Efficiently Computing Minimal Sets of Critical Pairs

In the computation of a Gr"obner basis using Buchberger's algorithm, a key issue for improving the efficiency is to produce techniques for avoiding as many unnecessary critical pairs as possible. A good solution would be to avoid _all_ non-minimal critical pairs, and hence to process only a_minimal_ set of generators of the module generated by the critical syzygies. In this paper we show how to obtain that desired solution in the homogeneous case while retaining the same efficiency as with the classical implementation. As a consequence, we get a new Optimized Buchberger Algorithm.Comment: LaTeX using elsart.cls, 27 page

Computing minimal finite free resolutions

AbstractIn this paper we address the basic problem of computing minimal finite free resolutions of homogeneous submodules of graded free modules over polynomial rings. We develop a strategy, which keeps the resolution minimal at every step. Among the relevant benefits is a marked saving of time, as the first reported experiments in CoCoA show. The algorithm has been optimized using a variety of techniques, such as minimizing the number of critical pairs and employing an “ad hoc” Hilbert-driven strategy. The algorithm can also take advantage of various a priori pieces of information, such as the knowledge of the Castelnuovo regularity

Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world. To date, all proposed approaches rely on human expertise to manually adapt a given UDA method (e.g. DANN) to a specific backbone architecture (e.g. ResNet). This dependency on handcrafted designs limits the applicability of a given approach in time, as old methods need to be constantly adapted to novel backbones.Existing Neural Architecture Search (NAS) approaches cannot be directly applied to mitigate this issue, as they rely on labels that are not available in the UDA setting. Furthermore, most NAS methods search for full architectures, which precludes the use of pre-trained models, essential in a vast range of UDA settings for reaching SOTA results.To the best of our knowledge, no prior work has addressed these aspects in the context of NAS for UDA. Here we tackle both aspects with an Adversarial Branch Architecture Search for UDA (ABAS): i. we address the lack of target labels by a novel data-driven ensemble approach for model selection; and ii. we search for an auxiliary adversarial branch, attached to a pre-trained backbone, which drives the domain alignment. We extensively validate ABAS to improve two modern UDA techniques, DANN and ALDA, on three standard visual recognition datasets (Office31, Office-Home and PACS). In all cases, ABAS robustly finds the adversarial branch architectures and parameters which yield best performances. https://github.com/lr94/abas

Fast Reduction of Bivariate Polynomials with Respect to Sufficiently Regular Gröbner Bases

International audienc

Unsupervised Domain Adaptation through Inter-Modal Rotation for RGB-D Object Recognition

Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions. In robotics, DA is used to take advantage of automatically generated synthetic data, that come with 'free' annotation, to make effective predictions on real data. However, existing DA methods are not designed to cope with the multi-modal nature of RGB-D data, which are widely used in robotic vision. We propose a novel RGB-D DA method that reduces the synthetic-to-real domain shift by exploiting the inter-modal relation between the RGB and depth image. Our method consists of training a convolutional neural network to solve, in addition to the main recognition task, the pretext task of predicting the relative rotation between the RGB and depth image. To evaluate our method and encourage further research in this area, we define two benchmark datasets for object categorization and instance recognition. With extensive experiments, we show the benefits of leveraging the inter-modal relations for RGB-D DA. The code is available at: 'https://github.com/MRLoghmani/relative-rotation'

Co-Occurring Psychiatric and Substance Use Disorders: Clinical Survey Among a Rural Cohort of Italian Patients

Purpose: Dual diagnosis (DD) is the co-occurrence of both a mental illness and a substance use disorder (SUD). Lots of studies have analysed the integrated clinical approach, which involves both psychiatry and toxicology medical experts. The purpose of this study is to analyse the socio-demographic characteristics and treatment strategies of patients with DD in a rural area of Italy. Patients and Methods: Clinical data of 750 patients were collected in 2016 through the analysis of health plan records. Results: The rate of co-occurring disorders is highly variable among people with SUD. In the considered area, patients with DD are 24%, of these only 46.1% have been treated with an integrated clinical program. Moreover, this percentage is further reduced (35.8%) if only patients with heroin use disorder are considered. Conclusion: A comprehensive revision of DD treatment is needed, especially for people suffering from heroin use disorder and living in remote areas. Meticulous data analysis from other addiction health services of rural areas could be necessary to identify a science-based clinical intervention

Ionic Strength Responsive Sulfonated Polystyrene Opals

Stimuli-responsive photonic crystals (PCs) represent an intriguing class of smart materials very promising for sensing applications. Here, selective ionic strength responsive polymeric PCs are reported. They are easily fabricated by partial sulfonation of polystyrene opals, without using toxic or expensive monomers and etching steps. The color of the resulting hydrogel-like ordered structures can be continuously shifted over the entire visible range (405-760 nm) by changing the content of ions over an extremely wide range of concentration (from about 70 μM to 4 M). The optical response is completely independent from pH and temperature, and the initial color can be fully recovered by washing the sulfonated opals with pure water. These new smart photonic materials could find important applications as ionic strength sensors for environmental monitoring as well as for healthcare screening