Unsupervised Semantic Discovery Through Visual Patterns Detection

We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail. Through the modeling of what is a visual pattern in an image, we introduce the notion of “semantic levels" and devise a conceptual framework along with measures and a dedicated benchmark dataset for future comparisons. Our algorithm is composed by two phases. A filtering phase, which selects semantical hotsposts by means of an accumulator space, then a clustering phase which propagates the semantic properties of the hotspots on a superpixels basis. We provide both qualitative and quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency. We also made code and dataset publicly available

Correlation between Quality of Life and severity of Parkinson's Disease by assessing an optimal cut-off point on the Parkinson's Disease questionnaire (PDQ-39) as related to the Hoehn & Yahr (H&Y) scale

Purpose: Strong evidence shows that symptoms in individuals with Parkinson's Disease (PD) restrict both their independence and social participation, leading to a low Quality of Life (QoL). Conversely, a reduced QoL has a negative impact on symptoms. The aim is to evaluate the correlation between QoL and severity of PD by assessing the presence of an optimal cut-off point on the Parkinson's disease questionnaire (PDQ-39) as related to the Hoehn &Yahr (H&Y) scale in a cohort of Italian adults with PD. Methods: A multicenter, cross-sectional study was performed. This study was conducted on a cohort of consecutive individuals. All participants were evaluated with the PDQ-39, and the severity of PD was recorded according to the H&Y scale by a neurologist. Receiver op-erating characteristic (ROC) curves and coordinates, visually inspected, were used to find cut-off points with optimal sensitivity and specificity. These were in turn used to determine the optimal PDQ-39 cut-off score for identifying disease severity according to H&Y stages. Results: 513 individuals were included in the study. The ROC curve analysis showed that QoL worsened with an increase in disease severity and age. Moreover, QoL was worse in females. Conclusions: The results of this study allowed for the correlation of QoL and disease severity in a cohort of individuals with PD. With this cut-off point, it is now possible to make a determination of QoL of an individual with PD at a certain stage of the disease, in a specific age range, and of a particular gender

Smaller is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning

The design of machines and algorithms capable of learning in a dynamically changing environment has become an increasingly topical problem with the increase of the size and heterogeneity of data available to learning systems. As a consequence, the key issue of Continual Learning has become that of addressing the stability-plasticity dilemma of connectionist systems, as they need to adapt their model without forgetting previously acquired knowledge. Within this context, rehearsal-based methods i.e., solutions in where the learner exploits memory to revisit past data, has proven to be very effective, leading to performance at state-of-the-art. In our study, we propose an analysis of the memory quantity/quality trade-off adopting various data reduction approaches to increase the number of instances storable in memory. In particular, we investigate complex instance compression techniques such as deep encoders, but also trivial approaches such as image resizing and linear dimensionality reduction. Our findings suggest that the optimal trade-off is severely skewed toward instance quantity, where rehearsal approaches with several heavily compressed instances easily outperform state-of-the-art approaches with the same amount of memory at their disposal. Further, in high memory configurations, deep approaches extracting spatial structure combined with extreme resizing (of the order of 8 × 8 images) yield the best results, while in memory-constrained configurations where deep approaches cannot be used due to their memory requirement in training, a variation of Extreme Learning Machines (ELM) offer a clear advantage. Code and experiments available at https://github.com/francesco-p/smaller-is-bette

Thermal Cycling Effect on the Premartensitic and Martensitic Transition in a Ti Rich NiTi Alloy

The study was carried on a Ti-rich NiTi shape memory alloy. The electrical resistance and the elastic modulus were determined versus temperature. The aim of this work is to study the thermal cycling effect on the transformation features as the temperatures or the phase succession as well as the structural modifications. The major results of this work concern the R-phase occurrence and its evolution. We have shown that the premartensitic phase could exist in a narrow range of temperature on cooling and is revealed only after a few cycles. In fact, its presence and stability is directly linked to the diminishing of Ms. The main condition for the austenite to be transform in a rhombohedral structure is that Ms becomes low enough to be below Tr. Intempted cooling runs also show that contrarily to the austenite-martensite transition, the austenite-R-phase transition doesn't exhibit any hysteretic behaviour

High Temperature Internal Friction in Ni Ti Alloy

Former internal friction studies concerning NiTi alloys are currently limited to the temperature range 300 K-400 K relative to the martensitic transition. This paper describes results obtained by isothermal mechanical spectroscopy between 400 K and 1050 K with a sample in austenitic phase after different thermomechanical treatments : cold rolled, deformed by tension, submitted to successive transition cycles or annealed at high temperature. Two relaxation peaks superimposed to a low frequency (i.e. high temperature) background were found. For the first one (520 K at 1 Hz) both, the variation of the relaxation strength with the measurement temperatures and the relaxation parameters (Ea = 1.35 eV and τo = 2.5x10-14 s) correspond to a Zener relaxation. The high temperature peak (870 K at 1 Hz) was associated with a relaxation due to dislocation segment motion

Martensitic Phase in NiTi and CuAlNi Studied by Isothermal Mechanical Spectroscopy

Internal friction has been measured in isothermal conditions over a large frequency range (10-3 Hz-10 Hz) in NiTi (49.6%Ni) and CuAlNi (15% Al, 3%Ni) alloys. After convenient thermal treatments, the samples present a stable martensitic phase between room temperature and ≈350K for NiTi and ≈873K for CuAlNi. We have found an increase of Q-1 at very low frequencies which depend on temperature. The activation energy deduced from different spectra is 0.85 eV for NiTi and 0.45 eV for CuAlNi. Other experiments, performed on a 6% strained NiTi sample, have evidenced a decrease Ea. From these results we have assumed that the damping increase could be associated to the interface motion of the martemite variants. In other words, the high density of the variants in a non strained sample could inhibit their displacements

Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization

In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) - while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners.