To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

Central Neuropathic Pain in a Patient with Multiple Sclerosis Treated Successfully with Topical Amitriptyline

Central neuropathic pain in patients with multiple sclerosis (MS) is a common debilitating symptom, which is mostly treated with tricyclic antidepressants or antiepileptics. Unfortunately, the use of these drugs is often limited due to adverse events. We investigated the analgesic effect of topical amitriptyline 5% and 10% cream in a patient with central neuropathic pain due to MS. The analgesic effect of topical amitriptyline cream on neuropathic pain was dose related. To evaluate whether this analgesic effect is due to the active compound or placebo, we conducted a double-blind placebo-controlled n-of-1 study with amitriptyline 5% cream and placebo. The instruction was to alternate the creams every week following the pattern ABAB, with an escape possibility of amitriptyline 10% cream. The result was a complete pain reduction after application of cream B, while most of the time cream A did not reduce the pain. The patient could correctly unblind both creams, determining B as active. She noted that in the week of using the active cream no allodynia was present, with a carryover effect of one day

Deciphering Entrepreneurial Pitches: A Multimodal Deep Learning Approach to Predict Probability of Investment

Acquiring early-stage investments for the purpose of developing a business is a fundamental aspect of the entrepreneurial process, which regularly entails pitching the business proposal to potential investors. Previous research suggests that business viability data and the perception of the entrepreneur play an important role in the investment decision-making process. This perception of the entrepreneur is shaped by verbal and non-verbal behavioral cues produced in investor-entrepreneur interactions. This study explores the impact of such cues on decisions that involve investing in a startup on the basis of a pitch. A multimodal approach is developed in which acoustic and linguistic features are extracted from recordings of entrepreneurial pitches to predict the likelihood of investment. The acoustic and linguistic modalities are represented using both hand-crafted and deep features. The capabilities of deep learning models are exploited to capture the temporal dynamics of the inputs. The findings show promising results for the prediction of the likelihood of investment using a multimodal architecture consisting of acoustic and linguistic features. Models based on deep features generally outperform hand-crafted representations. Experiments with an explainable model provide insights about the important features. The most predictive model is found to be a multimodal one that combines deep acoustic and linguistic features using an early fusion strategy and achieves an MAE of 13.91

Alcohol dose in septal ablation for hypertrophic obstructive cardiomyopathy

Background: The aim of this study was to evaluate short- and long-term outcomes related to dose of alcohol administered during alcohol septal ablation (ASA) in patients with hypertrophic obstructive cardiomyopathy (HOCM). Current guidelines recommend using 1–3 mL of alcohol administered in the target septal perforator artery, but this recommendation is based more on practical experience of interventionalists rather than on systematic evidence. Methods: We included 1448 patients and used propensity score to match patients who received a low-dose (1.0–1.9 mL) versus a high-dose (2.0–3.8 mL) of alcohol during ASA. Results: The matched cohort analysis comprised 770 patients (n = 385 in both groups). There was a similar occurrence of 30-day post-procedural adverse events (13% vs. 12%; p = 0.59), and similar all-cause mortality rates (0.8% vs. 0.5%; p = 1) in the low-dose group and the high-dose group, respectively. In the long-term follow-up (5.4 ± 4.5 years), a total of 110 (14%) patients died representing 2.58 deaths and 2.64 deaths per 100 patient-years in the low dose and the high dose group (logrank, p = 0.92), respectively. There were no significant differences in the long-term dyspnea and left ventricular outflow gradient between the two groups. Patients treated with a low-dose of alcohol underwent more subsequent septal reduction procedures (logrank, p = 0.04). Conclusions: Matched HOCM patients undergoing ASA with a low-dose (1.0–1.9 mL) or a high-dose (2.0–3.8 mL) of alcohol had similar short- and long-term outcomes. A higher rate of repeated septal reduction procedures was observed in the group treated with a low-dose of alcohol. © 2021 The Author