New elementary components of the Gorenstein locus of the Hilbert scheme of points

We construct new explicit examples of nonsmoothable Gorenstein algebras with Hilbert function $(1,n,n,1)$. This gives a new infinite family of elementary components in the Gorenstein locus of the Hilbert scheme of points and solves the cubic case of Iarrobino's conjecture.Comment: 18 page

SURVEYING IN THE STUDIES OF THE STABILITY OF EARTHY CONSTRUCTIONS, FOCUS ON SELECTED HISTORICAL MOUNDS IN KRAKOW (POLAND)

Mounds, as anthropogenic constructions of a very delicate structure, are subdued to constant changes, which, due to the impact of external factors (prolonged precipitation, tremors) are subdued to deformations in the form of mass movements. These phenomena usually have the character of mild soil creep in time and sometimes, as a result of rapid loss of stability, they are seriously damaged by landslide. This phenomenon causes temporary exclusion of the object from use. In the framework of the protection of these objects, the maintenance was carried out within the preventive measures referring to the construction and surveying monitoring of the geometry changes in time, as a result of phenomena taking place in the ground medium under the influence of environmental factors causing strains. The process of the deformation of mounds is similar to the characteristic, according to the Terzagie’s theory. The application of surveying technologies of high precision allows the monitoring of changes in their geometry in time. The properly defined study area and the proper selection of measurement technology in the aspect of the accuracy of the prediction of changes, can efficiently help in defining the scale of deformations in the decisive process referring to the way of efficient protection of barrows. The article presents the results of point monitoring carried out with surveying technologies within 11 measurement series carried out on the selected measurement base of the Wanda Mound. The use of measurement technologies of integrated and specialist software, allows complex assessment of the degree of deformation and the trends of these changes in time, as well as identifying anomaly zones in the framework of the landslide monitorin

Elasticity of disordered binary crystals

The properties of crystals consisting of several components can be widely tuned. Often solid solutions are produced, where substitutional or interstitional disorder determines the crystal thermodynamic and mechanical properties. The chemical and structural disorder impedes the study of the elasticity of such solid solutions, since standard procedures like potential expansions cannot be applied. We present a generalization of a density-functional based approach recently developed for one-component crystals to multi-component crystals. It yields expressions for the elastic constants valid in solid solutions with arbitrary amounts of point defects and up to the melting temperature. Further, both acoustic and optical phonon eigenfrequencies can be computed in linear response from the equilibrium particle densities and established classical density functionals. As a proof of principle, dispersion relations are computed for two different binary crystals: A random fcc crystal as an example for a substitutional, and a disordered sodium chloride structure as an example of an interstitial solid solution. In cases where one of the components couples only weakly to the others, the dispersion relations develop characteristic signatures. The acoustic branches become flat in much of the first Brillouin zone, and a crossover between acoustic and optic branches takes place at a wavelength which can far exceed the lattice spacing.

A Python library for efficient computation of molecular fingerprints

Machine learning solutions are very popular in the field of chemoinformatics, where they have numerous applications, such as novel drug discovery or molecular property prediction. Molecular fingerprints are algorithms commonly used for vectorizing chemical molecules as a part of preprocessing in this kind of solution. However, despite their popularity, there are no libraries that implement them efficiently for large datasets, utilizing modern, multicore architectures. On top of that, most of them do not provide the user with an intuitive interface, or one that would be compatible with other machine learning tools. In this project, we created a Python library that computes molecular fingerprints efficiently and delivers an interface that is comprehensive and enables the user to easily incorporate the library into their existing machine learning workflow. The library enables the user to perform computation on large datasets using parallelism. Because of that, it is possible to perform such tasks as hyperparameter tuning in a reasonable time. We describe tools used in implementation of the library and asses its time performance on example benchmark datasets. Additionally, we show that using molecular fingerprints we can achieve results comparable to state-of-the-art ML solutions even with very simple models.Comment: 56 page

A High-Frequency Load-Store Queue with Speculative Allocations for High-Level Synthesis

Dynamically scheduled high-level synthesis (HLS) enables the use of load-store queues (LSQs) which can disambiguate data hazards at circuit runtime, increasing throughput in codes with unpredictable memory accesses. However, the increased throughput comes at the price of lower clock frequency and higher resource usage compared to statically scheduled circuits without LSQs. The lower frequency often nullifies any throughput improvements over static scheduling, while the resource usage becomes prohibitively expensive with large queue sizes. This paper presents a method for achieving dynamically scheduled memory operations in HLS without significant clock period and resource usage increase. We present a novel LSQ based on shift-registers enabled by the opportunity to specialize queue sizes to a target code in HLS. We show a method to speculatively allocate addresses to our LSQ, significantly increasing pipeline parallelism in codes that could not benefit from an LSQ before. In stark contrast to traditional load value speculation, we do not require pipeline replays and have no overhead on misspeculation. On a set of benchmarks with data hazards, our approach achieves an average speedup of 11$\times$ against static HLS and 5$\times$ against dynamic HLS that uses a state of the art LSQ from previous work. Our LSQ also uses several times fewer resources, scaling to queues with hundreds of entries, and supports both on-chip and off-chip memory.Comment: To appear in the International Conference on Field Programmable Technology (FPT'23), Yokohama, Japan, 11-14 December 202

Compiler Discovered Dynamic Scheduling of Irregular Code in High-Level Synthesis

Dynamically scheduled high-level synthesis (HLS) achieves higher throughput than static HLS for codes with unpredictable memory accesses and control flow. However, excessive dataflow scheduling results in circuits that use more resources and have a slower critical path, even when only a part of the circuit exhibits dynamic behavior. Recent work has shown that marking parts of a dataflow circuit for static scheduling can save resources and improve performance (hybrid scheduling), but the dynamic part of the circuit still bottlenecks the critical path. We propose instead to selectively introduce dynamic scheduling into static HLS. This paper presents an algorithm for identifying code regions amenable to dynamic scheduling and shows a methodology for introducing dynamically scheduled basic blocks, loops, and memory operations into static HLS. Our algorithm is informed by modulo-scheduling and can be integrated into any modulo-scheduled HLS tool. On a set of ten benchmarks, we show that our approach achieves on average an up to 3.7$\times$ and 3$\times$ speedup against dynamic and hybrid scheduling, respectively, with an area overhead of 1.3$\times$ and frequency degradation of 0.74$\times$ when compared to static HLS.Comment: To appear in the 33rd International Conference on Field-Programmable Logic and Applications (2023