Miracle-dz/UniFAR-FTUs huggingface dataset:
GFDM - A Framework for Virtual PHY Services in 5G Networks

The next generation of wireless networks will face different challenges from new scenarios. The main contribution of this paper is to show that Generalized Frequency Division Multiplexing (GFDM), as a baseline of flexible circular filtered multicarrier systems, can be used as a framework to virtualize the PHY service for the upper layers of 5G networks. This framework opens the possibility to apply software-defined network principles to produce software-defined waveforms capable of addressing the requirements of future mobile networks. Hence, a block oriented concept will be used to provide the modulation service, emulating different flavors of waveforms designed to go beyond the well-established Orthogonal Frequency Division Multiplexing (OFDM) principles, in scenarios where they perform best. The virtual physical layer (PHY) service opens the opportunity to have a fast and dynamic evolution of the infrastructure, as applications change over time. The presented unified modulation concept contributes with future research directions to address burst and continuous transmissions, referencing basic approaches for synchronization and advanced receiver design that can be exploited in future for the whole frame structure design and channel estimation strategies.
DoodleDebug, Objects Should Sketch Themselves For Code Understanding

Developers override toString() and printOn: methods to allow objects to display themselves. This is done to track object state while debugging. Although very popular, the technique breaks down when displaying complex, multi-dimensional objects. We propose an approach in which objects have two-dimensional visualizations at various levels of granularity. This makes it easier to compose visualizations from object parts, and enables “semantic zooming” of object visualizations while debugging. We have carried out an empirical study to understand how printOn: methods are used in practice, and we are developing DoodleDebug, a framework to support visualizable objects.
Financial Derivatives and Partial Differential Equations

1. ASSETS AND DERIVATIVES. Assets of all sorts are traded in financial markets: stocks and stock indices, foreign currencies, loan contracts with various interest rates, energy in many forms, agricultural products, precious metals, etc. The prices of these assets fluctuate, sometimes wildly. As an example, Figure 1 shows the price of IBM stock within a single day. The picture would look more or less the same across a month, a year, or a decade, though the axis scales would be different.
Observations of three pre-cataclysmic variables from the Edinburgh-Cape blue object survey

Aims: This study aims at determining the parameters of the three candidate pre-cataclysmic binaries EC 12477–1738, EC 13349–3237, and EC 14329–1625, most importantly their orbital period. Methods: Time-series photometry reveals orbital modulation in the form of sinusoidal variation due to the reflection effect. Photometric observations are complemented by time-resolved spectroscopy that yields radial velocities of the H$\alpha$ emission line. The combination of both methods allows us to unambiguously determine the orbital periods. The average spectra are used to estimate physical parameters of the primary and secondary stellar components. Results: We determine the orbital period for EC 12477–1738 as 0.362 d, thus confirming the value previously reported. A similar period, P=0.350 d, is found for EC 14329–1625. Both systems incorporate a medium-hot white dwarf ( $T = 15\,000{-}20\,000$ K) and an M3V secondary star. The third pre-CV, EC 13349–3237, is the youngest of the three, with a hot WD ( $T \sim 35\,000$ K), and it also has the longest period P=0.469 d. It furthermore turns out to be one of the still rare pre-CVs with a comparatively early-type, M1V, secondary star, which will eventually evolve into a CV above the period gap.
Position Aware Topic Modelling of Text

Keywords: natural language processing, topic modelling, hidden Markov models Modern text processing algorithms typically model documents as a bag of words or with hidden Markov chains. Intuitively, the bag of words model loses valuable information by ignoring the position of words in a document. Specifically, words in the same sentence or paragraph are more closely related than are two random words appearing in the same document. Further, paragraphs and documents often have complex structures where the beginning sets a topic, a digression occurs in the middle, and the end relates the digression to the topic. This aspect is not captured by hidden Markov models, which assume locality of related text. Finally, position indicates importance i.e. the opening and concluding paragraphs of an essay are usually the most important understanding the document as a whole. I hypothesize that machine learning can model how and where position indicates relatedness and importance of text, ultimately leading to improved topic models. Topic modelling is a popular and useful technique for automatically identifying the subjects of large numbers of documents. For example, it has been successfully applied to group news articles [2] and to search for academic papers [6]. As the quantity of documents available increases, especially on the Internet, it is crucial for machines to assist users in categorizing and finding information. Doing so vastly improves the ability of people to discover and process relevant information. As search engines have shown, public adoption of such algorithms depends crucially on the quality of results, which this project aims to improve. In the literature, topic models such as probabilistic latent semantic analysis (pLSA) [6] and latent Dirichlet allocation (LDA) [2] are usually introduced with a bag of words model that simply counts word occurrences in documents. However, other work [5] illustrates the importance of position to the related problem of segmenting documents based on topic. The very notion of segmenting documents by topic suggests that topic models could be improved by accounting for the positions of words. Indeed, both pLSA [3] and LDA [8] were later extended with a Markov model for topic transitions. Learning cue words for topic transitions has also been tried [1] successfully. I believe that natural language topic structures are more complex than captured by these models and propose a plan to make a better one. Making a new model proceeds in several steps: looking at the data available, developing an intuition …
The Lefff, a Freely Available and Large-coverage Morphological and Syntactic Lexicon for French

In this paper, we introduce the Lefff, a freely available, accurate and large-coverage morphological and syntactic lexicon for French, used in many NLP tools such as large-coverage parsers. We first describe Alexina, the lexical framework in which the Lefff is developed as well as the linguistic notions and formalisms it is based on. Next, we describe the various sources of lexical data we used for building the Lefff, in particular semi-automatic lexical development techniques and conversion and merging of existing resources. Finally, we illustrate the coverage and precision of the resource by comparing it with other resources and by assessing its impact in various NLP tools.
An Improved Approach to Term Weighting in Hierarchical Web Page Classification

Currently, in web page classification, Absolute Weighting Method is a common method to weight HTML main structure features. The disadvantage of the method is that weighting coefficient is a fixed value, which has different effects on the long and short text. So the influence of structure features on local text will be weakened with the length of local text increasing. To solve the problem, we propose an improved weighting method, namely Relative Weighting Method. In the experiment of web page hierarchical classification, we compare the two methods' classification performance on a single label and several labels combination. The results show that Relative Weighting Method can effectively improve the classification accuracy, which is better than the Absolute Weighting Method.
An Online Badging System Supporting Educators' STEM Learning

paper, we investigate how a digital badging system was used as part of an informal, not-for-credit professional development project. Teacher Learning Journeys was designed for personalized science learning for educators in K-12 schools, museums, universities, and teaching colleges through employing two levels of micro-credentials: lower achievement digital stamps and higher achievement digital badges. Researchers conducted a qualitative collective case study centered on 36 teachers; the primary data were records from learners' interactions within the digital badge system; secondary data came from a survey at the end of the experience and two interviews with 11 focal teachers. Findings suggest the following design principles: (a) two levels of assessment can support personalized learning, (b) mastery of learning can be demonstrated and assessed through reflective logs, (c) collaboration during and after badging activities can provide value to the learners, and (d) establishment of relevance of badging experiences can support the application of content outside the badging system.
An Efficient Approach to Event Detection and Forecasting in Dynamic Multivariate Social Media Networks

Anomalous subgraph detection has been successfully applied to event detection in social media. However, the subgraph detection problembecomes challenging when the social media network incorporates abundant attributes, which leads to a multivariate network. The multivariate characteristic makes most existing methods incapable to tackle this problem effectively and efficiently, as it involves joint feature selection and subgraph detection that has not been well addressed in the current literature, especially, in the dynamic multivariate networks in which attributes evolve over time. This paper presents a generic framework, namely dynamic multivariate evolving anomalous subgraphs scanning (DMGraphScan), to addressthis problem in dynamic multivariate social media networks. We generalize traditional nonparametric statistics, and propose a new class of scan statistic functions for measuring the joint significance of evolving subgraphs and subsets of attributes to indicate the ongoing or forthcoming event in dynamic multivariate networks. We reformulate each scan statistic function as a sequence of subproblems with provable guarantees, and then propose an efficient approximation algorithm for tackling each subproblem. This algorithm resorts to the Lagrangian relaxation and a dynamic programming based on tree-shaped priors. As a case study, we conduct extensive experiments to demonstrate the performance of our proposed approach on two real-world applications (flu outbreak detection, haze detection) in different domains.
Private Learning on Networks

Continual data collection and widespread deployment of machine learning algorithms, particularly the distributed variants, have raised new privacy challenges. In a distributed machine learning scenario, the dataset is stored among several machines and they solve a distributed optimization problem to collectively learn the underlying model. We present a secure multi-party computation inspired privacy preserving distributed algorithm for optimizing a convex function consisting of several possibly non-convex functions. Each individual objective function is privately stored with an agent while the agents communicate model parameters with neighbor machines connected in a network. We show that our algorithm can correctly optimize the overall objective function and learn the underlying model accurately. We further prove that under a vertex connectivity condition on the topology, our algorithm preserves privacy of individual objective functions. We establish limits on the what a coalition of adversaries can learn by observing the messages and states shared over a network.
Auditory Verbal Hallucination and the Sense of Ownership

Abstract:About 75% of subjects diagnosed with schizophrenia experience auditory-verbal hallucination (AVH) and report "hearing voices" that are not actually present. Wu maintains that AVHs should be understood as aberrant auditory experiences. However, his account is unable to make sense of a full range of AVH experiences and mistakenly treats AVH as an isolated symptom. Because AVH is phenomenologically heterogeneous in certain respects, an adequate explanation of AVH needs to take this diversity of experience into account. Still, it is important to understand what these different sorts of AVHs have in common with each other, and also how they are linked to some of the other characteristic symptoms of schizophrenia. I maintain that AVHs result from an underlying disruption to self-consciousness, namely, a diminished sense of ownership. This diminished sense of ownership, in turn, is caused by a breakdown in selective attention.
The Neurocognitive Components of Pitch Processing: Insights from Absolute Pitch

The natural variability of pitch naming ability in the population (known as absolute pitch or AP) provides an ideal method for investigating individual differences in pitch processing and auditory knowledge formation and representation. We have demonstrated the involvement of different cognitive processes in AP ability that reflects varying skill expertise in the presence of similar early age of onset of music tuition. These processes were related to different regions of brain activity, including those involved in pitch working memory (right prefrontal cortex) and the long-term representation of pitch (superior temporal gyrus). They reflected expertise through the use of context dependent pitch cues and the level of automaticity of pitch naming. They impart functional significance to structural asymmetry differences in the planum temporale of musicians and establish a neurobiological basis for an AP template. More generally, they indicate variability of knowledge representation in the presence of environmental fostering of early cognitive development that translates to differences in cognitive ability.
Domain Specific Facts Extraction Using Weakly Supervised Active Learning Approach

An ontology is defined using concepts and relationships between the concepts. In this paper, we focus on second problem: relation extraction from plain text. Generic Knowledge Bases like YAGO, Freebase, and DBPedia have made accessible huge collections of facts and their properties from various domains. But acquiring and maintaining various facts and their relations from domain specific corpus becomes very important and challenging task due to low availability of annotated data. Here, we proposed a label propagation based semi-supervised approach for relation extraction by choosing most informative instances for annotation. We also proposed weakly supervised approach for data annotation using generic ontologies like Freebase, which further reduces the cost of annotating data manually. We checked efficiency of our approach by performing experiments on various domain specific corpora.
Logging versus Soft Updates: Asynchronous Meta-data Protection in File Systems

The UNIX Fast File System (FFS) is probably the most widely-used ﬁle system for performance comparisons. However, such comparisons frequently overlook many of the performance enhancements that have been added over the past decade. In this paper, we explore the two most commonly used approaches for improving the performance of meta-data operations and recovery: logging and Soft Updates. The commercial sector has moved en masse to logging ﬁle systems, as evidenced by their presence on nearly every server platform available today: Solaris, AIX, Digital UNIX, HP-UX, Irix, and Windows NT. On all but Solaris, the default ﬁle system uses logging. In the meantime, Soft Updates holds the promise of providing stronger reliability guarantees than logging, with faster recovery and superior performance in certain boundary cases. In this paper, we explore the beneﬁts of both Soft Updates and logging, comparing their behavior on both microbenchmarks and workload-based macrobenchmarks. We ﬁnd that logging alone is not sufﬁcient to “solve” the meta-data update problem. If synchronous semantics are required (i.e., meta-data operations are durable once the system call returns), then the logging systems cannot realize their full potential. Only when this synchronicity requirement is relaxed can logging systems approach the performance of systems like Soft Updates. Our asynchronous logging and Soft Updates systems perform comparably in most cases. While Soft Updates excels in some meta-data intensive microbenchmarks, it outperforms logging on only two of the four workloads we examined and performs less well on one.
Financial Crises , Risk Premia , and the Term Structure of Risky Assets ∗

The literature on rare disasters shows that low probability events can explain high, time-varying risk premia. I find that large spikes in risk premia occur around financial crises but not around other disasters such as wars. A model with financial intermediaries generates endogenous financial crises that quantitatively match those in the data, while also replicating high equity risk premia and volatility. Compared to a standard disasters framework, the model makes additional empirical predictions which I confirm in the data. First, the equity of the intermediary sector strongly forecasts stock returns. Second, financial crises are temporary, which implies that the term structure of risky assets is downward sloping during financial crises when risk premia are concentrated in the near term. The model explains the level and slope of the term structure of risky assets including equities, corporate bonds, and VIX, both unconditionally and in a crisis. I then use the term structure of risky assets to infer the daily probability and persistence of a financial crisis in real time, providing a useful tool to analyze policy responses in a crisis. ∗Kellogg School of Management, Department of Finance. I would especially like to thank my committee, Arvind Krishnamurthy, Andrea Eisfeldt, Dimitris Papanikolaou, and Ravi Jagannathan. I would also like to thank Jason Chen, Anna Cieslak, Rob Dam, Jules van Binsbergen, Jonathan Parker, Victor Todorov, Torben Andersen, Nicola Fusari, Arik Bendor, Annette Vissing-Jorgensen, and seminar participants at Kellogg. I thank Barclays and Moody’s Analytics for providing data on credit spreads, returns, and default probabilities. All errors are mine. The most recent version of this paper can be found at www.kellogg. northwestern.edu/faculty/muir/Muir_JMP. pdf .
Modeling of the Human Alveolar Rhabdomyosarcoma Pax3-Foxo1 Chromosome Translocation in Mouse Myoblasts Using CRISPR-Cas9 Nuclease

Many recurrent chromosome translocations in cancer result in the generation of fusion genes that are directly implicated in the tumorigenic process. Precise modeling of the effects of cancer fusion genes in mice has been inaccurate, as constructs of fusion genes often completely or partially lack the correct regulatory sequences. The reciprocal t(2;13)(q36.1;q14.1) in human alveolar rhabdomyosarcoma (A-RMS) creates a pathognomonic PAX3-FOXO1 fusion gene. In vivo mimicking of this translocation in mice is complicated by the fact that Pax3 and Foxo1 are in opposite orientation on their respective chromosomes, precluding formation of a functional Pax3-Foxo1 fusion via a simple translocation. To circumvent this problem, we irreversibly inverted the orientation of a 4.9 Mb syntenic fragment on chromosome 3, encompassing Foxo1, by using Cre-mediated recombination of two pairs of unrelated oppositely oriented LoxP sites situated at the borders of the syntenic region. We tested if spatial proximity of the Pax3 and Foxo1 loci in myoblasts of mice homozygous for the inversion facilitated Pax3-Foxo1 fusion gene formation upon induction of targeted CRISPR-Cas9 nuclease-induced DNA double strand breaks in Pax3 and Foxo1. Fluorescent in situ hybridization indicated that fore limb myoblasts show a higher frequency of Pax3/Foxo1 co-localization than hind limb myoblasts. Indeed, more fusion genes were generated in fore limb myoblasts via a reciprocal t(1;3), which expressed correctly spliced Pax3-Foxo1 mRNA encoding Pax3-Foxo1 fusion protein. We conclude that locus proximity facilitates chromosome translocation upon induction of DNA double strand breaks. Given that the Pax3-Foxo1 fusion gene will contain all the regulatory sequences necessary for precise regulation of its expression, we propose that CRISPR-Cas9 provides a novel means to faithfully model human diseases caused by chromosome translocation in mice.
Genetic Diversity in Barley and Wheat for Tolerance to Soil Constraints

Surface soil sodicity as well as subsoil salinity, acidity, and phytotoxic concentrations of chloride (Cl) are major soil constraints to crop production in many soils of sub-tropical, north-eastern Australia. The identification of genotypes tolerant to these soil constraints may be an option to maintain and improve productivity on these soils. We evaluated performance of 11 barley and 17 wheat genotypes grown on two sites 800 mg·Cl·kg−1) in the subsoil (0.9–1.3 m soil depth) and higher exchangeable sodium percentage (ESP) (>6%) in the surface and subsoil. Barley grain yield and plant available water capacity (PAWC) were reduced between 5%–25% and 40%–66%, respectively, for different genotypes at the sodic site as compared to the non-sodic site. For wheat genotypes, grain yield was between 8% and 33% lower at the sodic site compared to the non-sodic site and PAWC was between 3% and 37% lower. Most barley and wheat genotypes grown at the sodic site showed calcium (Ca) deficiency symptoms on younger leaves. Analysis of the youngest fully mature leaf (YML) confirmed that genotypes grown at the sodic site with Ca concentration < 0.2% exhibited deficiency symptoms. Grain yields of both barley and wheat genotypes grown on the sodic and non-sodic sites increased significantly with increasing Ca and K in YML and decreased significantly with increasing Na and Cl concentrations in YML. Sodium (Na) concentrations in YML of wheat genotypes grown at the sodic site were 10-fold higher than those from the non-sodic site whereas this increase was only two-fold in barley genotypes. In step-wise regression, the PAWC of barley and wheat genotypes grown on sodic and non-sodic sites was the principal determinant of variability of barley and wheat grain yield. Including the Ca concentration in the YML of wheat genotypes and K:Na ratio in the YML of barley genotypes significantly improved the prediction of grain yield in the regression analysis. Barley genotypes, Mackay and Kaputar, were relatively susceptible while Baronesse and Grout were relatively more tolerant to sodicity. Wheat genotypes Gregory and Stampede were generally relatively more susceptible to sodicity, and genotypes Baxter, Hume, and the experimental line HSF1-255 were relatively more tolerant than the former group.