17,481 research outputs found
Influence of disordered porous media in the anomalous properties of a simple water model
The thermodynamic, dynamic and structural behavior of a water-like system
confined in a matrix is analyzed for increasing confining geometries. The
liquid is modeled by a two dimensional associating lattice gas model that
exhibits density and diffusion anomalies, in similarity to the anomalies
present in liquid water. The matrix is a triangular lattice in which fixed
obstacles impose restrictions to the occupation of the particles. We show that
obstacules shortens all lines, including the phase coexistence, the critical
and the anomalous lines. The inclusion of a very dense matrix not only suppress
the anomalies but also the liquid-liquid critical point
Thermodynamic and Dynamic Anomalies for Dumbbell Molecules Interacting with a Repulsive Ramp-Like Potential
Using collision driven discrete molecular dynamics (DMD), we investigate the
thermodynamics and dynamics of systems of 500 dumbbell molecules interacting by
a purely repulsive ramp-like discretized potential, consisting of steps of
equal size. We compare the behavior of the two systems, with and steps. Each system exhibits both thermodynamic and dynamic anomalies, a
density maximum and the translational and rotational mobilities show anomalous
behavior. Starting with very dense systems and decreasing the density, both
mobilities first increase, reache a maximum, then decrease, reache a minimum,
and finally increase; this behavior is similar to the behavior of SPC/E water.
The regions in the pressure-temperature plane of translational and rotational
mobility anomalies depend strongly on . The product of the translational
diffusion coefficient and the orientational correlation time increases with
temperature, in contrast with the behavior of most liquids
Modeling the input history of programs for improved instruction-memory performance
When a program is loaded into memory for execution, the relative position of
its basic blocks is crucial, since loading basic blocks that are unlikely to be
executed first places them high in the instruction-memory hierarchy only to be
dislodged as the execution goes on. In this paper we study the use of Bayesian
networks as models of the input history of a program. The main point is the
creation of a probabilistic model that persists as the program is run on
different inputs and at each new input refines its own parameters in order to
reflect the program's input history more accurately. As the model is thus
tuned, it causes basic blocks to be reordered so that, upon arrival of the next
input for execution, loading the basic blocks into memory automatically takes
into account the input history of the program. We report on extensive
experiments, whose results demonstrate the efficacy of the overall approach in
progressively lowering the execution times of a program on identical inputs
placed randomly in a sequence of varied inputs. We provide results on selected
SPEC CINT2000 programs and also evaluate our approach as compared to the gcc
level-3 optimization and to Pettis-Hansen reordering
Diffusion anomaly and dynamic transitions in the Bell-Lavis water model
In this paper we investigate the dynamic properties of the minimal Bell-Lavis
(BL) water model and their relation to the thermodynamic anomalies. The
Bell-Lavis model is defined on a triangular lattice in which water molecules
are represented by particles with three symmetric bonding arms interacting
through van der Waals and hydrogen bonds. We have studied the model diffusivity
in different regions of the phase diagram through Monte Carlo simulations. Our
results show that the model displays a region of anomalous diffusion which lies
inside the region of anomalous density, englobed by the line of temperatures of
maximum density (TMD). Further, we have found that the diffusivity undergoes a
dynamic transition which may be classified as fragile-to-strong transition at
the critical line only at low pressures. At higher densities, no dynamic
transition is seen on crossing the critical line. Thus evidence from this study
is that relation of dynamic transitions to criticality may be discarded
Using Holographically Compressed Embeddings in Question Answering
Word vector representations are central to deep learning natural language
processing models. Many forms of these vectors, known as embeddings, exist,
including word2vec and GloVe. Embeddings are trained on large corpora and learn
the word's usage in context, capturing the semantic relationship between words.
However, the semantics from such training are at the level of distinct words
(known as word types), and can be ambiguous when, for example, a word type can
be either a noun or a verb. In question answering, parts-of-speech and named
entity types are important, but encoding these attributes in neural models
expands the size of the input. This research employs holographic compression of
pre-trained embeddings, to represent a token, its part-of-speech, and named
entity type, in the same dimension as representing only the token. The
implementation, in a modified question answering recurrent deep learning
network, shows that semantic relationships are preserved, and yields strong
performance.Comment: 12 pages, 6 figures, 1 table, 9th International Conference on
Advanced Information Technologies and Applications (ICAITA 2020), July 11~12,
2020, Toronto, Canada, Advanced Natural Language Processing Sub-Conference
(AdNLP 2020
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