Miracle-dz/UniFAR-FTUs huggingface dataset:
Modern computer algebra

Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the 'bible of computer algebra', gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany one- or two-semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, cryptography, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text. In this third edition, errors have been corrected and much of the Fast Euclidean Algorithm chapter has been renovated.
An Expectation-Maximization Algorithm for the Matrix Normal Distribution

Dramatic increases in the size and dimensionality of many recent data sets make crucial the need for sophisticated methods that can exploit inherent structure and handle missing values. In this article we derive an expectation-maximization (EM) algorithm for the matrix normal distribution, a distribution well-suited for naturally structured data such as spatio-temporal data. We review previously established maximum likelihood matrix normal estimates, and then consider the situation involving missing data. We apply our EM method in a simulation study exploring errors across different dimensions and proportions of missing data. We compare these errors and computational running times to those from two alternative methods. Finally, we implement the proposed EM method on a satellite image dataset to investigate land-cover classification separability.
Pancreatic adenocarcinoma up-regulated factor as a target for the treatment of serous ovarian adenocarcinoma

Ovarian cancer is the deadliest gynecological cancer and when diagnosed at advanced stages the five-year survival rate is typically less than 30%. Pancreatic adenocarcinoma up-regulated factor (PAUF) plays a crucial role in tumor progression, angiogenesis, and immune evasion in the pancreatic tumor microenvironment. The current study aimed to characterize the potential tumorigenic role of PAUF in serous ovarian cancer as high expression of the factor is correlated with poor prognosis in epithelial ovarian cancer. Extracellular treatment with recombinant PAUF activated intracellular signal pathways that subsequently led to enhanced proliferation, migration, and invasion in various serous ovarian adenocarcinoma cell lines. A PAUF-knockout OVCAR-5 cell line demonstrated an apparent reduction in tumor incidence and delayed tumor growth in mouse xenograft experiments. Furthermore, the administration of an anti-PAUF antibody exhibited a notable therapeutic efficacy in mice bearing the OVCAR-5 cell line-derived xenograft tumors. Taken together, our results suggest that PAUF plays a role in the progression of serous ovarian adenocarcinoma and may serve as a novel therapeutic target of the disease.
Probabilistic Attention-to-Influence Neural Models for Event Sequences

Discovering knowledge about which types of events influence others, using datasets of event sequences without time stamps, has several practical applications. While neural sequence models are able to capture complex and potentially long-range historical dependencies, they often lack the interpretability of simpler models for event sequence dynamics. We provide a novel neural framework in such a setting – a probabilistic attention-to-influence neural model – which not only captures complex instance-wise interactions between events but also learns influencers for each event type of interest. Given event sequence data and a prior distribution on type-wise influence, we efficiently learn an approximate posterior for type-wise influence by an attention-to-influence transformation using variational inference. Our method subsequently models the conditional likelihood of sequences by sampling the above posterior to focus attention on influencing event types. We motivate our general framework and show improved performance in experiments compared to existing baselines on synthetic data as well as real-world benchmarks, for tasks involving prediction and influencing set identification.
Towards Robust and Safe Reinforcement Learning with Benign Off-policy Data

Previous work demonstrates that the optimal safe reinforcement learning policy in a noise-free environment is vulnerable and could be un-safe under observational attacks. While adversarial training effectively improves robustness and safety, collecting samples by attacking the behavior agent online could be expensive or prohibitively dangerous in many applications. We propose the robuSt vAriational ofF-policy lEaRning (SAFER) approach, which only requires benign training data without at-tacking the agent. SAFER obtains an optimal non-parametric variational policy distribution via convex optimization and then uses it to improve the parameterized policy robustly via supervised learning. The two-stage policy optimization facilitates robust training, and extensive experiments on multiple robot platforms show the efficiency of SAFER in learning a robust and safe policy: achieving the same reward with much fewer constraint violations during training than on-policy baselines.
ESTIMASI PERMEABILITAS DENGAN BEBERAPA METODE KARAKTERISASI RESERVOIR UNTUK FORMASI TALANGAKAR

Permeabilitas merupakan salat satu properti reservoir yang sangat penting. Data permeabilitas dapat diperoleh secara langsung dengan pengamatan core di laboratorium dan secara tidak langsung dengan berdasarkan estimasi yang akan dikaji pada kegiatan ini. Kajian dilakukan dengan membandingkan beberapa metode karakterisasi reservoir yaitu metode fuzzy logic , FZI ( Flow Zone Indicator ) dan PGS ( Pore Geometry Structure ) pada satu lapangan dengan terlebih dahulu melakukan estimasi permeabilitas pada uncored zone dan/atau uncored wells pada reservoir studi yang kemudian digunakan dalam pemodelan.
Importance of Winds and Soil Moistures to the US Summertime Drought of 1988: A GCM Simulation Study

The climate version of NASA's GEOS 2 GCM did not simulate a realistic 1988 summertime drought in the central United States (Mocko et al., 1999). Despite several new upgrades to the model's parameterizations, as well as finer grid spacing from 4x5 degrees to 2x2.5 degrees, no significant improvements were noted in the model's simulation of the U.S. drought.
High precision inter-continental GPS network

GPS relative positioning provides precision of the order of 1 part per million (ppm) within relatively short periods of time. Considering this level of precision, together with the fact that the cost of GPS receivers is continuing come down, it is now apparent that most of the future geodetic surveys will be performed by GPS, It is therefore important to establish geodetic control networks which are suitable for geodetic GPS activities. The Brazilian Institute of Geography and Statistics (IBGE), which is the National Geodetic Surveying organization in Brazil, has proposed a high accuracy Brazilian GPS network, which has characteristics of an Active Control System (ACS). Such a system provides the users with the capability to perform relative positioning using only one receiver. An investigation of the methodology, algorithms and analysis, related to the IBGE network, has been carried out during this research. An initial investigation to assess the level of accuracy which can be obtained by a static user of the IBGE network equipped with only one receiver and observing within different scenarios, has shown relative precision in the range of 0.2 to 0.1 ppm. GPS positioning requires a global coordinate reference frame. The integration of a GPS network into such a global frame requires stations already connected to the global reference frame to be observed simultaneously with the new network stations. The processing is then carried out involving the network stations together with those already connected to the global reference frame. The reeent1y created International GPS Geodynamics Service (IGS) provides the capability for such easy integration. GPS data and ephemerides generated from the IGS Epoch '92 Campaign have been used in the processing carried out to integrate the Brazilian GPS network into the IERS Terrestrial Reference Frame 1993 (ITRF93). This data processing involved very large network, and the GPS processing software had to be expanded in order to provide higher accuracies. The results demonstrated repeatability of the order of 20 mm, for baseline lengths of up to 8200 km. The expansion of the software mentioned above provided capability of processing very large or even global GPS networks. It allowed the investigation of several aspects related to global GPS, namely, free adjustment, the application of loose constraints to the parameters and the use of internal constraints in the covariance- matrix. The use of GPS to realize a global reference frame has also been tested. In order to investigate all these aspects, an inter-continental network involving the Brazilian and IGS stations was used. Results have shown a level of agreement after the transformation between the free network reference frame and the ITRF93 coordinates of the order of 6.4 mm.
Appendix for Cross-Domain Image Captioning with Discriminative Finetuning

We chose the stimuli for the human annotation experiment as follows. We iterated over the images in our Conceptual Captions test set, and sampled, for each image, the 9 closest neighbours, thus creating sets composed of 10 images: 1 target and 9 distractors. We set a threshold of maximum cosine similarity to the nearest neighbour of 0.8. We decided on this threshold after visual inspection of the generated sets: we aimed at having sets challenging for the annotators, yet not impossible to solve, and higher thresholds could lead to sets containing almost identical images, such as subsequent frames extracted from the same video or different croppings of the same picture. Neither targets nor distractors were repeated in the sets and we manually excluded disturbing images. In the human retrieval experiment, we annotated each set with 3 types of captions: human captions, or captions generated by DiscriTune(-ConCap) or ClipCap(-ConCap), respectively. We randomly divided the entire set into blocks of 100 questions containing mixed caption types. On each screen, the 10 images from a set were presented at the center, arranged in two arrays of 5 images, with the caption written above–see Figure 1. Participants were asked to click on the image that matched the caption best. They were shown one example before starting the task, and were also warned that some cases could contain automaticallygenerated captions: we asked them to always reply with the answer they found most plausible. Finally, they were warned that the experiment contained some control items, used to ensure annotation quality. Each subject was presented with one block of questions, plus 5 randomly placed controls, designed to ensure that annotators were paying attention to the task. These cases were made intentionally very simple: targets were surrounded by random distractors not appearing in the other sets, and the associated caption was a human generated one. We made sure internally that these sets could be easily processed with 100% retrieval accuracy. The data collection routine was written in Psychopy [7] and launched through Pavlovia. There was no time limit for completing the study. We recruited participants via
How Embedded Lead Users Contribute to Innovation Processes: A Systematic Literature Review and Conceptual Framework

Embedded lead users (ELUs) are company employees who are also users of the company products, most often holding this role due to their active participation in the user domain in their spare time. ELUs are able to integrate knowledge of customer needs in the innovation process based on their social contacts, user experiences, and knowledge of the innovating network or organization, for example. Due to this ability, ELUs can facilitate innovation processes. The aim of this systematic literature review is to examine how ELUs can contribute to different stages of the company innovation proces s. The literature used in the review consisted of peer reviewed academic journals and conference proceedings after the year 2000. Broader literature of external lead user collaboration was not included, as the focus was on narrower discussion of embedded lead users. Three research questions are answered to form a conceptual synthesis of ELU literature 1) What kind of resources can ELUs offer for the innovation process? 2) How and based on which resources do ELUs contribute to the different stages of the innovation process (opportunity identification and idea formation, design and development, and testing and launching)? 3) What kinds of roles do ELUs have and how does their dual role in the user domain and the company influence the innovation process? Findings of the study bring new managerial perspectives on how companies can utilize ELUs in their innovation practices. In addition, by forming a holistic conceptual framework of current ELU research and the role of ELUs in the innovation process, this paper offers a variety of theoretical contributions and ground for future studies.
The unsupervised learning of natural language structure

There is precisely one complete language processing system to date: the human brain. Though there is debate on how much built-in bias human learners might have, we definitely acquire language in a primarily unsupervised fashion. On the other hand, computational approaches to language processing are almost exclusively supervised, relying on hand-labeled corpora for training. This reliance is largely due to unsupervised approaches having repeatedly exhibited discouraging performance. In particular, the problem of learning syntax (grammar) from completely unannotated text has received a great deal of attention for well over a decade, with little in the way of positive results. We argue that previous methods for this task have generally underperformed because of the representations they used. Overly complex models are easily distracted by non-syntactic correlations (such as topical associations), while overly simple models aren't rich enough to capture important first-order properties of language (such as directionality, adjacency, and valence). In this work, we describe several syntactic representations and associated probabilistic models which are designed to capture the basic character of natural language syntax as directly as possible. First, we examine a nested, distributional method which induces bracketed tree structures. Second, we examine a dependency model which induces word-to-word dependency structures. Finally, we demonstrate that these two models perform better in combination than they do alone. With these representations, high-quality analyses can be learned from surprisingly little text, with no labeled examples, in several languages (we show experiments with English, German, and Chinese). Our results show above-baseline performance in unsupervised parsing in each of these languages. Grammar induction methods are useful since parsed corpora exist for only a small number of languages. More generally, most high-level NLP tasks, such as machine translation and question-answering, lack richly annotated corpora, making unsupervised methods extremely appealing even for common languages like English. Finally, while the models in this work are not intended to be cognitively plausible, their effectiveness can inform the investigation of what biases are or are not needed in the human acquisition of language.
Much Ado about Microbunching: Coherent Bunching in High Brightness Electron Beams

The push to provide ever brighter coherent radiation sources has led to the creation of correspondingly bright electron beams. With billions of electrons packed into normalized emittances (phase space) below one micron, collective effects may dominate both the preservation and use of such ultra-bright beams. An important class of collective effects is due to density modulations within the bunch, or microbunching. Microbunching may be deleterious, as in the case of the Microbunching Instability (MBI), or it may drive radiation sources of unprecedented intensity, as in the case of Free Electron Lasers (FELs). In this work we begin by describing models of microbunching due to inherent beam shot noise, which sparks both the MBI as well as SLAC's Linac Coherent Light Source, the world's first hard X-ray laser. We first use this model to propose a mechanism for reducing the inherent beam shot noise as well as for predicting MBI effects. We then describe experimental measurements of the resulting microbunching at LCLS, including optical radiation from the MBI, as well as the first gain length and harmonic measurements from a hard X-ray FEL. In the final chapters, we describe schemes that use external laser modulations to microbunch light sources of themore » future. In these sections we describe coherent light source schemes for both both linacs and storage rings. « less
Heterosis of Fitness and Phenotypic Variance in the Evolution of Diploid Gene Regulatory Network

Heterosis describes the phenomenon whereby a hybrid population has higher fitness than an inbred population, and has previously been explained by either Mendelian dominance or overdominance, where it is generally assumed that one gene controls one trait. How-ever, recent studies have demonstrated that genes interact through a complex gene regulatory network (GRN). Furthermore, phenotypic variance due to noise is reportedly lower for heterozygotes, whereas the origin of such variance-related heterosis remains elusive. There-fore, a theoretical analysis linking heterosis to GRN evolution and stochastic gene expression dynamics is required. Here, we investigate heterosis related to fitness and phenotypic variance in a system with interacting genes, by numerically evolving diploid GRNs. According to the results, the heterozygote population exhibited higher fitness than the homozygote population, that is, fitness-related heterosis resulting from evolution. In addition, the heterozygote population expressed lower noise-related phenotypic variance in expression levels than the homozygous population, implying that the heterozygote population is more robust to noise. Furthermore, the distribution of the ratio of heterozygote phenotypic variance to homozygote phenotypic variance exhibited quantitative agreement with previous experimental results. By applying dominance and over-dominance to the gene expression pattern rather than only a single gene expression, we confirmed the correlation between heterosis and overdominance. We explain our results by proposing that the convex high-fitness region is evolutionarily shaped in the genetic space to gain noise robustness under genetic mixing through sexual reproduction. Significance Statement Heterosis, that is higher fitness in hybrid populations than inbred populations, is a long-standing problem in genetics, evolution, and breeding studies. Heterosis is not necessarily the result of a trait uniquely determined by a single gene, but likely involves stochasticity and interactions among genes. Through numerical evolution of the gene regulatory network, we demonstrate that heterosis is shaped by evolution to achieve robustness against noise and genetic mixing by sexual recombination in gene expression dynamics. That is, a mixed population is more robust to noise than inbred populations, as revealed experimentally by reduced phenotypic variance. The observed link between heterosis and phenotypic robustness, Mendelian dominance, and the convex single-humped fitness landscape represents a novel avenue in evolution and genetics research.
Within your rights: Dissociating wrongness and permissibility in moral judgement.

Are we ever morally permitted to do what is morally wrong? It seems intuitive that we are, but evidence for dissociations among judgement of permissibility and wrongness is relatively scarce. Across four experiments (N = 1438), we show that people judge that some behaviours can be morally wrong and permissible. The dissociations arise because these judgements track different morally relevant aspects of everyday moral encounters. Judgements of individual rights predicted permissibility but not wrongness, while character assessment predicted wrongness but not permissibility. These findings suggest a picture in which moral evaluation is granular enough to express reasoning about different types of normative considerations, notably the possibility that people can exercise their rights in morally problematic ways.
: Empowering Informed Choices in the Web

Individuals lack proper means to supervise the services they contact and the information they exchange when surﬁng the web. This security task has become challenging due to the complexity of the modern web, of the data delivering technology, and even to the adoption of encryption, which, while improving privacy, makes in-network services ineffective. The implications are serious, from a person contacting undesired services or unwillingly exposing private information, to a company being unable to control the ﬂow of its information to the outside world. To empower transparency and the capability of taking informed choices in the web, we propose C ROWD S URF , a system for comprehensive and collaborative auditing of data exchanged with Internet services. Similarly to crowdsourced efforts, we enable users to contribute in building awareness, supported by the semi-automatic analysis of data offered by a cloud-based system. The result is the creation of “suggestions” that individuals can transform in enforce-able “rules” to customize their web browsing policy. C ROWD S URF provides the core infrastructure to let individuals and enterprises regain visibility and control on their web activity. Preliminary results obtained executing a prototype implementation demonstrate the feasibility and potential of C ROWD S URF .
FLORISTIC COMPOSITION, STRUCTURE AND REGENERATION STATUS OF ACHERA NATURAL FOREST IN CHILGA DISTRICT, NORTHWEST ETHIOPIA

: This research focused on the floristic description and forest condition assessment comparisons of Achera Forest in Northwest Ethiopia. The floristic data were recorded from 20 x 20 m 30 sampling plots. Diameter at breast height, height and number of each species individuals were recorded from all plots. A total of 48 woody species, representing 41 genera and 25 families were recorded. The Fabaceae family was the richest (7 species) followed by Combretaceae (5 species) and Asclepiadaceae (4 species). Life-form composition was 60.87% trees, 9.57% shrubs, 17.37% tree/shrubs and 2.17% woody climbers. The overall Shannon diversity index H′ = 3.37 was high for the richness and evenness of the woody species. The low basal area (3.61 m 2 /ha) and population structure revealed the need for conservation measures for species with poor regeneration status. The floristic diversity revealed that the forest was a Combretum - Terminalia woodland type. There was a density decline in the higher diameter class suggesting the existence of disturbance and unsustainable exploitation of the forest. Based on the results of the study, comprehensive studies and sound management system are recommended.