Query: A framework for analyzing semantic change of words across time

Relevant passages:
Measuring praise and criticism: Inference of semantic orientation from association <=SEP=> The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.

Human behavior and the principle of least effort : An introduction to human ecology <=SEP=> JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.

Word Epoch Disambiguation: Finding How Words Change Over Time <=SEP=> In this paper we introduce the novel task of “word epoch disambiguation,” defined as the problem of identifying changes in word usage over time. Through experiments run using word usage examples collected from three major periods of time (1800, 1900, 2000), we show that the task is feasible, and significant differences can be observed between occurrences of words in different periods of time.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews <=SEP=> This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

Dynamic topic models <=SEP=> A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.

Logic Programming for Software-Defined Networks <=SEP=> 

Direct Manipulation Interfaces <=SEP=> Approach • Social information independent of physical analog s – Text (e.g., emote) – abstract graphical representations (e.g., chat cir cles) • Interested in Abstract Approach a. Creates and deploys working systems b. Lack of attention Babble Prototype

Fine-grained metadata journaling on NVM <=SEP=> Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.

Individual ball possession in soccer <=SEP=> This paper describes models for detecting individual and team ball possession in soccer based on position data. The types of ball possession are classified as Individual Ball Possession (IBC), Individual Ball Action (IBA), Individual Ball Control (IBC), Team Ball Possession (TBP), Team Ball Control (TBC) und Team Playmaking (TPM) according to different starting points and endpoints and the type of ball control involved. The machine learning approach used is able to determine how long the ball spends in the sphere of influence of a player based on the distance between the players and the ball together with their direction of motion, speed and the acceleration of the ball. The degree of ball control exhibited during this phase is classified based on the spatio-temporal configuration of the player controlling the ball, the ball itself and opposing players using a Bayesian network. The evaluation and application of this approach uses data from 60 matches in the German Bundesliga season of 2013/14, including 69,667 IBA intervals. The identification rate was F = .88 for IBA and F = .83 for IBP, and the classification rate for IBC was κ = .67. Match analysis showed the following mean values per match: TBP 56:04 ± 5:12 min, TPM 50:01 ± 7:05 min and TBC 17:49 ± 8:13 min. There were 836 ± 424 IBC intervals per match and their number was significantly reduced by -5.1% from the 1st to 2nd half. The analysis of ball possession at the player level indicates shortest accumulated IBC times for the central forwards (0:49 ± 0:43 min) and the longest for goalkeepers (1:38 ± 0:58 min), central defenders (1:38 ± 1:09 min) and central midfielders (1:27 ± 1:08 min). The results could improve performance analysis in soccer, help to detect match events automatically, and allow discernment of higher value tactical structures, which is based on individual ball possession.

Tank-Like Module-Based Climbing Robot Using Passive Compliant Joints <=SEP=> This paper proposes an underactuated modular climbing robot with flat dry elastomer adhesives. This robot is designed to achieve high speed, high payload, and dexterous motions that are typical drawbacks of previous climbing robots. Each module is designed as a tread-wheeled mechanism to simultaneously realize high speed and high adhesive force. Two modules are connected by compliant joints, which induce a positive preload on the front wheels resulting in stable climbing and high payload capacity. Compliant joints also help the robot to perform various transitions. An active tail is adopted to regulate the preload of the second module. Force transfer equations are derived and stable operating conditions are verified. The stiffness coefficients of the compliant joints and the active tail force are determined optimally to satisfy the constraints of stable operation. The prototype two-module robot achieves 6-cm/s speed and 500-g payload capacity on vertical surfaces. The abilities of flat surface locomotion, internal, external, and thin-wall transitions, and overcoming various sized obstacles are validated through experiment. The principle of joint compliance can be adopted in other climbing robots to enhance their stability and transition capability.

Sparse Bayesian Extreme Learning Machine for Multi-classification <=SEP=> Extreme learning machine (ELM) has become a popular topic in machine learning in recent years. ELM is a new kind of single-hidden layer feedforward neural network with an extremely low computational cost. ELM, however, has two evident drawbacks: 1) the output weights solved by Moore-Penrose generalized inverse is a least squares minimization issue, which easily suffers from overfitting and 2) the accuracy of ELM is drastically sensitive to the number of hidden neurons so that a large model is usually generated. This brief presents a sparse Bayesian approach for learning the output weights of ELM in classification. The new model, called Sparse Bayesian ELM (SBELM), can resolve these two drawbacks by estimating the marginal likelihood of network outputs and automatically pruning most of the redundant hidden neurons during learning phase, which results in an accurate and compact model. The proposed SBELM is evaluated on wide types of benchmark classification problems, which verifies that the accuracy of SBELM model is relatively insensitive to the number of hidden neurons; and hence a much more compact model is always produced as compared with other state-of-the-art neural network classifiers.

An Integrated Environment for the Development of Knowledge-Based Recommender Applications <=SEP=> The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.

Agile distributed software development: enacting control through media and context <=SEP=> While face-to-face interaction is fundamental in agile software development, distributed environments must rely extensively on mediated interactions. Practicing agile principles in distributed environments therefore poses particular control challenges related to balancing fixed vs. evolving quality requirements and people vs. process-based collaboration. To investigate these challenges, we conducted an in-depth case study of a successful agile distributed software project with participants from a Russian firm and a Danish firm. Applying Kirsch’s elements of control framework, we offer an analysis of how control was enacted through the project context and in the participants’ mediated communication. The analysis reveals that formal measurement and evaluation control were persistently enacted through mediated communication. These formal control practices were, however, predominantly carried out in conjunction with informal roles and relationships such as clan-like control inherent in agile development. Overall, the study demonstrates that, if appropriately applied, communication technologies can significantly support distributed, agile practices by allowing concurrent enactment of both formal and informal controls. The paper discusses these findings as they relate to previous research and concludes with their implications for future research.

Active Learning with Oracle Epiphany <=SEP=> We present a theoretical analysis of active learning with more realistic interactions with human oracles. Previous empirical studies have shown oracles abstaining on difficult queries until accumulating enough information to make label decisions. We formalize this phenomenon with an “oracle epiphany model” and analyze active learning query complexity under such oracles for both the realizable and the agnostic cases. Our analysis shows that active learning is possible with oracle epiphany, but incurs an additional cost depending on when the epiphany happens. Our results suggest new, principled active learning approaches with realistic oracles.

CLOS: Integrating Object-Oriented and Functional Programming <=SEP=> Lisp has a long history as a functional language,* where action is invoked by calling a procedure, and where procedural abstraction and encapsulation provide convenient modularity boundaries. A number of attempts have been made to graft object-oriented programming into this framework without losing the essential character of Lisp—to include the benefits of data abstraction, extensible type classification, incremental operator definition, and code reuse through an inheritance hierarchy.
The Common Lisp Object System (CLOS) [3], a result of the ANSI standardization process for Common Lisp, represents a marriage of these two traditions. This article explores the landscape in which the major object-oriented facilities exist, showing how the CLOS solution is effective within the two contexts.

Time-Bounded Authentication of FPGAs <=SEP=> This paper introduces a novel technique to authenticate and identify field-programmable gate arrays (FPGAs). The technique uses the reconfigurability feature of FPGAs to perform self-characterization and extract the unique timing of the FPGA building blocks over the space of possible inputs. The characterization circuit is then exploited for constructing a physically unclonable function (PUF). The PUF can accept different forms of challenges including pulsewidth, digital binary, and placement challenges. The responses from the PUF are only verifiable by entities with access to the unique timing signature. However, the authentic device is the only entity who can respond within a given time constraint. The constraint is set by the gap between the speed of PUF evaluation on authentic hardware and simulation of its behavior. A suite of authentication protocols is introduced based on the time-bounded mechanism. We ensure that the responses are robust to fluctuations in operational conditions such as temperature and voltage variations by employing: 1) a linear calibration mechanism that adjusts the clock frequency by a feedback from on-chip temperature and voltage sensor readings, and 2) a differential PUF structure with real-valued responses that cancels out the common impact of variations on delays. Security against various attacks is discussed and a proof-of-concept implementation of signature extraction and authentication are demonstrated on Xilinx Virtex 5 FPGAs.

CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum <=SEP=> Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course.
 Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.

The Weibull-G Family of Probability Distributions <=SEP=> Abstract: The Weibull distribution is the most important distribution for problems in reliability. We study some mathematical properties of the new wider Weibull-G family of distributions. Some special models in the new family are discussed. The properties derived hold to any distribution in this family. We obtain general explicit expressions for the quantile function, ordinary and incomplete moments, generating function and order statistics. We discuss the estimation of the model parameters by maximum likelihood and illustrate the potentiality of the extended family with two applications to real data.

Using Smart Sensor Strings for Continuous Monitoring of Temperature Stratification in Large Water Bodies <=SEP=> A "smart" thermistor string for continuous long-term temperature profiling in large water bodies is described allowing highly matched yet low-cost spatial and temporal temperature measurements. The sensor uses the three-wire SDI-12 communications standard to enable a low-powered radio or data logger on supporting buoys to command measurements and retrieve high-resolution temperature data in digital form. Each "smart" temperature sensor integrates a thermistor element, measurement circuitry, power control, calibration coefficient storage, temperature computation, and data communications. Multiple addressable sensors at discrete vertical depths are deployed along a three-wire cable that provides power and allows data transfer at regular intervals. Circuit, manufacturing, and automated calibration techniques allow temperature measurements with a resolution of plusmn0.003degC, and with intersensor matching of plusmn0.006degC. The low cost of each sensor is achieved by using poor tolerance thermistor and circuit components in conjunction with a 15-bit charge-balance analog-to-digital converter. Sensor inaccuracies and temperature coefficients are corrected by a two-point calibration procedure made possible by a standard-curve generator within the sensor, based upon the method of finite differences. This two-point calibration process allows in-field sensor string calibration in stratified water bodies and provides a means to correct for long-term calibration drift without having to return the string to a laboratory

Reduction of Computational Efforts in Finite Element-Based Permanent Magnet Traction Motor Optimization <=SEP=> This paper presents a method for reducing computational time in constrained single objective optimization problems related to permanent magnet motors modeled using computationally intensive finite element method. The method is based on differential evolution algorithm. The principal approach is to interrupt the evaluation of inequality constraints after encountering the constraint which is violated and continue their evaluation only if better solutions are obtained in comparison with the candidate vector from the previous generation both in terms of inequality constraints and the cost function. This approach avoids unnecessary time consuming finite element calculations required for solving the inequality constraints and saves the overall optimization time without affecting the convergence rate. The main features of this approach and how it complements the existing method for handling inequality constraints in Differential Evolution algorithm is demonstrated on design optimization of an interior permanent magnet motor for the low-floor tram TMK 2200. The motor geometry is optimized for maximum torque density.

State-of-the-art autogenous ear reconstruction in cases of microtia. <=SEP=> Ear reconstruction is considered to be a challenging form of surgery. In cases of microtia, surgeons must reconstruct complex missing contours, which necessitates the use of a support and skin remnants to cover this support. Although the use of synthetic material has been proposed in order to avoid harvesting and carving cartilage, the best long-term choice for reconstructing an ear is autologous rib cartilage. This procedure requires good understanding of the 3-dimensional architecture of the ear and learning the step-by-step construction of a harmonious framework (which with practice will become the most straightforward part of the procedure). Surgery, usually performed at the age of 9 or 10 years, is planned in 2 stages. In the first stage, the framework is placed under a skin pocket. Six months later, the sulcus is created using an additional cartilage graft for projection and a skin-grafted galeal fascial flap. In order to shorten the learning curve, a detailed carving process is described here, as well as a tool to enable training before surgery. Remnants of the microtic ear can have many different shapes; therefore, a comprehensive approach to skin management is proposed, providing a simple surgical classification for all types of microtia. Furthermore, some refinements of the cartilage framework and the construction of the retroauricular sulcus have improved results. Whenever possible, successful reconstruction of a microtic ear with autologous rib cartilage, as opposed to synthetic materials, is by far the best option.

An alignment based similarity measure for hand detection in cluttered sign language video <=SEP=> Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a Web cam in a user's home. Moreover, the signers' clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The histogram of oriented gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a support vector machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the vocabulary guided pyramid match kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

Phylogenetic analysis in the anomaly zone. <=SEP=> The concatenation method has been widely used as a means of combining data to estimate phylogenetic trees (Huelsenbeck et al. 1996a, 1996b; Glazko and Nei 2003). However, simulation studies have shown that the maximum likelihood (ML) estimate of the species tree for concatenated sequences may be statistically inconsistent if the gene trees are highly heterogeneous (Kolaczkowski and Thornton 2004; Kubatko and Degnan 2007). Recently, Degnan and Rosenberg (2006) defined an “anomaly zone”—a set of short internal branches in species trees that will generate gene trees that are discordant with the species tree more often than gene trees that are concordant. Kubatko and Degnan (2007) went on to show that when DNA sequences are generated from gene trees simulated from species trees in the anomaly zone, as well as from species trees slightly outside this zone but still with short internal branches, the ML estimate of the species tree for the concatenated sequences can be inconsistent, resulting in increasing certainty in the wrong species tree. These studies were all performed with a molecular clock on rooted gene and species trees within the variation realized in stochastic simulations of DNA sequences under the Jukes and Cantor (1969) model of nucleotide substitution. They applied the ML method with a clock to recover phylogenetic trees from their simulated concatenated data sets. In this paper, we show that phylogenetic methods that solely utilize the relative order of divergences among a set of DNA sequences as a criterion for inferring phylogenies, such as the unweighted pair group method with arithmetic mean (UPGMA), are statistically consistent even when DNA sequences are generated from gene trees simulated from species trees in the anomaly zone. In addition, we use simulation to assess the performance of a variety of tree construction methods when analyzing concatenated sequences generated from 4 and 5-taxon species tree located in the anomaly zone and show that a variety of methods do in fact recover the correct species tree topology, whereas ML, with or without a molecular clock, remains inconsistent. However, the branch lengths of the tree inferred from concatenated data are inevitably overestimated, as predicted by theory. Finally, simulations also suggest that a newly proposed Bayesian approach for estimating species trees from multiple unlinked loci, BEST (Liu and Pearl 2007; Liu et al. 2008), is consistent in both topology and branch lengths on data sets generated from species trees in the anomaly zone. THEORETICAL RESULTS

Autocomplete hand-drawn animations <=SEP=> Hand-drawn animation is a major art form and communication medium, but can be challenging to produce. We present a system to help people create frame-by-frame animations through manual sketches. We design our interface to be minimalistic: it contains only a canvas and a few controls. When users draw on the canvas, our system silently analyzes all past sketches and predicts what might be drawn in the future across spatial locations and temporal frames. The interface also offers suggestions to beautify existing drawings. Our system can reduce manual workload and improve output quality without compromising natural drawing flow and control: users can accept, ignore, or modify such predictions visualized on the canvas by simple gestures. Our key idea is to extend the local similarity method in [Xing et al. 2014], which handles only low-level spatial repetitions such as hatches within a single frame, to a global similarity that can capture high-level structures across multiple frames such as dynamic objects. We evaluate our system through a preliminary user study and confirm that it can enhance both users' objective performance and subjective satisfaction.

An MEG-based brain–computer interface (BCI) <=SEP=> Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

3D Reconstruction of Human Skeleton from Single Images or Monocular Video Sequences <=SEP=> In this paper, we first review the approaches to recover 3D shape and related movements of a human and then we present an easy and reliable approach to recover a 3D model using just one image or monocular video sequence. A simplification of the perspective camera model is required, due to the absence of stereo view. The human figure is reconstructed in a skeleton form and to improve the visual quality, a pre-defined human model is also fitted to the recovered 3D data.

Estimation of Primary Quantization Matrix in Double Compressed JPEG Images <=SEP=> In this report, we present a method for estimation of primary quantization matrix from a double compressed JPEG image. We first identify characteristic features that occur in DCT histograms of individual coefficients due to double compression. Then, we present 3 different approaches that estimate the original quantization matrix from double compressed images. Finally, most successful of them Neural Network classifier is discussed and its performance and reliability is evaluated in a series of experiments on various databases of double compressed images. It is also explained in this paper, how double compression detection techniques and primary quantization matrix estimators can be used in steganalysis of JPEG files and in digital forensic analysis for detection of digital forgeries.

Crowd analysis using visual and non-visual sensors, a survey <=SEP=> This paper proposes a critical survey of crowd analysis techniques using visual and non-visual sensors. Automatic crowd understanding has a massive impact on several applications including surveillance and security, situation awareness, crowd management, public space design, intelligent and virtual environments. In case of emergency, it enables practical safety applications by identifying crowd situational context information. This survey identifies different approaches as well as relevant work on crowd analysis by means of visual and non-visual techniques. Multidisciplinary research groups are addressing crowd phenomenon and its dynamics ranging from social, and psychological aspects to computational perspectives. The possibility to use smartphones as sensing devices and fuse this information with video sensors data, allows to better describe crowd dynamics and behaviors. Eventually, challenges and further research opportunities with reference to crowd analysis are exposed.

Rare adipose disorders (RADs) masquerading as obesity <=SEP=> Rare adipose disorders (RADs) including multiple symmetric lipomatosis (MSL), lipedema and Dercum's disease (DD) may be misdiagnosed as obesity. Lifestyle changes, such as reduced caloric intake and increased physical activity are standard care for obesity. Although lifestyle changes and bariatric surgery work effectively for the obesity component of RADs, these treatments do not routinely reduce the abnormal subcutaneous adipose tissue (SAT) of RADs. RAD SAT likely results from the growth of a brown stem cell population with secondary lymphatic dysfunction in MSL, or by primary vascular and lymphatic dysfunction in lipedema and DD. People with RADs do not lose SAT from caloric limitation and increased energy expenditure alone. In order to improve recognition of RADs apart from obesity, the diagnostic criteria, histology and pathophysiology of RADs are presented and contrasted to familial partial lipodystrophies, acquired partial lipodystrophies and obesity with which they may be confused. Treatment recommendations focus on evidence-based data and include lymphatic decongestive therapy, medications and supplements that support loss of RAD SAT. Associated RAD conditions including depression, anxiety and pain will improve as healthcare providers learn to identify and adopt alternative treatment regimens for the abnormal SAT component of RADs. Effective dietary and exercise regimens are needed in RAD populations to improve quality of life and construct advanced treatment regimens for future generations.

LSTM-Based NeuroCRFs for Named Entity Recognition <=SEP=> Although NeuroCRF, an augmented Conditional Random Fields (CRF) model whose feature function is parameterized as a Feed-Forward Neural Network (FF NN) on word embeddings, has soundly outperformed traditional linear-chain CRF on many sequence labeling tasks, it is held back by the fact that FF NNs have a fixed input length and therefore cannot take advantage of the full input sentence. We propose to address this issue by replacing the FF NN with a Long Short-Term Memory (LSTM) NN, which can summarize an input of arbitrary length into a fixed dimension representation. The resulting model obtains F1=89.28 on WikiNER dataset, a significant improvement over the NeuroCRF baseline’s F1=87.58, which is already a highly competitive result.