Learning to learn: From smart machines to intelligent machines

Since its birth, more than five decades ago, one of the biggest challenges of artificial intelligence remained the building of intelligent machines. Despite amazing advancements, we are still far from having machines that reach human intelligence level. The current paper tries to offer a possible explanation of this situation. For this purpose, we make a review of different learning strategies and context types that are involved in the learning process. We also present the results of a study on cognitive development applied to the problem of face recognition for social robotics.     

Cytokine Secretion Dynamics of Isolated PBMC after Cladribine Exposure in RRMS Patients

Cladribine (CLD) treats multiple sclerosis (MS) by selectively and transiently depleting B and T cells with a secondary long-term reconstruction of the immune system. This study provides evidence of CLD’s immunomodulatory role in peripheral blood mononuclear cells (PBMCs) harvested from 40 patients with untreated relapsing-remitting MS (RRMS) exposed to CLD. We quantified cytokine secretion from PBMCs isolated by density gradient centrifugation with Ficoll–Paque using xMAP technology on a FlexMap 3D analyzer with a highly sensitive multiplex immunoassay kit. The PBMC secretory profile was evaluated with and without CLD exposure. PBMCs isolated from patients with RRMS for ≤12 months had significantly higher IL-4 but significantly lower IFN-γ and TNF-α secretion after CLD exposure. PBMCs isolated from patients with RRMS for >12 months had altered inflammatory ratios toward an anti-inflammatory profile and increased IL-4 but decreased TNF-α secretion after CLD exposure. CLD induced nonsignificant changes in IL-17 secretion in both RRMS groups. Our findings reaffirm CLD’s immunomodulatory effect that induces an anti-inflammatory phenotype.     

Texture-independent recognition of facial expressions in image snapshots and videos

This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines.     

Embedding new observations via sparse-coding for non-linear manifold learning

Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data—the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance.     

Inferring competitive role patterns in reality TV show through nonverbal analysis

This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person’s role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority.     

Structure–property relationship of sodium deoxycholate based poly(ester ether)urethane ionomers for biomedical applications

New sodium deoxycholate based poly(ester ether)urethane ionomers were prepared for the development of biomedical materials. A structure–property relationship in the tested biomaterials was established by cross‐examination of the dynamic mechanical and dielectric properties, attenuated total reflection–Fourier transform infrared investigation, thermogravimetric analysis, and surface morphology characterization. A stronger ionic interaction and solvation capacity of the ions and a higher ionic conductivity were manifested in the case of poly(ethylene oxide)‐rich segments than for poly(propylene oxide)‐rich segments in these polyurethane ionomers. The molecular and ionic interactions of the bile‐salt moiety with different polyether cosoft segments influenced chain packing and conformation, supramolecular organization, and the resulting surface morphological microstructures of the polyurethane biomembranes. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42921.     

The Importance of Small Extracellular Vesicles in the Cerebral Metastatic Process

Brain metastases represent more than 50% of all cerebral tumors encountered in clinical practice. Recently, there has been increased interest in the study of extracellular vesicles, and the knowledge about exosomes is constantly expanding. Exosomes are drivers for organotropic metastatic spread, playing important roles in the brain metastatic process by increasing the permeability of the blood–brain barrier and preparing the premetastatic niche. The promising results of the latest experimental studies raise the possibility of one day using exosomes for liquid biopsies or as drug carriers, contributing to early diagnosis and improving the efficacy of chemotherapy in patients with brain metastases. In this review, we attempted to summarize the latest knowledge about the role of exosomes in the brain metastatic process and future research directions for the use of exosomes in patients suffering from brain metastatic disease.     

Organochlorine pesticides in colostrums in case of normal and preterm labor (IASI, Romania)

Assessment of organochlorine pesticides (OCPs) in human body is important for human health because they have weak estrogenic or antiestrogenic effects and are considered endocrine disrupters. We used colostrum of women as indicator for levels of OCPs in human body for mothers with normal and preterm labor from eastern part of Romania. Sixty- three samples of colostrum were extracted by solid-phase extraction. Analyses were carried out using gas chromatography coupled to mass spectrometry (GC-MS). OCPs have been detected in all samples, with p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) and γ-hexachlorocyclohexane (γ-HCH) being at the highest concentrations. Of the organochlorines measured in clostrum samples from women in preterm labor, median levels of DDTs (470 ng/g) and HCHs (99 ng/g) were higher than for the same compounds from women in normal labor (median of DDTs = 268 ng/g and median of HCHs = 96 ng/g). Normal labor had higher median concentrations of HCB (19.5 ng/g) versus preterm labor (14 ng/g). Statistical data show high Spearman correlation coefficients between various OCPs. We found a good correlation between α-, γ-, β- and δ- HCH isomers (p < 0.001) for both normal and preterm labor. The most abundant target compound was p,p’-DDE (median value 96 ng/g, and 137 ng/g for mother with normal and preterm labor, respectively) in all colostrum samples. The estimated daily intakes of HCHs by infants exceeded corresponding Health Canada guidelines.     

Classification Using Φ-Machines and Constructive Function Approximation

This article presents a new classification algorithm, called CLEF, which induces a -machine by constructing its own features based on the training data. The features can be viewed as defining subsets of the instance space, and they allow CLEF to create useful non-linear functions over the input variables. The algorithm is guaranteed to find a classifier that separates the training instances, if such a separation is possible. We compare CLEF empirically to several other classification algorithms, including a well-known decision tree inducer, an artificial neural network inducer, and a support vector machine inducer. Our results show that the CLEF-induced -machines and support vector machines have similar accuracy on the suite tested, and that both are significantly more accurate than the other classifiers produced. We argue that the classifiers produced by CLEF are easy to interpret, and hence may be preferred over support vector machines in certain circumstances.