Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Cognitive Abilities in High-Performing Cochlear Implant Users

Despite being considered one of the most successful neural prostheses, cochlear implants (CIs) provide recipients with a wide range of speech perception performance. While some CI users can understand speech in the absence of visual cues, other recipients exhibit more limited speech perception. Cognitive skills have been documented as a contributor to complex auditory processing, such as language understanding; however, there are no normative data for existing standardized clinical tests assessing cognitive abilities in CI users. Here, we assess the impact of modality of presentation (i.e., auditory-visual versus visual) for the administration of working memory tests in high-performing CI users in addition to measuring processing speed, cognitive efficiency and intelligence quotient (IQ). Second, we relate performance on these cognitive measures to clinical CI speech perception outcomes. Methods: Twenty one post-lingually deafened, high-performing, adult CI users [age range: 52–88 years; 3 unilateral CI, 13 bimodal (i.e., CI with contralateral hearing aid), 5 bilateral CI] with clinical speech perception scores (i.e., AzBio sentences in quiet for the first-ear CI) of ≥60% were recruited. A cognitive test battery assessing auditory-visual working memory (AVWM), visual working memory (VWM), processing speed, cognitive efficiency and IQ was administered, in addition to clinical measures of speech perception in quiet (i.e., AzBio sentences in quiet). AzBio sentences were assessed in two conditions: first-ear CI only, and best-aided everyday wearing condition. Subjects also provided self-reported measures of performance and benefit from their CI using standardized materials, including the Glasgow Benefit Inventory (GBI) and the Nijmegen Cochlear Implant questionnaire (NCIQ). Results: High-performing CI users demonstrated greater VWM than AVWM recall. VWM was positively related to AzBio scores when measured in the first-ear CI only. AVWM, processing speed, cognitive efficiency, and IQ did not relate to either measure of speech perception (i.e., first-ear CI or best-aided conditions). Subjects’ self-reported benefit as measured by the GBI predicted best-aided CI speech perception performance. Conclusion: In high-performing CI recipients, visual presentation of working memory tests may improve our assessment of cognitive function.
Machine learning for the recognition of emotion in the speech of couples in psychotherapy using the Stanford Suppes Brain Lab Psychotherapy Dataset

The automatic recognition of emotion in speech can inform our understanding of language, emotion, and the brain. It also has practical application to human-machine interactive systems. This paper examines the recognition of emotion in naturally occurring speech, where there are no constraints on what is said or the emotions expressed. This task is more difficult than that using data collected in scripted, experimentally controlled settings, and fewer results are published. Our data come from couples in psychotherapy. Video and audio recordings were made of three couples (A, B, C) over 18 hour-long therapy sessions. This paper describes the method used to code the audio recordings for the four emotions of Anger, Sadness, Joy and Tension, plus Neutral, also covering our approach to managing the unbalanced samples that a naturally occurring emotional speech dataset produces. Three groups of acoustic features were used in our analysis: filter-bank, frequency, and voice-quality features. The random forests model classified the features. Recognition rates are reported for each individual, the result of the speaker-dependent models that we built. In each case, the best recognition rates were achieved using the filter-bank features alone. For Couple A, these rates were 90% for the female and 87% for the male for the recognition of three emotions plus Neutral. For Couple B, the rates were 84% for the female and 78% for the male for the recognition of all four emotions plus Neutral. For Couple C, a rate of 88% was achieved for the female for the recognition of the four emotions plus Neutral and 95% for the male for three emotions plus Neutral. For pairwise recognition, the rates ranged from 76% to 99% across the three couples. Our results show that couple therapy is a rich context for the study of emotion in naturally occurring speech.
iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox

Imaging-based, non-contact measurement of physiology (including imaging photoplethysmography and imaging ballistocardiography) is a growing field of research. There are several strengths of imaging methods that make them attractive. They remove the need for uncomfortable contact sensors and can enable spatial and concomitant measurement from a single sensor. Furthermore, cameras are ubiquitous and often low-cost solutions for sensing. Open source toolboxes help accelerate the progress of research by providing a means to compare new approaches against standard implementations of the state-of-the-art. We present an open source imaging-based physiological measurement toolbox with implementations of many of the most frequently employed computational methods. We hope that this toolbox will contribute to the advancement of noncontact physiological sensing methods. Code: https://github.com/danmcduff/iphys-toolbox
LEP-CNN: A Lightweight Edge Device Assisted Privacy-preserving CNN Inference Solution for IoT

Supporting convolutional neural network (CNN) inference on resource-constrained IoT devices in a timely manner has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, the prevailing solution is to offload the CNN inference task, which is usually composed of billions of operations, to public cloud. However, the "offloading-to-cloud" solution may cause privacy breach while moving sensitive data to cloud. For privacy protection, the research community has resorted to advanced cryptographic primitives and approximation techniques to support CNN inference on encrypted data. Consequently, these attempts cause impractical computational overhead on IoT devices and degrade the performance of CNNs. Moreover, relying on the remote cloud can cause additional network latency and even make the system dysfunction when network connection is off. We proposes an extremely lightweight edge device assisted private CNN inference solution for IoT devices, namely LEP-CNN. The main design of LEP-CNN is based on a novel online/offline encryption scheme. The decryption of LEP-CNN is pre-computed offline via utilizing the linear property of the most time-consuming operations of CNNs. As a result, LEP-CNN allows IoT devices to securely offload over 99% CNN operations, and edge devices to execute CNN inference on encrypted data as efficient as on plaintext. LEP-CNN also provides an integrity check option to help IoT devices detect error results with a successful rate over 99%. Experiments on AlexNet show that LEP-CNN can speed up the CNN inference for more than 35 times for resource constrained IoT devices. A homomorphic encryption based AlexNet using CryptoNets is implemented to compare with LEP-CNN to demonstrate that LEP-CNN has a better performance than homomorphic encryption based privacy preserving neural networks under time-sensitive scenarios.
Posterior inference unchained with EL_2O

Statistical inference of analytically non-tractable posteriors is a difficult problem because of marginalization of correlated variables and stochastic methods such as MCMC and VI are commonly used. We argue that stochastic KL divergence minimization used by MCMC and VI is noisy, and we propose instead EL_2O, expectation optimization of L_2 distance squared between the approximate log posterior q and the un-normalized log posterior of p. When sampling from q the solutions agree with stochastic KL divergence minimization based VI in the large sample limit, however EL_2O method is free of sampling noise, has better optimization properties, and requires only as many sample evaluations as the number of parameters we are optimizing if q covers p. As a consequence, increasing the expressivity of q improves both the quality of results and the convergence rate, allowing EL_2O to approach exact inference. Use of automatic differentiation methods enables us to develop Hessian, gradient and gradient free versions of the method, which can determine M(M+2)/2+1, M+1 and 1 parameter(s) of q with a single sample, respectively. EL_2O provides a reliable estimate of the quality of the approximating posterior, and converges rapidly on full rank gaussian approximation for q and extensions beyond it, such as nonlinear transformations and gaussian mixtures. These can handle general posteriors, while still allowing fast analytic marginalizations. We test it on several examples, including a realistic 13 dimensional galaxy clustering analysis, showing that it is several orders of magnitude faster than MCMC, while giving smooth and accurate non-gaussian posteriors, often requiring a few to a few dozen of iterations only.
Optimal decoding of dynamic stimuli encoded by heterogeneous populations of spiking neurons - a closed form approximation

Neural decoding may be formulated as dynamic state estimation (filtering) based on point process observations, a generally intractable problem. Numerical sampling techniques are often practically useful for the decoding of real neural data. However, they are less useful as theoretical tools for modeling and understanding sensory neural systems, since they lead to limited conceptual insight about optimal encoding and decoding strategies. We consider sensory neural populations characterized by a distribution over neuron parameters. We develop an analytically tractable Bayesian approximation to optimal filtering based on the observation of spiking activity, that greatly facilitates the analysis of optimal encoding in situations deviating from common assumptions of uniform coding. Continuous distributions are used to approximate large populations with few parameters, resulting in a filter whose complexity does not grow with the population size, and allowing optimization of population parameters rather than individual tuning functions. Numerical comparison with particle filtering demonstrates the quality of the approximation. The analytic framework leads to insights which are difficult to obtain from numerical algorithms, and is consistent with biological observations about the distribution of sensory cells' preferred stimuli.
RNN-based Generative Model for Fine-Grained Sketching

Deep generative models have shown great promise when it comes to synthesising novel images. While they can generate images that look convincing on a higher-level, generating fine-grained details is still a challenge. In order to foster research on more powerful generative approaches, this paper proposes a novel task: generative modelling of 2D tree skeletons. Trees are an interesting shape class because they exhibit complexity and variations that are well-suited to measure the ability of a generative model to generated detailed structures. We propose a new dataset for this task and demonstrate that state-of-the-art generative models fail to synthesise realistic images on our benchmark, even though they perform well on current datasets like MNIST digits. Motivated by these results, we propose a novel network architecture based on combining a variational autoencoder using Recurrent Neural Networks and a convolutional discriminator. The network, error metrics and training procedure are adapted to the task of fine-grained sketching. Through quantitative and perceptual experiments, we show that our model outperforms previous work and that our dataset is a valuable benchmark for generative models. We will make our dataset publicly available.
Simple juntas for shifted families

We say that a family $\mathcal F$ of $k$-element sets is a $j$-junta if there is a set $J$ of size $j$ such that, for any $F$, its presence in $\mathcal F$ depends on its intersection with $J$ only. Approximating arbitrary families by $j$-juntas with small $j$ is a recent powerful technique in extremal set theory. The weak point of all known approximation by juntas results is that they work in the range $n>Ck$, where $C$ is an extremely fast-growing function of the input parameters, such as the quality of approximation or the number of families we simultaneously approximate. We say that a family $\mathcal F$ is shifted if for any $F=\{x_1,\ldots, x_k\}\in \mathcal F$ and any $G =\{y_1,\ldots, y_k\}$ such that $y_i\le x_i$, we have $G\in \mathcal F$. For many extremal set theory problems, including the Erdős Matching Conjecture, or the Complete $t$-Intersection Theorem, it is sufficient to deal with shifted families only. In this note, we present very general approximation by juntas results for shifted families with explicit (and essentially linear) dependency on the input parameters. The results are best possible up to some constant factors, moreover, they give meaningful statements for almost all range of values of $n$. The proofs are shorter than the proofs of the previous approximation by juntas results and are completely self-contained.
Jam To-morrow and Jam Yesterday, but Never Jam To-day: The of Theology Libraries Planning the Twenty-first Century

The future of theology libraries is far from clear. Since the nineteenth century, theology libraries have evolved to support the work of theological education. This article briefly reviews the development of theology libraries in North America and examines the contextual changes impacting theology libraries today. Three significant factors that will shape theology libraries in the coming decade are collaborative models of pedagogy and scholarship, globalization and rapid changes in information technology, and changes in the nature of scholarly publishing including the digitization of information. A large body of research is available to assist those responsible for guiding the direction of theology libraries in the next decade, but there are significant gaps in what we know about the impact of technology on how people use information that must be filled in order to provide a solid foundation for planning. Has anyone done work on the relationship between the Internet resources and the need for and use of books these days? I know that faculty and the kind of assignments they give, for example, are involved in this puzzle. The use of our library by students has decreased the past few years and we are trying to sort out what may be the cause and if we are dealing with a trend toward more Internet resources / courses and its effect on fewer books being read and taken out. (David MacLachlan)
Metabolic Syndrome in Psychotic Disorder Patients Treated With Oral and Long-Acting Injected Antipsychotics

Background: Severe mental illnesses are associated with increased risks for metabolic syndrome (MetS) and other medical disorders, often with unfavorable outcomes. MetS may be more likely with schizoaffective disorder (SzAff) than schizophrenia (Sz). MetS is associated with long-term antipsychotic drug treatment, but relative risk with orally administered vs. long-acting injected (LAI) antipsychotics is uncertain. Methods: Subjects (n = 151 with a DSM-IV-TR chronic psychotic disorder: 89 Sz, 62 SzAff), treated with oral or LAI antipsychotics were compared for risk of MetS, initially with bivariate comparisons and then by multivariate regression modeling. Results: Aside from measures on which diagnosis of MetS is based, factors preliminarily associated with MetS included antipsychotic drug dose, “high-risk” antipsychotics associated with weight-gain, older age and female sex. Defining factors associated with diagnosis of MetS ranked in multivariate regression as: higher fasting glucose, lower LDL cholesterol, higher diastolic blood pressure, and higher BMI. Risk of MetS with antipsychotics ranked: quetiapine ≥ clozapine ≥ paliperidone ≥ olanzapine ≥ risperidone ≥ haloperidol ≥ aripiprazole. Other associated risk factors in multivariate modeling ranked: higher antipsychotic dose, older age, and SzAff diagnosis, but not oral vs. LAI antipsychotics Conclusions: SzAff diagnosis and higher antipsychotic doses were associated with MetS, whereas orally vs. injected antipsychotics did not differ in risk of MetS.
Mycobacterial Lipoprotein Z Triggers Efficient Innate and Adaptive Immunity for Protection Against Mycobacterium tuberculosis Infection

Mycobacterial lipoproteins are considered to be involved in both virulence and immunoregulatory processes during Mycobacterium tuberculosis (M.tb) infection. In our previous investigations on the immunoreactivity of more than 30 M.tb proteins in active TB patients, we identified mycobacterial lipoprotein Z (LppZ) as one of the most immune dominant antigens. How LppZ triggers immune responses is still unclear. In this study, we analyzed LppZ-mediated innate and adaptive immunity using a murine air pouch model and an M.tb infection model, respectively. We found that LppZ could not only recruit inflammatory cells but also induce the production of proinflammatory cytokines inside the pouches. LppZ could also induce strong Th1 responses following immunization and confer protection against challenge with M.tb virulent strain H37Rv at a similar level to BCG vaccination but with less pathological damage in the lungs. Furthermore, we revealed the presence of LppZ-specific functional CD4+ T cells in the lungs of the challenged mice that were capable of secreting double or triple cytokines, including IFN-γ, IL-2, and TNF-α. Our study thus demonstrates that LppZ is of strong immunogenicity during M.tb infection in both humans and mice and has the ability to trigger effective innate and cellular immunity. Considering the limitations of candidate antigens in the pipeline of TB vaccine development, LppZ-mediated immune protection against M.tb challenge in the mouse model implies its potential application in vaccine development.
Variations in CD14 Gene Are Associated With Autoimmune Thyroid Diseases in the Chinese Population

Autoimmune thyroid diseases (AITDs) are chronic organ-specific autoimmune diseases and mainly include Graves' disease (GD) and Hashimoto's thyroiditis (HT). CD14 is an important component of the immune system as a receptor for gram-negative lipopolysaccharide (LPS). The genetic polymorphisms of CD14 have been confirmed to be associated with a variety of autoimmune diseases. However, its relationship with AITDs is still unclear. The study was aimed to determine whether four single nucleotide polymorphisms (rs2915863, rs2569190, rs2569192, and rs2563298) of CD14 are associated with AITDs and its subgroups of GD and HT. The results showed significant association of rs2915863 and rs2569190 with GD. The frequencies of rs2915863 genotypes and T allele in patients with GD differed significantly from their controls (P = 0.007 and P = 0.021, respectively). For rs2569190, frequencies of genotypes and G allele in GD patients also showed positive P-values (P = 0.038 and P = 0.027, respectively). The correlations between these two loci and GD are more pronounced in female GD patients and patients with a family history. In genetic model analysis, the allele model, recessive model, and homozygous model of rs2569190 and rs2915863 embodied strong correlations with GD after the adjusting of age and gender (P = 0.014, P = 0.015, P = 0.009, and P = 0.014, P = 0.001, P = 0.006, respectively). However, these four sites are not related to HT. We firstly discovered the relationship between CD14 gene polymorphism and GD, and the results indicate that CD14 is an important risk locus for AITD and its SNPs may contribute to host's genetic predisposition to GD.
Ranking Methods for Query Relaxation in Book Search

In this paper, we propose a method to support book search tasks where users issue a query describing the story in a book to a database storing brief descriptions of books. Such a query may include extraneous words that do not appear in the brief description of the book in the database. In addition, queries by users who only have vague memories of the stories may even include wrong keywords. In order to find books with such queries, we need a query relaxation scheme. In the scheme we propose in this paper, we classify words in a user query describing a book into four types based on their roles in the description, and for each type, we estimate the probability of their appearance in the description in the database. We estimate it based on statistics we obtained through an analysis of an archive of queries and answers in the past. We then generate relaxed queries by using every subset of the words in the user query, and rank the queries based on the expected ranking of the target book in their results. The expected ranking of the target book in a query result is estimated by using appearance probabilities of words in the query and the number of books matching the query. We conducted an experiment for comparing various ranking schemes by their MRR, and our ranking scheme that uses both the word appearance probabilities and the number of matching books showed a good performance.
Online Social Networks in Health Care: A Study of Mental Disorders on Reddit

The alarming increase in the number of people afflicted by mental health disorders has become one of the major public health problems faced by governments worldwide. Traditional face-to-face clinical interventions are costly, and, in many cases, leave out a sizable number of people who are struggling to improve their mental health conditions. Alternative forms of intervention that have a larger reach and allow for continual interaction while reducing costs are being investigated, including those relying on Online Social Networks (OSNs). Initially designed for promoting friendship, OSNs started to connect people willing to share experiences related to mental health disorders. In light of this fact, we investigate four Reddit online communities: Depression, SuicideWatch, Anxiety and Bipolar. We focus on user activities and interactions, and on the discourse pattern analysis of posts and comments made by the community members. We found that (i) interaction patterns are very similar across these subreddits, and interactions are centered around content, rather than users; (ii) most posts that generate the longest discussion trees are requests for help and, more often than not, multiple users offer support; (iii) the four subreddits share a common language and encouragement words are a frequent pattern, for instance. We hope that the insights unveiled by our analyses will help on building successful online interventions to support people in crisis and assist their counselors.
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations

Learning a low-rank matrix from missing and corrupted observations is a fundamental problem in many machine learning applications. However, the role of side information in low-rank matrix learning has received little attention, and most current approaches are either ad-hoc or only applicable in certain restrictive cases. In this paper, we propose a general model that exploits side information to better learn low-rank matrices from missing and corrupted observations, and show that the proposed model can be further applied to several popular scenarios such as matrix completion and robust PCA. Furthermore, we study the effect of side information on sample complexity and show that by using our model, the efficiency for learning can be improved given sufficiently informative side information. This result thus provides theoretical insight into the usefulness of side information in our model. Finally, we conduct comprehensive experiments in three real-world applications— relationship prediction, semi-supervised clustering and noisy image classification, showing that our proposed model is able to properly exploit side information for more effective learning both in theory and practice.