Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

Probabilistic graphical models are a central tool in AI; however, they are generally not as expressive as deep neural models, and inference is notoriously hard and slow. In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks. Therefore, we introduce conditional SPNs (CSPNs), conditional density estimators for multivariate and potentially hybrid domains which allow harnessing the expressive power of neural networks while still maintaining tractability guarantees. One way to implement CSPNs is to use an existing SPN structure and condition its parameters on the input, e.g., via a deep neural network. This approach, however, might misrepresent the conditional independence structure present in data. Consequently, we also develop a structure-learning approach that derives both the structure and parameters of CSPNs from data. Our experimental evidence demonstrates that CSPNs are competitive with other probabilistic models and yield superior performance on multilabel image classification compared to mean field and mixture density networks. Furthermore, they can successfully be employed as building blocks for structured probabilistic models, such as autoregressive image models.Comment: 13 pages, 6 figure

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal

Carbon nanotube multilayered nanocomposites as multifunctional substrates for actuating neuronal differentiation and functions of neural stem cells

Carbon nanotubes (CNTs) have shown potential applications in neuroscience as growth substrates owing to their numerous unique properties. However, a key concern in the fabrication of homogeneous composites is the serious aggregation of CNTs during incorporation into the biomaterial matrix. Moreover, the regulation mechanism of CNT-based substrates on neural differentiation remains unclear. Here, a novel strategy was introduced for the construction of CNT nanocomposites via layer-by-layer assembly of negatively charged multi-walled CNTs and positively charged poly(dimethyldiallylammonium chloride). Results demonstrated that the CNT-multilayered nanocomposites provided a potent regulatory signal over neural stem cells (NSCs), including cell adhesion, viability, differentiation, neurite outgrowth, and electrophysiological maturation of NSC-derived neurons. Importantly, the dynamic molecular mechanisms in the NSC differentiation involved the integrin-mediated interactions between NSCs and CNT multilayers, thereby activating focal adhesion kinase, subsequently triggering downstream signaling events to regulate neuronal differentiation and synapse formation. This study provided insights for future applications of CNT-multilayered nanomaterials in neural fields as potent modulators of stem cell behavior

Long-term outcomes of radiofrequency ablation vs. partial nephrectomy for cT1 renal cancer: A meta-analysis and systematic review

BackgroundPartial nephrectomy (PN) is one of the most preferred nephron-sparing treatments for clinical T1 (cT1) renal cancer, while radiofrequency ablation (RFA) is usually used for patients who are poor surgical candidates. The long-term oncologic outcome of RFA vs. PN for cT1 renal cancer remains undetermined. This meta-analysis aims to compare the treatment efficacy and safety of RFA and PN for patients with cT1 renal cancer with long-term follow-up of at least 5 years.MethodThis meta-analysis was performed following the PRISMA reporting guidelines. Literature studies that had data on the comparison of the efficacy or safety of RFA vs. PN in treating cT1 renal cancer were searched in databases including PubMed, Embase, Web of Science, and the Cochrane Library from 1 January2000 to 1 May 2022. Only long-term studies with a median or mean follow-up of at least 5 years were included. The following measures of effect were pooled: odds ratio (OR) for recurrence and major complications; hazard ratio (HR) for progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS). Additional analyses, including sensitivity analysis, subgroup analysis, and publication bias analysis, were also performed.ResultsA total of seven studies with 1,635 patients were finally included. The treatment efficacy of RFA was not different with PN in terms of cancer recurrence (OR = 1.22, 95% CI, 0.45–3.28), PFS (HR = 1.26, 95% CI, 0.75–2.11), and CSS (HR = 1.27, 95% CI, 0.41–3.95) as well as major complications (OR = 1.31, 95% CI, 0.55–3.14) (P > 0.05 for all). RFA was a potential significant risk factor for OS (HR = 1.76, 95% CI, 1.32–2.34, P < 0.001). No significant heterogeneity and publication bias were observed.ConclusionThis is the first meta-analysis that focuses on the long-term oncological outcomes of cT1 renal cancer, and the results suggest that RFA has comparable therapeutic efficacy with PN. RFA is a nephron-sparing technique with favorable oncologic efficacy and safety and a good treatment alternative for cT1 renal cancer

The effective on intradermal acupuncture based on changes in biological specificity of acupoints for major depressive disorder: study protocol of a prospective, multicenter, randomized, controlled trial

BackgroundAntidepressants still have some side effects in treating major depressive disorder (MDD), and acupuncture therapy is a complementary therapy of research interest for MDD. Acupoints are sensitive sites for disease response and stimulation points for acupuncture treatment. Prior studies suggest that the biological specificity of acupoints is altered in physiological and pathological situations. Therefore, we hypothesize that the biological specificity of acupoints is associated with the diagnosis of MDD and that stimulating acupoints with significant biological specificity can achieve a better therapeutic effect than clinical common acupoints. This study aims to investigate the efficacy and safety of intradermal acupuncture (IA) treatment for MDD based on changes in the biological specificity of acupoints.MethodsThe first part of the study will enroll 30 MDD patients and 30 healthy control (HC) participants to assess pain sensitivity and thermal specificity of MDD-related acupoints using a pressure pain threshold gauge (PTG) and infrared thermography (IRT). The potentially superior acupoints for treating MDD will be selected based on the results of PTG and IRT tests and referred to as pressure pain threshold strong response acupoints (PSA) and temperature strong response acupoints (TSA).The second part of the study will enroll 120 eligible MDD patients randomly assigned to waiting list (WL) group, clinical common acupoint (CCA) group, TSA group, and PSA group in a 1:1:1:1 ratio. The change in the Patient Health Questionnaire-9 Items (PHQ-9), the MOS item short-form health survey (SF-36), pressure pain threshold, temperature of acupoints, and adverse effects will be observed. The outcomes of PHQ-9 and SF-36 measures will be assessed before intervention, at 3 and 6 weeks after intervention, and at a 4-week follow-up. The biological specificity of acupoint measures will be assessed before intervention and at 6 weeks after intervention. All adverse effects will be assessed.DiscussionThis study will evaluate the therapeutic effect and safety of IA for MDD based on changes in the biological specificity of acupoints. It will investigate whether there is a correlation between the biological specificity of MDD-related acupoints and the diagnosis of MDD and whether stimulating strong response acupoints is superior to clinical common acupoints in the treatment of MDD. The study’s results may provide insights into the biological mechanisms of acupuncture and its potential as a complementary therapy for MDD.Clinical Trial RegistrationClinicalTrials.gov, identifier: NCT05524519

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Explaining and Interactively Debugging Deep Models

Artificial Intelligence (AI) has made a huge impact on our everyday lives. As a dominant branch of AI since the 1990s, Machine Learning (ML) has been applied to a wide range of scenarios, including image recognition, speech recognition, fraud detection, recommendation systems, time series prediction and self-driving cars. Deep learning, backed up by Deep Neural Networks (DNNs), is a major subfield of machine learning. DNNs are good at approximating smooth functions, i.e., learning a mapping from inputs to outputs, which is also known as the predictive or supervised learning approach. Sometimes, one is not interested in a specific predictive task, but rather in finding interesting patterns in the data. In this case, a descriptive or unsupervised learning approach is needed, and the task can be formalized as density estimation. Deep probabilistic models have gained popularity for density estimation because they maintain a good balance between expressivity and tractability, whereas classical probabilistic models face an inherent trade-off. Deep neural networks and deep probabilistic models are both deep models in the sense that they are composed of multiple layers of computation units. They are essentially computation graphs and consequently, it is hard for humans to understand the underlying decision logic behind their behavior. Despite the representational and predictive power deep models have demonstrated in many complex problems, their opaqueness is a common reason for concern. In this thesis, we provide insights into deep models using high-level interpretations and explanations of why particular decisions are made. Explanations that contradict our intuitions or prior knowledge on the underlying domain can expose a potential concern, which may imply some desiderata of ML systems are not met. For example, a deep model may obtain high predictive accuracy by exploiting a spurious correlation in the dataset, which can lead to a lack of robustness, or unfairness if the spurious correlation is linked to a protected attribute. Built on the framework of Explanatory Interactive Machine Learning (XIL), we propose to interactively improve deep models based on the explanations we get. This way, we put users in the training loop and take user feedback on explanations as additional training signals. As an effect, the model can learn the rules that align with our intuitions or prior knowledge

Microencapsulation of Algal Oil Using Spray Drying Technology

This work aims at developing a process of microencapsulation of algal oil containing ≥40 % docosahexaenoic acid (DHA) using spray drying technology. Purity Gum® 2000 and Capsul®, both obtained from waxy corn starch, were chosen as the encapsulation materials. The effects of emulsification conditions on the droplet size, stability, viscosity and surface tension, and the effects of spraying conditions on the particle size, moisture content and surface oil content were investigated successively. The morphology of emulsion droplets and the microcapsules was observed by optical microscope and scanning electron micro scopy. The results showed that the produced spherical microcapsules were smooth and free of pores, cracks, and surface indentation when shear velocity was 8.63 m/s in the first step of emulsification, homogenization pressure was 1.75·10˄8 Pa and number of passes through homogenization unit was six for fine emulsification, rotational speed of spray disk was 400 s-1, and air inlet temperature was 170 °C. Therefore, it was concluded that the emulsification and encapsulation of algal oil containing DHA with above process was feasible