1,120 research outputs found
One Step Forward, Two Steps Back: The Recognized But Undefined Federal Psychotherapist-Patient Privilege
Instead of demanding every man\u27s evidence, as is the general presumption, courts recognize the need for some evidence to remain private. Federal Rule of Evidence 501 governs federal evidentiary privileges without defining or identifying specific privileges. As a result, courts have developed privileges on a case-by-case basis, including the recognition of a psychotherapist-patient privilege. As different circuits pass judgment on the psychotherapist-patient privilege, conflict inevitably arises. Some circuits simply reject the privilege, while others recognize the privilege but apply it differently. The recognition of the psychotherapist-patient privilege was the necessary first step, but leaving the privilege undefined encourages litigation and undermines the privilege by making its application uncertain
Nanosecond electro-optical switching with a repetition rate above 20MHz
We describe an electro-optical switch based on a commercial electro-optic
modulator (modified for high-speed operation) and a 340V pulser having a rise
time of 2.2ns (at 250V). It can produce arbitrary pulse patterns with an
average repetition rate beyond 20MHz. It uses a grounded-grid triode driven by
transmitting power transistors. We discuss variations that enable analog
operation, use the step-recovery effect in bipolar transistors, or offer other
combinations of output voltage, size, and cost.Comment: 3 pages, 3 figures. Minor change
A Hierarchial Neural Network Implementation for Forecasting
In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctl
Algorithmic aspects of disjunctive domination in graphs
For a graph , a set is called a \emph{disjunctive
dominating set} of if for every vertex , is either
adjacent to a vertex of or has at least two vertices in at distance
from it. The cardinality of a minimum disjunctive dominating set of is
called the \emph{disjunctive domination number} of graph , and is denoted by
. The \textsc{Minimum Disjunctive Domination Problem} (MDDP)
is to find a disjunctive dominating set of cardinality .
Given a positive integer and a graph , the \textsc{Disjunctive
Domination Decision Problem} (DDDP) is to decide whether has a disjunctive
dominating set of cardinality at most . In this article, we first propose a
linear time algorithm for MDDP in proper interval graphs. Next we tighten the
NP-completeness of DDDP by showing that it remains NP-complete even in chordal
graphs. We also propose a -approximation
algorithm for MDDP in general graphs and prove that MDDP can not be
approximated within for any unless NP
DTIME. Finally, we show that MDDP is
APX-complete for bipartite graphs with maximum degree
Reducing Occupational Distress in Veterinary Medicine Personnel with Acceptance and Commitment Training: A Pilot Study
Aims
To determine whether an educational programme targeting the reaction of veterinary personnel to difficult client interactions reduced burden transfer, stress and burnout in veterinary staff.
Methods
Employees of three small-animal veterinary hospitals in the south-western United States of America were recruited and randomised to intervention (educational programme; n = 16) or control (no intervention; n = 18) groups. Participants of this randomised, parallel arms trial completed pre-programme assessment including the Burden Transfer Inventory (BTI), Perceived Stress Scale, and Copenhagen Burnout Inventory. Assessment was followed by two, group-format educational sessions, based on acceptance and commitment training, tailored to reducing reactivity to difficult veterinary client interactions (intervention group only). After training was completed, both groups were assessed using the same measures and the intervention participants provided use and acceptability ratings.
Results
Intervention participants rated the programme as useful and appropriate, and reported that programme techniques were used a median of 43 (min 9, max 68) times during the 2 weeks prior to retesting. Relative to pre-programme scores, median post-programme scores for reaction (subscore of BTI) to difficult client interactions decreased in the intervention group (33 vs. 54; p = 0.047), but not in the control group (51 vs. 59; p = 0.210). Changes in median scores for stress and burnout from pre- to post-programme were non-significant for both groups.
Conclusions
This pilot and feasibility trial showed high rates of acceptability and use by participants, as well as promising reductions in burden transfer. A larger scale clinical trial with follow-up at extended time points is needed to more fully examine the efficacy of this novel programme
Smoothed Analysis of the Minimum-Mean Cycle Canceling Algorithm and the Network Simplex Algorithm
The minimum-cost flow (MCF) problem is a fundamental optimization problem
with many applications and seems to be well understood. Over the last half
century many algorithms have been developed to solve the MCF problem and these
algorithms have varying worst-case bounds on their running time. However, these
worst-case bounds are not always a good indication of the algorithms'
performance in practice. The Network Simplex (NS) algorithm needs an
exponential number of iterations for some instances, but it is considered the
best algorithm in practice and performs best in experimental studies. On the
other hand, the Minimum-Mean Cycle Canceling (MMCC) algorithm is strongly
polynomial, but performs badly in experimental studies.
To explain these differences in performance in practice we apply the
framework of smoothed analysis. We show an upper bound of
for the number of iterations of the MMCC algorithm.
Here is the number of nodes, is the number of edges, and is a
parameter limiting the degree to which the edge costs are perturbed. We also
show a lower bound of for the number of iterations of the
MMCC algorithm, which can be strengthened to when
. For the number of iterations of the NS algorithm we show a
smoothed lower bound of .Comment: Extended abstract to appear in the proceedings of COCOON 201
Minimal Obstructions for Partial Representations of Interval Graphs
Interval graphs are intersection graphs of closed intervals. A generalization
of recognition called partial representation extension was introduced recently.
The input gives an interval graph with a partial representation specifying some
pre-drawn intervals. We ask whether the remaining intervals can be added to
create an extending representation. Two linear-time algorithms are known for
solving this problem.
In this paper, we characterize the minimal obstructions which make partial
representations non-extendible. This generalizes Lekkerkerker and Boland's
characterization of the minimal forbidden induced subgraphs of interval graphs.
Each minimal obstruction consists of a forbidden induced subgraph together with
at most four pre-drawn intervals. A Helly-type result follows: A partial
representation is extendible if and only if every quadruple of pre-drawn
intervals is extendible by itself. Our characterization leads to a linear-time
certifying algorithm for partial representation extension
On strongly chordal graphs that are not leaf powers
A common task in phylogenetics is to find an evolutionary tree representing
proximity relationships between species. This motivates the notion of leaf
powers: a graph G = (V, E) is a leaf power if there exist a tree T on leafset V
and a threshold k such that uv is an edge if and only if the distance between u
and v in T is at most k. Characterizing leaf powers is a challenging open
problem, along with determining the complexity of their recognition. This is in
part due to the fact that few graphs are known to not be leaf powers, as such
graphs are difficult to construct. Recently, Nevries and Rosenke asked if leaf
powers could be characterized by strong chordality and a finite set of
forbidden subgraphs.
In this paper, we provide a negative answer to this question, by exhibiting
an infinite family \G of (minimal) strongly chordal graphs that are not leaf
powers. During the process, we establish a connection between leaf powers,
alternating cycles and quartet compatibility. We also show that deciding if a
chordal graph is \G-free is NP-complete, which may provide insight on the
complexity of the leaf power recognition problem
Partitioning SKA Dataflows for Optimal Graph Execution
Optimizing data-intensive workflow execution is essential to many modern
scientific projects such as the Square Kilometre Array (SKA), which will be the
largest radio telescope in the world, collecting terabytes of data per second
for the next few decades. At the core of the SKA Science Data Processor is the
graph execution engine, scheduling tens of thousands of algorithmic components
to ingest and transform millions of parallel data chunks in order to solve a
series of large-scale inverse problems within the power budget. To tackle this
challenge, we have developed the Data Activated Liu Graph Engine (DALiuGE) to
manage data processing pipelines for several SKA pathfinder projects. In this
paper, we discuss the DALiuGE graph scheduling sub-system. By extending
previous studies on graph scheduling and partitioning, we lay the foundation on
which we can develop polynomial time optimization methods that minimize both
workflow execution time and resource footprint while satisfying resource
constraints imposed by individual algorithms. We show preliminary results
obtained from three radio astronomy data pipelines.Comment: Accepted in HPDC ScienceCloud 2018 Worksho
Labels direct infants’ attention to commonalities during novel category learning
Recent studies have provided evidence that labeling can influence the outcome of infants’ visual categorization. However, what exactly happens during learning remains unclear. Using eye-tracking, we examined infants’ attention to object parts during learning. Our analysis of looking behaviors during learning provide insights going beyond merely observing the learning outcome. Both labeling and non-labeling phrases facilitated category formation in 12-month-olds but not 8-month-olds (Experiment 1). Non-linguistic sounds did not produce this effect (Experiment 2). Detailed analyses of infants’ looking patterns during learning revealed that only infants who heard labels exhibited a rapid focus on the object part successive exemplars had in common. Although other linguistic stimuli may also be beneficial for learning, it is therefore concluded that labels have a unique impact on categorization
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