1,019 research outputs found
Integrating Preclinical and Clinical Models of Negative Urgency
Overwhelming evidence suggests that negative urgency is robustly associated with rash, ill-advised behavior, and this trait may hamper attempts to treat patients with substance use disorder. Research applying negative urgency to clinical treatment settings has been limited, in part, due to the absence of an objective, behavioral, and translational model of negative urgency. We suggest that development of such a model will allow for determination of prime neurological and physiological treatment targets, the testing of treatment effectiveness in the preclinical and the clinical laboratory, and, ultimately, improvement in negative-urgency-related treatment response and effectiveness. In the current paper, we review the literature on measurement of negative urgency and discuss limitations of current attempts to assess this trait in human models. Then, we review the limited research on animal models of negative urgency and make suggestions for some promising models that could lead to a translational measurement model. Finally, we discuss the importance of applying objective, behavioral, and translational models of negative urgency, especially those that are easily administered in both animals and humans, to treatment development and testing and make suggestions on necessary future work in this field. Given that negative urgency is a transdiagnostic risk factor that impedes treatment success, the impact of this work could be large in reducing client suffering and societal costs
Occupational Fraud Detection Through Visualization
Occupational fraud affects many companies worldwide causing them economic
loss and liability issues towards their customers and other involved entities.
Detecting internal fraud in a company requires significant effort and,
unfortunately cannot be entirely prevented. The internal auditors have to
process a huge amount of data produced by diverse systems, which are in most
cases in textual form, with little automated support. In this paper, we exploit
the advantages of information visualization and present a system that aims to
detect occupational fraud in systems which involve a pair of entities (e.g., an
employee and a client) and periodic activity. The main visualization is based
on a spiral system on which the events are drawn appropriately according to
their time-stamp. Suspicious events are considered those which appear along the
same radius or on close radii of the spiral. Before producing the
visualization, the system ranks both involved entities according to the
specifications of the internal auditor and generates a video file of the
activity such that events with strong evidence of fraud appear first in the
video. The system is also equipped with several different visualizations and
mechanisms in order to meet the requirements of an internal fraud detection
system
Efficient Generation of Geographically Accurate Transit Maps
We present LOOM (Line-Ordering Optimized Maps), a fully automatic generator
of geographically accurate transit maps. The input to LOOM is data about the
lines of a given transit network, namely for each line, the sequence of
stations it serves and the geographical course the vehicles of this line take.
We parse this data from GTFS, the prevailing standard for public transit data.
LOOM proceeds in three stages: (1) construct a so-called line graph, where
edges correspond to segments of the network with the same set of lines
following the same course; (2) construct an ILP that yields a line ordering for
each edge which minimizes the total number of line crossings and line
separations; (3) based on the line graph and the ILP solution, draw the map. As
a naive ILP formulation is too demanding, we derive a new custom-tailored
formulation which requires significantly fewer constraints. Furthermore, we
present engineering techniques which use structural properties of the line
graph to further reduce the ILP size. For the subway network of New York, we
can reduce the number of constraints from 229,000 in the naive ILP formulation
to about 4,500 with our techniques, enabling solution times of less than a
second. Since our maps respect the geography of the transit network, they can
be used for tiles and overlays in typical map services. Previous research work
either did not take the geographical course of the lines into account, or was
concerned with schematic maps without optimizing line crossings or line
separations.Comment: 7 page
A Singular Value Thresholding Algorithm for Matrix Completion
This paper introduces a novel algorithm to approximate the matrix with minimum
nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood
as the convex relaxation of a rank minimization problem and arises in many important
applications as in the task of recovering a large matrix from a small subset of its entries (the famous
Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable
to large problems of this kind with over a million unknown entries. This paper develops a simple
first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in
which the optimal solution has low rank. The algorithm is iterative, produces a sequence of matrices
{X^k,Y^k}, and at each step mainly performs a soft-thresholding operation on the singular values
of the matrix Y^k. There are two remarkable features making this attractive for low-rank matrix
completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix;
the second is that the rank of the iterates {X^k} is empirically nondecreasing. Both these facts allow
the algorithm to make use of very minimal storage space and keep the computational cost of each
iteration low. On the theoretical side, we provide a convergence analysis showing that the sequence
of iterates converges. On the practical side, we provide numerical examples in which 1,000 × 1,000
matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate
that our approach is amenable to very large scale problems by recovering matrices of rank about
10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are
connected with the recent literature on linearized Bregman iterations for ℓ_1 minimization, and we
develop a framework in which one can understand these algorithms in terms of well-known Lagrange
multiplier algorithms
Efficient First Order Methods for Linear Composite Regularizers
A wide class of regularization problems in machine learning and statistics
employ a regularization term which is obtained by composing a simple convex
function \omega with a linear transformation. This setting includes Group Lasso
methods, the Fused Lasso and other total variation methods, multi-task learning
methods and many more. In this paper, we present a general approach for
computing the proximity operator of this class of regularizers, under the
assumption that the proximity operator of the function \omega is known in
advance. Our approach builds on a recent line of research on optimal first
order optimization methods and uses fixed point iterations for numerically
computing the proximity operator. It is more general than current approaches
and, as we show with numerical simulations, computationally more efficient than
available first order methods which do not achieve the optimal rate. In
particular, our method outperforms state of the art O(1/T) methods for
overlapping Group Lasso and matches optimal O(1/T^2) methods for the Fused
Lasso and tree structured Group Lasso.Comment: 19 pages, 8 figure
Conic Multi-Task Classification
Traditionally, Multi-task Learning (MTL) models optimize the average of
task-related objective functions, which is an intuitive approach and which we
will be referring to as Average MTL. However, a more general framework,
referred to as Conic MTL, can be formulated by considering conic combinations
of the objective functions instead; in this framework, Average MTL arises as a
special case, when all combination coefficients equal 1. Although the advantage
of Conic MTL over Average MTL has been shown experimentally in previous works,
no theoretical justification has been provided to date. In this paper, we
derive a generalization bound for the Conic MTL method, and demonstrate that
the tightest bound is not necessarily achieved, when all combination
coefficients equal 1; hence, Average MTL may not always be the optimal choice,
and it is important to consider Conic MTL. As a byproduct of the generalization
bound, it also theoretically explains the good experimental results of previous
relevant works. Finally, we propose a new Conic MTL model, whose conic
combination coefficients minimize the generalization bound, instead of choosing
them heuristically as has been done in previous methods. The rationale and
advantage of our model is demonstrated and verified via a series of experiments
by comparing with several other methods.Comment: Accepted by European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD)-201
Cognitive behaviour analysis based on facial information using depth sensors
Cognitive behaviour analysis is considered of high impor- tance with many innovative applications in a range of sectors including healthcare, education, robotics and entertainment. In healthcare, cogni- tive and emotional behaviour analysis helps to improve the quality of life of patients and their families. Amongst all the different approaches for cognitive behaviour analysis, significant work has been focused on emo- tion analysis through facial expressions using depth and EEG data. Our work introduces an emotion recognition approach using facial expres- sions based on depth data and landmarks. A novel dataset was created that triggers emotions from long or short term memories. This work uses novel features based on a non-linear dimensionality reduction, t-SNE, applied on facial landmarks and depth data. Its performance was eval- uated in a comparative study, proving that our approach outperforms other state-of-the-art features
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