An Enhancement of Futures Runtime in Presence of Cache Memory Hierarchy

A future is a simple abstraction mechanism for exposing potential concurrency in programs. In this paper, we propose an enhancement of our previously developed runtime for scheduling and executing futures based on the lazy task creation technique that aims to reflect the cache memory hierarchy present in modern multi-core and multiprocessor systems

Constrained Monotonic Neural Networks

Wider adoption of neural networks in many critical domains such as finance and healthcare is being hindered by the need to explain their predictions and to impose additional constraints on them. Monotonicity constraint is one of the most requested properties in real-world scenarios and is the focus of this paper. One of the oldest ways to construct a monotonic fully connected neural network is to constrain signs on its weights. Unfortunately, this construction does not work with popular non-saturated activation functions as it can only approximate convex functions. We show this shortcoming can be fixed by constructing two additional activation functions from a typical unsaturated monotonic activation function and employing each of them on the part of neurons. Our experiments show this approach of building monotonic neural networks has better accuracy when compared to other state-of-the-art methods, while being the simplest one in the sense of having the least number of parameters, and not requiring any modifications to the learning procedure or post-learning steps. Finally, we prove it can approximate any continuous monotone function on a compact subset of $\mathbb{R}^n$

Umjetna inteligencija i primjene u dijagnostici

Tehnika dubokog učenja neuronskih mreža pokrenula je pravu revoluciju u izradi algoritama koji imaju podjednaku ili čak bolju točnost od ljudskih stručnjaka na čitavom nizu kognitivnih audio, vizualnih i tekstualnih zadataka te ovaj nagli napredak nazivamo i novo proljeće umjetne inteligencije. Glavna karakteristika ove tehnike je potpuna nezavisnost od domene u kojoj se primjenjuje, tako da se isti algoritam primjenjuje kod prepoznavanja mačaka na slikama s interneta kao i prepoznavanja patologija na radiološkim snimkama. U ovom preglednom članku ćemo kratko opisati glavne karakteristike i procese dubokog učenja, te dati jedan primjer dijagnostike pneumonije na osnovi radiološke snimke pluća

Umjetna inteligencija i primjene u dijagnostici

Tehnika dubokog učenja neuronskih mreža pokrenula je pravu revoluciju u izradi algoritama koji imaju podjednaku ili čak bolju točnost od ljudskih stručnjaka na čitavom nizu kognitivnih audio, vizualnih i tekstualnih zadataka te ovaj nagli napredak nazivamo i novo proljeće umjetne inteligencije. Glavna karakteristika ove tehnike je potpuna nezavisnost od domene u kojoj se primjenjuje, tako da se isti algoritam primjenjuje kod prepoznavanja mačaka na slikama s interneta kao i prepoznavanja patologija na radiološkim snimkama. U ovom preglednom članku ćemo kratko opisati glavne karakteristike i procese dubokog učenja, te dati jedan primjer dijagnostike pneumonije na osnovi radiološke snimke pluća

Some Things Algorithms Cannot Do

Abstract. Church–Turing thesis captured the notion of a computable function by defining an envelope containing all functions that could be computed by a mechanical procedure. The intended use of the thesis was negative — it provided a theoretical framework for undecidability results. The ASM thesis captured the notion of algorithm, rather than the function it computes. The intended use of the thesis was positive application — it has been most successful in specification of algorithms. We raise the question can the thesis be used for negative purposes as well. More precisely, we attempt to give a meaningful interpretation to negative abstract results in the context of an ASM model of abstract cryptography. In this paper we develop a theoretical framework for establishing negative result in the general behavioral theory of algorithms enabling such negative applications of the ASM thesis. The algorithms studied are small-step algorithms, non-interactive and ordinary interactive, possibly importing from a background

An Enhancement of Futures Runtime in Presence of Cache Memory Hierarchy

Abstract. A future is a simple abstraction mechanism for exposing potential concurrency in programs. In this paper, we propose an enhancement of our previously developed runtime for scheduling and executing futures based on the lazy task creation technique that aims to reflect the cache memory hierarchy present in modern multi-core and multiprocessor systems