Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Annotation in the LUNA Italian Corpus of Spontaneous Dialogues

In this paper we present an active approach to annotate with lexical and semantic labels an Italian corpus of conversational human-human and Wizard-of-Oz dialogues. This procedure consists in the use of a machine learner to assist human annotators in the labeling task. The computer assisted process engages human annotators to check and correct the automatic annotation rather than starting the annotation from un-annotated data. The active learning procedure is combined with an annotation error detection to control the reliablity of the annotation. With the goal of converging as fast as possible to reliable automatic annotations minimizing the human effort, we follow the active learning paradigm, which selects for annotation the most informative training examples required to achieve a better level of performance. We show that this procedure allows to quickly converge on correct annotations and thus minimize the cost of human supervision.
DATR Theories and DATR Models

Evans and Gazdar (Evans and Gazdar, 1989a; Evans and Gazdar, 1989b) introduced DATR as a simple, non-monotonic language for representing natural language lexicons. Although a number of implementations of DATR exist, the full language has until now lacked an explicit, declarative semantics. This paper rectifies the situation by providing a mathematical semantics for DATR. We present a view of DATR as a language for defining certain kins of partial functions by cases. The formal model provides a transparent treatment of DATR's notion of global context. It is shown that DATR's default mechanism can be accounted for by interpreting value descriptors as families of values indexed by paths.
Durable Address Translation in PCM-Based Flash Storage Systems

Phase change memory (PCM) is a promising DRAM alternative because of its non-volatility, high density, low standby power and close-to-DRAM performance. These features make PCM an attractive solution to optimize the management of NAND flash memory in embedded systems. However, PCM's limited write endurance hinders its application in embedded systems. Therefore, how to manage flash memory with PCM—particularly guarantee PCM a reasonable lifetime—becomes a challenging issue. In this paper, we propose to partially replace DRAM using PCM to optimize the management of flash memory metadata for better system reliability in the presence of power failure and system crash. To prolong PCM's lifetime, we present a write-activity-aware PCM-assisted flash memory management scheme, called PCM-FTL. By differentiating sequential and random I/O behaviors, a novel two-level mapping mechanism and a customized wear-leveling scheme are developed to reduce writes to PCM and extend its lifetime. We evaluate PCM-FTL with a variety of general-purpose and mobile I/O workloads. Experimental results show that PCM-FTL can significantly reduce write activities and achieve an even distribution of writes in PCM with very low overhead.
Extraction and Mapping of Drug Names from Free Text to a Standardized Nomenclature

Free text fields are often used to store clinical drug data in electronic health records. The use of free text facilitates rapid data entry by the clinician. Errors in spelling, abbreviations, and jargon, however, limit the utility of these data. We designed and implemented an algorithm, using open source tools and RxNorm, to extract and normalize drug data stored in free text fields of an anesthesia electronic health record. The algorithm was developed using a training set containing drug data from 49,518 cases, and validated using a validation set containing data from 14,655 cases. Overall sensitivity and specificity for the validation set were 92.2% and 95.7% respectively. The mains sources of error were misspellings and unknown but valid drug names. These preliminary results demonstrate that free text clinical drug data can be efficiently extracted and mapped to a controlled drug nomenclature.
ContextPreRF: Enhancing the Performance and Energy of GPUs With Nonuniform Register Access

Register files are a key data storage unit that impacts instruction throughput for graphics processing units (GPUs). Typically, GPU register files are quite large to accommodate many concurrent threads and are implemented using the same SRAM technology as the on-chip cache. We propose contextrf, a new register file architecture that efficiently leverages register files with nonuniform access characteristics, including hybrid SRAM/DRAM (S/D) and spintronic domain-wall memories (DWMs). Contextrf allows greater-capacity register files to be implemented in the same area within the GPU, with reduced power consumption. We also propose contextPreRF, a hardware preswitch scheme to hide switching delays-as soon as a register request is queued, the nonuniform access memories containing the corresponding register are sent a preemptive switch request. Thus, our scheme transparently hides the penalties of switching between register contexts. After replacing the register file SRAM with S/D, we can reduce energy by 37%, with a 1.4% average performance drop. Employing DWM, we reduce register file energy by 74%, with a 0.4% average performance penalty. For the denser DWM, we model converting the saved area into additional registers, cache, and shared memory-this improves performance by 13.5% over the baseline SRAM register file.
Center for Studies in Demography and Ecology Assessing Degeneracy in Statistical Models of Social Networks

This paper presents recent advances in the statistical modeling of random graphs that have an impact on the empirical study of social networks. Statistical exponential family models (Wasserman and Pattison 1996) are a generalization of the Markov random graph models introduced by Frank and Strauss (1986), which in turn are derived from developments in spatial statistics (Besag 1974). These models recognize the complex dependencies within relational data structures. A major barrier to the application of random graph models to social networks has been the lack of a sound statistical theory to evaluate model fit. This problem has at least three aspects: the specification of realistic models, the algorithmic difficulties of the inferential methods, and the assessment of the degree to which the graph structure produced by the models matches that of the data. We discuss these and related issues of the model degeneracy and inferential degeneracy for commonly used estimators.
Cerebral Autoregulation in Orthostatic Hypotension

arterial pressure moved in parallel fashion. The diastolic blood flow velocity increased more in the control group than in the patients during the early stages of the test; furthermore, only in the healthy volunteers did it increase significantly more than arterial pressure after 30 seconds and 60 seconds. Regarding the pulsatility index, the differences between the two groups were similar to the diastolic velocity results. Conclusions (1) Monitoring of mean blood flow velocity showed the ability to mantain an adequate cerebral blood flow in healthy subjects; this mechanism was not efficient in the patients with autonomic failure. (2) Diastolic velocity and pulsatility index values clearly showed that only in healthy subjects were cerebral hemodynamics relatively independent of pressure values. (Stroke. 1994^5:1771-1775.)
Improved detection and characterization of obscured central gland tumors of the prostate: texture analysis of non contrast and contrast enhanced MR images for differentiation of benign prostate hyperplasia (BPH) nodules and cancer

Objective: The purpose of this study to assess the value of texture analysis (TA) for prostate cancer (PCa) detection on T2 weighted images (T2WI) and dynamic contrastenhanced images (DCE) by differentiating between the PCa and Benign Prostate Hyperplasia (BPH). Materials & Methods: This study used 10 retrospective MRI data sets that were acquired from men with confirmed PCa. The prostate region of interest (ROI) was delineated by an expert on MRI data sets using automated prostate capsule segmentation scheme. The statistical significance test was used for feature selection scheme for optimal differentiation of PCa from BPH on MR images. In pre-processing, for T2-WI, Bias correction and all images intensities are standardized to a representative template. For DCE images, Bias correction and all images are registered to time point 1 for that patient. Following pre-processing texture, features from ROI were extracted and analyzed. Texture features that were extracted are: Intensity mean and standard deviation, Sobel (Edge detection), Haralick features, and Gabor features.
Using Brain Measurement to Evaluate Reality Based Interactions

We proposed the notion of Reality-Based Interaction (RBI) as a unifying concept to tie together a large subset of emerging interaction styles [7]. Interfaces should adhere to reality-based principles unless designers explicitly choose to make a tradeoff where they sacrifice reality in order to gain other positive qualities. When a reality-based interface has been designed, and the reality tradeoffs have been carefully considered, it is very difficult to determine whether or not the reality-based principles or reality tradeoffs resulted in a ‘good’ interface. To address this problem, we use functional near infrared spectroscopy (fNIRs), a new, non-invasive brain imaging technique, to complement standard usability experiments. We demonstrate an experimental protocol and data analysis algorithms which can help evaluators of RBI systems to acquire objective, real time metrics of users’ workload, and other states while working with a given interface.
The 3T3 cell cycle at low proliferation rates.

When the proliferation rate of Swiss 3T3 cells is decreased by limiting the availability of growth factors, cell cycle variability increases, as predicted by the transition probability model. Nevertheless, the transition probabilities would appear to play a relatively minor role in the regulation of proliferation rate. Instead, at least 40% of the increase in the average cycle time is brought about by an elongation of the minimum cycle time (i.e. the 'deterministic' part of the cycle). In addition, we have found that a substantial proportion of the cells (roughly 20%, in the present experiments, for doubling times of the order of 35-40 h) drop out of cycle in each generation, leading to a growth fraction of less than 1.0. The non-dividing cells, which we have previously shown to remain capable of division, would seem to support the existence of a Go state outside the normal cell cycle, and distinct from the indeterminate states postulated by the transition probability model. Because of the generation of nondividing cells at low proliferation rates, the log alpha and beta plots (distributions of cycle times, and sibling cycle time differences, respectively) are markedly concave, with a continuously decreasing slope. The transition probabilities cannot therefore be estimated directly and it is impossible to determine the extent to which they contribute to the regulation of proliferation rate. Rather, our data suggest that the transition probabilities are not uniform throughout the population under these conditions, but vary substantially from cell to cell. In addition to the changes in cell cycle kinetics, we also report an increased failure rate of cytokinesis, at low proliferation rates, leading initially to the appearance of binucleate cells. Such failures of cytokinesis may be responsible for the well-known rise in the incidence of binucleate and polyploid cells in the liver, with age.
Wintertime fCO2 Variability in the Subpolar North Atlantic Since 2004

Winter data of surface ocean temperature (SST), salinity (SSS) and CO2 fugacity (fCO2) collected on the VOS M/V Nuka Arctica in the subpolar North Atlantic between 2004 and 2017 are used to establish trends, drivers, and interannual variability. Over the period, waters cooled and freshened, and the fCO2 increased at a rate similar to the atmospheric CO2 growth rate. When accounting for the freshening, the inferred increase in dissolved inorganic carbon (DIC) was found to be approximately twice that expected from atmospheric CO2 alone. This is attributed to the cooling. In the Irminger Sea, fCO2 exhibited additional interannual variations driven by atmospheric forcing through winter mixing. As winter fCO2 in the region is close to the atmospheric, the subpolar North Atlantic has varied between being slightly supersaturated and slightly undersaturated over the investigated period.
Short vector code generation for the discrete Fourier transform

In this paper we use a mathematical approach to automatically generate high performance short vector code for the discrete Fourier transform (DFT). We represent the well-known Cooley-Tukey fast Fourier transform in a mathematical notation and formally derive a "short vector variant". Using this recursion we generate for a given DFT a large number of different algorithms, represented as formulas, and translate them into short vector code. Then we present a vector code specific dynamic programming method that searches in the space of different implementations for the fastest on the given architecture. We implemented this approach as part of the SPIRAL library generator On Pentium III and 4, our automatically generated SSE and SSE2 vector code compares favorably with the hand-tuned Intel vendor library.
PapierCraft: A System for Interactive Paper

The affordances of paper (e.g., ease of annotation and navigation) make it a fundamental tool for knowledge gathering and crystallization tasks. During such tasks, users create a rich web of annotation and cross references. Unfortunately, as paper is a static media, this web often gets trapped in the physical world. Some systems such as XLibris [33] address this problem by transferring this task in the digital realm where it is easy to capture all links created by the users. This approach is very powerful but suffers from the limitations of current tablet computers such as a limited screen space. In this paper we propose a paper-based interface to support the knowledge gathering and crystallization process. Our system considers document printouts as proxies of digital documents stored on the user’s computer. Users can draw command gestures on printouts to indicate operations such a copying a document area, pasting an area previously copied, or creating a link. Upon pen synchronization, our infrastructure will execute these commands and present the result in our custom built viewer. In this paper we present the design and implementation of the PapierCraft system and report our experience while building this system as well as early feedback from a small group of users.
A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.
History Matching Channelized Facies Models Using Ensemble Smoother With A Deep Learning Parameterization

Summary Ensemble data assimilation methods have been successfully applied in several real-life history-matching problems. However, because these methods rely on Gaussian assumptions, their performance is severely degraded when the prior geology is described in terms of complex facies distributions. This work introduces a novel parameterization based on deep learning for history matching facies models with ensemble methods. The proposed method consists on a parameterization of geological facies by means of a deep belief network (DBN) used as an autoencoder. The process begins with a large set of facies realizations which are used for training the DBN. The trained network has two parts: an encoder and a decoder function. The encoder is used to construct a continuous parameterization of the facies which is iteratively updated to account for observed production data using the method ensemble smoother with multiple data assimilation (ES-MDA). After each iteration of ES-MDA, the decoder is used to reconstruct the facies realizations. The proposed method is tested in three synthetic history-matching problems with channelized facies constructed with multiple point geostatistics. We compare the results of the DBN parameterization against the standard ES-MDA (with no parameterization) and the recently proposed optimization-based principal component analysis (OPCA). Our results show that all procedures are able to match the observed production data. However, standard ES-MDA failed to generate channel facies with well-defined boundaries. OPCA and DBN parameterizations improved the facies description resulting in the expected bi-modal distributions of log-permeability. This paper reports our initial results on an ongoing investigation with deep learning. Nevertheless, the results presented here indicate a great potential on the use of deep learning technologies in the inverse modeling of petroleum reservoirs.
Hierarchical Cellular Tree: An Efficient Indexing Scheme for Content-Based Retrieval on Multimedia Databases

One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. The developers and users who are accustomed to making queries to retrieve a particular multimedia item from a large scale database can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the requirements of a multimedia database, such as dynamic indexing or the presence of high-dimensional audiovisual features. Such structures do not scale well with the ever increasing size of multimedia databases whilst inducing corruption and resulting in an over-crowded indexing structure. This paper addresses such problems and presents a novel indexing technique, hierarchical cellular tree (HCT), which is designed to bring an effective solution especially for indexing large multimedia databases. Furthermore it provides an enhanced browsing capability, which enables user to make a guided tour within the database. A pre-emptive cell-search mechanism is introduced in order to prevent corruption, which may occur due to erroneous item insertions. Among the hierarchical levels that are built in a bottom-up fashion, similar items are collected into appropriate cellular structures at some level. Cells are subject to mitosis operations when the dissimilarity exceeds a required level. By mitosis operations, cells are kept focused and compact and yet, they can grow into any dimension as long as the compactness is maintained. The proposed indexing scheme is then used along with a recently introduced query method, the progressive query, in order to achieve the ultimate goal, from the user point of view that is retrieval of the most relevant items in the earliest possible time regardless of the database size. Experimental results show that the speed of retrievals is significantly improved and the indexing structure shows no sign of degradations when the database size is increased. Furthermore, HCT indexing body can conveniently be used for efficient browsing and navigation operations among the multimedia database items
Using the crowd for readability prediction

Abstract While human annotation is crucial for many natural language processing tasks, it is often very expensive and time-consuming. Inspired by previous work on crowdsourcing, we investigate the viability of using non-expert labels instead of gold standard annotations from experts for a machine learning approach to automatic readability prediction. In order to do so, we evaluate two different methodologies to assess the readability of a wide variety of text material: A more traditional setup in which expert readers make readability judgments and a crowdsourcing setup for users who are not necessarily experts. To this purpose two assessment tools were implemented: a tool where expert readers can rank a batch of texts based on readability, and a lightweight crowdsourcing tool, which invites users to provide pairwise comparisons. To validate this approach, readability assessments for a corpus of written Dutch generic texts were gathered. By collecting multiple assessments per text, we explicitly wanted to level out readers' background knowledge and attitude. Our findings show that the assessments collected through both methodologies are highly consistent and that crowdsourcing is a viable alternative to expert labeling. This is a good news as crowdsourcing is more lightweight to use and can have access to a much wider audience of potential annotators. By performing a set of basic machine learning experiments using a feature set that mainly encodes basic lexical and morpho-syntactic information, we further illustrate how the collected data can be used to perform text comparisons or to assign an absolute readability score to an individual text. We do not focus on optimising the algorithms to achieve the best possible results for the learning tasks, but carry them out to illustrate the various possibilities of our data sets. The results on different data sets, however, show that our system outperforms the readability formulas and a baseline language modelling approach. We conclude that readability assessment by comparing texts is a polyvalent methodology, which can be adapted to specific domains and target audiences if required.
STAIR Codes: A General Family of Erasure Codes for Tolerating Device and Sector Failures

Practical storage systems often adopt erasure codes to tolerate device failures and sector failures, both of which are prevalent in the field. However, traditional erasure codes employ device-level redundancy to protect against sector failures, and hence incur significant space overhead. Recent sector-disk (SD) codes are available only for limited configurations. By making a relaxed but practical assumption, we construct a general family of erasure codes called STAIR codes, which efficiently and provably tolerate both device and sector failures without any restriction on the size of a storage array and the numbers of tolerable device failures and sector failures. We propose the upstairs encoding and downstairs encoding methods, which provide complementary performance advantages for different configurations. We conduct extensive experiments on STAIR codes in terms of space saving, encoding/decoding speed, and update cost. We demonstrate that STAIR codes not only improve space efficiency over traditional erasure codes, but also provide better computational efficiency than SD codes based on our special code construction. Finally, we present analytical models that characterize the reliability of STAIR codes, and show that the support of a wider range of configurations by STAIR codes is critical for tolerating sector failure bursts discovered in the field.