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 $n$ steps of equal size. We compare the behavior of the two systems, with $n = 18$ and $n = 144$ 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 $n$. 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