramming in both thyroid and breast cancers. The presence of TRβ is also correlated with a positive prognosis and response to therapeutics in BRCA+ and triple-negative breast cancers, respectively. Ligand activation of TRβ enhances sensitivity to chemotherapeutics. TRβ co-regulators and bromodomain-containing chromatin remodeling proteins are emergent therapeutic targets. This review considers TRβ as a potential biomolecular diagnostic and therapeutic target.
A Mathematical Program for Scheduling Preventive Maintenance of Cogeneration Plants with Production

This paper considers the scheduling of preventive maintenance for the boilers, turbines, and distillers of power plants that produce electricity and desalinated water. It models the problem as a mathematical program (MP) that maximizes the sum of the minimal ratios of production to the demand of electricity and water during a planning time horizon. This objective encourages the plants’ production and enhances the chances of meeting consumers’ needs. It reduces the chance of power cuts and water shortages that may be caused by emergency disruptions of equipment on the network. To assess its performance and effectiveness, we test the MP on a real system consisting of 32 units and generate a preventive maintenance schedule for a time horizon of 52 weeks (one year). The generated schedule outperforms the schedule established by experts of the water plant; it induces, respectively, 16% and 12% increases in the surpluses while either matching or surpassing the total production. The sensitivity analysis further indicates that the generated schedule can handle unforeseen longer maintenance periods as well as a 120% increase in demand—a sizable realization in a country that heavily relies on electricity to acclimate to the harsh weather conditions. In addition, it suggests the robustness of the schedules with respect to increased demand. In summary, the MP model yields optimal systematic sustainable schedules.
Project 2 —— Machine Learning in Chemistry

—The dramatic development of machine learning has led to its widespread use in many ﬁelds, including the ﬁeld of chemistry. A widely used molecular representation in quantum chemistry, physics-based molecular representation, suffers from the problem of redundant vectors with a large number of features. In this project, seven feature selection algorithms are applied to reduce the dimensionality of a molecular representation dataset with a huge number of features, and the prediction accuracy is compared.
Freezing of gait in Parkinson’s disease: automatic early recognition of episodes from patients’ inertial data

Freezing of gait (FoG) in Parkinson’s disease is “a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk” [79]. It is a common symptom in 50-80% of the patients [79], occurring most frequently during turning, when passing through narrow paths or overcoming an obstacle, and is strongly affected by cognitive aspects such as attention, stress or anxiety [61]. Having a strong correlation with falls [47], FoG is considered as one of the most dangerous symptoms, with severe effects on autonomy and Quality of Life (QoL). Due to the great variability of FoG features, treatments have to be patient-specific. This aspect, together with the need of further investigating its causes, requires to develop objective methodologies to assess FoG. Laboratory assessment typically includes FoG eliciting in an artificial setting, with an altered emotional state of the patient due to examination anxiety. Thus many literature studies have been recently focused on home evaluation systems [8]. In this regard, a popular option is based on a combination of data coming from wearable inertial sensors with machine learning algorithms, trained in order to detect FoG events. Although different, all these methodologies share a similar processing pipeline. The aim of this study was to explore a new approach to the problem, performing additional offline preprocessing to identify “regions of interest” in the data, where there is a higher probability to identify FoG. Such step was introduced to increase time efficiency during the analysis and the precision of the final classifier predictions. In this study, tri-axial acceleration data coming from two previous experiments (defined as “Phase 1” and “Phase 2”), involving a total of 85 subjects and a waist-mounted commercial smartphone for data collection, were employed. Phase 1 data were used to design the algorithm for the selection of ”region of interest”, based on Continuous Wavelet Transform (CWT). On the selected pieces, a more traditional pipeline was implemented: the signals were segmented through a sliding window; from each segment a set of relevant spectral and temporal features was extracted and fed in input to several classifiers (K-NN, SVM, Random Forest) to compare their performances. Both multiclass and binary classification were explored. The best results were obtained using binary Support Vector Machine with RBF kernel, achieving accuracy, recall and precision of 95%, 84%, 90% respectively in a 10-fold stratified crossvalidation. Furthermore, the use of the implemented window of interest was found to increase specificity, in a false positive test over non-freezer patients, by 35 percentage points (from 62% to 97%).