Opportunities and challenges for using automatic human affect analysis in consumer research

The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions, and in naturalistic choice settings. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as a starting point for further analyses. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research

Unreflected Acceptance -- Investigating the Negative Consequences of ChatGPT-Assisted Problem Solving in Physics Education

Large language models (LLMs) have recently gained popularity. However, the impact of their general availability through ChatGPT on sensitive areas of everyday life, such as education, remains unclear. Nevertheless, the societal impact on established educational methods is already being experienced by both students and educators. Our work focuses on higher physics education and examines problem solving strategies. In a study, students with a background in physics were assigned to solve physics exercises, with one group having access to an internet search engine (N=12) and the other group being allowed to use ChatGPT (N=27). We evaluated their performance, strategies, and interaction with the provided tools. Our results showed that nearly half of the solutions provided with the support of ChatGPT were mistakenly assumed to be correct by the students, indicating that they overly trusted ChatGPT even in their field of expertise. Likewise, in 42% of cases, students used copy & paste to query ChatGPT -- an approach only used in 4% of search engine queries -- highlighting the stark differences in interaction behavior between the groups and indicating limited reflection when using ChatGPT. In our work, we demonstrated a need to (1) guide students on how to interact with LLMs and (2) create awareness of potential shortcomings for users.Comment: Pre-print currently under revie

Visualizing the ultra-structure of microorganisms using table-top extreme ultraviolet imaging

Table-top extreme ultraviolet (EUV) microscopy offers unique opportunities for label-free investigation of biological samples. Here, we demonstrate ptychographic EUV imaging of two dried, unstained model specimens: germlings of a fungus (Aspergillus nidulans), and bacteria (Escherichia coli) cells at 13.5 nm wavelength. We find that the EUV spectral region, which to date has not received much attention for biological imaging, offers sufficient penetration depths for the identification of intracellular features. By implementing a position-correlated ptychography approach, we demonstrate a millimeter-squared field of view enabled by infrared illumination combined with sub-60 nm spatial resolution achieved with EUV illumination on selected regions of interest. The strong element contrast at 13.5 nm wavelength enables the identification of the nanoscale material composition inside the specimens. Our work will advance and facilitate EUV imaging applications and enable further possibilities in life science

Accumulated coercion and short-term outcome of inpatient psychiatric care

<p>Abstract</p> <p>Background</p> <p>The knowledge of the impact of coercion on psychiatric treatment outcome is limited. Multiple measures of coercion have been recommended. The aim of the study was to examine the impact of accumulated coercive incidents on short-term outcome of inpatient psychiatric care</p> <p>Methods</p> <p>233 involuntarily and voluntarily admitted patients were interviewed within five days of admission and at discharge or after maximum three weeks of care. Coercion was measured as number of coercive incidents, i.e. subjectively reported and in the medical files recorded coercive incidents, including legal status and perceived coercion at admission, and recorded and reported coercive measures during treatment. Outcome was measured both as subjective improvement of mental health and as improvement in professionally assessed functioning according to GAF. Logistic regression analyses were performed with patient characteristics and coercive incidents as independent and the two outcome measures as dependent variables</p> <p>Results</p> <p>Number of coercive incidents did not predict subjective or assessed improvement. Patients having other diagnoses than psychoses or mood disorders were less likely to be subjectively improved, while a low GAF at admission predicted an improvement in GAF scores</p> <p>Conclusion</p> <p>The results indicate that subjectively and professionally assessed mental health short-term outcome of acute psychiatric hospitalisation are not predicted by the amount of subjectively and recorded coercive incidents. Further studies are needed to examine the short- and long-term effects of coercive interventions in psychiatric care.</p

Epidermal Transglutaminase (TGase 3) Is Required for Proper Hair Development, but Not the Formation of the Epidermal Barrier

Transglutaminases (TGase), a family of cross-linking enzymes present in most cell types, are important in events as diverse as cell-signaling and matrix stabilization. Transglutaminase 1 is crucial in developing the epidermal barrier, however the skin also contains other family members, in particular TGase 3. This isoform is highly expressed in the cornified layer, where it is believed to stabilize the epidermis and its reduction is implicated in psoriasis. To understand the importance of TGase 3 in vivo we have generated and analyzed mice lacking this protein. Surprisingly, these animals display no obvious defect in skin development, no overt changes in barrier function or ability to heal wounds. In contrast, hair lacking TGase 3 is thinner, has major alterations in the cuticle cells and hair protein cross-linking is markedly decreased. Apparently, while TGase 3 is of unique functional importance in hair, in the epidermis loss of TGase 3 can be compensated for by other family members

Gender differences in coerced patients with schizophrenia

European Commission (Quality of life and Management of Living Resources Programme, contract number QLG4-CT- 2002-01036), Czech Ministry of Education research grant MSM002160849, and research grants PRVOUK–P26/LF1/4 and PRVOUK–P03/LF1/

Predicting altcoin returns using social media.

Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called "altcoins" often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter

Concept and Design of a Bearingless Spinfilter

In many separation processes, filtration performance degrades over time due to retained particles blocking the flow through the filter membrane. A novel bearingless spinfilter extends the long-term performance by self-cleaning effects. The filter rotor is magnetically levitated and actuated by two self-bearing motors inside a hermetically sealed housing, which eliminates the need for bearings and rotary sealings, that both lead to process fluid contamination. Both bearingless motors have integrated electronics and independently control the levitation of the spinfilter rotor. A first prototype is designed and the concept is validated by the separation of a yeast cell culture. Special focus is placed on the internal rotary seal between the feed and filtrate regions, that is inevitably created when the filter membrane is in motion. Any leakage flow through the seal leads to filtrate impurities, which is minimized in this article with an embedded impeller as a pressure compensation method. A constant filtrate flux of 1750 Lh⁻¹m⁻² and a filtrate purity of 75% was achieved in a first series of tests

Opportunities and challenges for using automatic human affect analysis in consumer research

The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as promising input for two-step machine learning in combination with other (multimodal) features. We illustrate this point in a case study using facial activity as input features to predict crying behavior in response to sad movies. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research