Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Prediction in Hybrid Conjoint Analysis

The authors propose a general model that includes the effects of discrete and continuous heterogeneity as well as self-stated and derived attribute importance in hybrid conjoint studies. Rather than use the self-stated importances as prior information, as has been done in several previous approaches, the authors consider them data and therefore include them in the formulation of the likelihood, which helps investigate the relationship of self-stated and derived importances at the individual level. The authors formulate several special cases of the model and estimate them using the Gibbs sampler. The authors reanalyze Srinivasan and Park's (1997) data and show that the current model predicts real choices better than competing models do. The posterior credible intervals of the predictions of models with the different heterogeneity specifications overlap, so there is no clear superior specification of heterogeneity. However, when different sources of data are used—that is, full profile evaluations, self-stated importances, or both—clear differences arise in the accuracy of predictions. Moreover, the authors find that including the self-stated importances in the likelihood leads to much better predictions than does considering them prior information.
Learning Discriminative αβ-Divergences for Positive Definite Matrices

Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data. To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their behaviors may be application dependent, raising the need of manual selection to achieve the best possible performance. Further and as a result of their overwhelming complexity for large-scale problems, computing pairwise similarities by clever embedding of SPD matrices is often preferred to direct use of the aforementioned measures. In this paper, we propose a discriminative metric learning framework, Information Divergence and Dictionary Learning (IDDL), that not only learns application specific measures on SPD matrices automatically, but also embeds them as vectors using a learned dictionary. To learn the similarity measures (which could potentially be distinct for every dictionary atom), we use the recently introduced αß-logdet divergence, which is known to unify the measures listed above. We propose a novel IDDL objective, that learns the parameters of the divergence and the dictionary atoms jointly in a discriminative setup and is solved efficiently using Riemannian optimization. We showcase extensive experiments on eight computer vision datasets, demonstrating state-of-the-art performances.
Macrophages offer a paradigm switch for CNS delivery of therapeutic proteins.

AIMS Active targeted transport of the nanoformulated redox enzyme, catalase, in macrophages attenuates oxidative stress and as such increases survival of dopaminergic neurons in animal models of Parkinson's disease. Optimization of the drug formulation is crucial for the successful delivery in living cells. We demonstrated earlier that packaging of catalase into a polyion complex micelle ('nanozyme') with a synthetic polyelectrolyte block copolymer protected the enzyme against degradation in macrophages and improved therapeutic outcomes. We now report the manufacture of nanozymes with superior structure and therapeutic indices. METHODS Synthesis, characterization and therapeutic efficacy of optimal cell-based nanoformulations are evaluated. RESULTS A formulation design for drug carriers typically works to avoid entrapment in monocytes and macrophages focusing on small-sized nanoparticles with a polyethylene glycol corona (to provide a stealth effect). By contrast, the best nanozymes for delivery in macrophages reported in this study have a relatively large size (≈ 200 nm), which resulted in improved loading capacity and release from macrophages. Furthermore, the cross-linking of nanozymes with the excess of a nonbiodegradable linker ensured their low cytotoxicity, and efficient catalase protection in cell carriers. Finally, the 'alternatively activated' macrophage phenotype (M2) utilized in these studies did not promote further inflammation in the brain, resulting in a subtle but statistically significant effect on neuronal regeneration and repair in vivo. CONCLUSION The optimized cross-linked nanozyme loaded into macrophages reduced neuroinflammatory responses and increased neuronal survival in mice. Importantly, the approach for nanoformulation design for cell-mediated delivery is different from the common requirements for injectable formulations.
Tricyclic antidepressants in treatment of depression and chronic pain: analysis of the supporting evidence.

There is now sufficient scientific evidence to indicate that chronic pain and depression share in common certain neurochemical abnormalities (1). With this interrelationship, it is not surprising that the tricyclic antidepressants have proven effective not only in the treatment of depression, but also in treating chronic pain. Most anesthesiologists are aware of the potential cardiac toxicity associated with the tricyclic antidepressants (2-8). Several reports have warned of tachycardia and deleterious arrhythmias in patients taking tricyclic antidepressants when given halothane and pancuronium during general anesthesia (9,lO). In contrast, the psychotropic and analgesic properties of the tricyclic antidepressants are less well known to many anesthesiologists. This review is oriented to the anesthesiologist interested in the management of chronic pain. It discusses depression and pain as concurrent disorders, the biomolecular mechanisms that control mood and pain, the usefulness of tricyclic antidepressants in chronic pain, and the direction for future studies. The juxtaposition of pain and depression and their treatment with tricyclic antidepressants also provides valuable insight into the neural abnormalities that occur during a chronic pain disorder.
Survival of Clinical and Poultry-Derived Isolates of Campylobacter jejuni at a Low Temperature (4°C)

ABSTRACT Campylobacter jejuni is a leading cause of bacterial gastroenteritis in humans, and contamination of poultry has been implicated in illness. The bacteria are fastidious in terms of their temperature requirements, being unable to grow below ca. 31°C, but have been found to be physiologically active at lower temperatures and to tolerate exposure to low temperatures in a strain-dependent manner. In this study, 19 field isolates of C. jejuni (10 of clinical and 9 of poultry origin) were studied for their ability to tolerate prolonged exposure to low temperature (4°C). Although substantial variability was found among different strains, clinical isolates tended to be significantly more likely to remain viable following cold exposure than poultry-derived strains. In contrast, the relative degree of tolerance of the bacteria to freezing at −20°C and freeze-thawing was strain specific but independent of strain source (poultry versus clinical) and degree of cold (4°C) tolerance.
Alleviating the negative waiting experience through the moderating role of service environment: an empirical study

This study aims to examine the influence of waiting time on customer satisfaction and further investigates the moderating role of perceived service environment in alleviating the negative impact of waiting duration on customer satisfaction. A total of 200 customers of an international-chain coffee shop in Indonesia became the respondents. The results confirm the negative impact of perceived waiting time on customer satisfaction. Another measure of waiting time, that is, disconfirmation between perceived waiting time and expected waiting time, is found to not significantly influence customer satisfaction. Additionally, the findings also reveal that perceived service environment, through favourable ambience, design, and employees, significantly reduces the negative impact of waiting duration on customer satisfaction. Implications for research and practices are discussed.
The Critical Role of Internal Marketing in Knowledge Management in Not-for-Profit Organizations

Australian not-for-profit (NFP) organizations are knowledge-intensive enterprises, but most struggle with knowledge management (KM), while at the same time identifying it as an increasingly important activity they must improve for an increasingly competitive and demanding environment. Replicating “corporate” best KM practices is often misaligned with NFPs’ unique cultures, finances, operations, and missions. This article advances previous work presented in this journal and highlights the importance of internal management practices as vital for success. This article draws from NFP research in knowledge and explores a key enabler (internal marketing) that has been overlooked for building a critical link between NFP staff/volunteers and the organization for building and sustaining KM in an NFP environment. Using in-depth interviewing (32 interviews) and an online survey (179 respondents), this article examines the elements of internal marketing’s benefit/value propositions to staff and the importance of personal relevance. Further, the article also explores the value of socialization strategies (such as communities of practice) as effective internal marketing channels.
Quasi-Cyclic LDPC Codes: An Algebraic Construction, Rank Analysis, and Codes on Latin Squares

Quasi-cyclic LDPC codes are the most promising class of structured LDPC codes due to their ease of implementation and excellent performance over noisy channels when decoded with message-passing algorithms as extensive simulation studies have shown. In this paper, an approach for constructing quasi-cyclic LDPC codes based on Latin squares over finite fields is presented. By analyzing the parity-check matrices of these codes, combinatorial expressions for their ranks and dimensions are derived. Experimental results show that, with iterative decoding algorithms, the constructed codes perform very well over the AWGN and the binary erasure channels.
Security threats and measures for the Internet of Things

Governments,companies and research institutions are pressuring research and development of the Internet of Things.However,the development of the Internet of Things will be constrained by security and privacy threats.This paper summarizes security threats to the Internet of Things to provide a theoretical reference for study of the Internet of Things and to provide useful security measures.The architecture of the Internet of Things is used to classify threats for the perception layer,transport layer and application layer.The perception layer threats include security threats for Radio Frequency Identification,Wireless Sensor Networks and mobile intelligent terminals.Transport layer threats include data leakage or damage and security issues during massive data integration.Application layer threats include the loss of user privacy,incorrect access control policies and inadequate security standards.Finally,this paper gives security measures and recommendations for all types of security threats.
Management of locoregional recurrent breast cancer

The influence of radiation and/or chemotherapy on locoregional tumor control and survival in patients treated for recurrent breast cancer after radical or modified radical mastectomy is retrospectively evaluated in 164 patients treated between 1972 and 1983 at the University of Texas M. D. Anderson Hospital. Treatment consisted of radiation alone in 57 patients, chemotherapy alone in 50 patients, and a combination of radiation and chemotherapy in 57 patients. Important differences in the composition of these three groups include a preponderance of postmenopausal women (44% vs. 32%) and more patients with four or more positive axillary nodes at the time of initial mastectomy (32% vs. 18%) in the radiotherapy group. Locoregional control of recurrent cancer was achieved in 65% of patients with radiotherapy compared to 46% of patients with chemotherapy (P = 0.049) and 67% with chemotherapy and radiotherapy. The addition of chemotherapy produced a trend toward improved disease‐free survival rates. The two prognostic factors affecting tumor control and survival in this study are the tumor burden of the recurrence and the histologic axillary node status at the time of the initial mastectomy.
Energy-Efficient Adaptive Computing With Multifunctional Memory

Digital memory arrays, which serve as an integral part of modern computing systems, are traditionally used for information storage. However, recent reports show that memory can be used on demand as a reconfigurable computing resource, drastically improving energy efficiency for many applications. In this case, memory usage is limited to storing function responses as multi-input multi-output lookup tables. In this brief, we propose a novel multifunctional memory (MFM) framework, which can function as typical memory for storage as well as in a neuroinspired computing mode. The system is based on a modified memory array, which can be dynamically switched between these two modes. Using a promising emerging memory device, namely, resistive random access memory, we present device-level engineering, circuit-level modifications, and appropriate architecture to realize the MFM framework. Simulation results demonstrate significant improvements in both energy efficiency and performance compared to a general-purpose processor, a field-programmable gate array, and a recent memory-based, reconfigurable computing framework (MAHA) for a set of common application kernels.
Scales in services marketing research: a critique and way forward

Purpose – The purpose of this paper is to discuss the use of measurement scales and to illustrate some of the drawbacks of using scales for measuring service quality without due recognition of the limitations and rigidity of such scales, especially when they are applied to the complexity of service marketing situations and contexts.Design/methodology/approach – A review of the most widely used scales in services measurement, including SERVQUAL and SERVPERF is provided, along with some of the conceptual issues surrounding scale design and use in service contexts. Then some qualitative research techniques are considered in terms of their adaptability and flexibility for carrying out research regarding the complex nature of services.Findings – Measurement scales are evaluated and discussed. The key criticisms of best‐known scales used for services situations are presented. Then consideration is given to what might be a “best practice” scenario for measuring and assessing service‐related issues in a service c.
Optimal oblivious routing in polynomial time

A recent seminal result of Racke is that for any network there is an oblivious routing algorithm with a polylog competitive ratio with respect to congestion. Unfortunately, Racke's construction is not polynomial time. We give a polynomial time construction that guarantee's Racke's bounds, and more generally gives the true optimal ratio for any network.
Hacking in Darkness: Return-oriented Programming against Secure Enclaves

Intel Software Guard Extensions (SGX) is a hardware-based Trusted Execution Environment (TEE) that is widely seen as a promising solution to traditional security threats. While SGX promises strong protections to bug-free software, decades of experience show that we have to expect vulnerabilities in any non-trivial application. In a traditional environment, such vulnerabilities often allow attackers to take complete control over the vulnerable systems. Efforts to evaluate the security of SGX have been focusing on side-channels. So far, neither a practical attack against a vulnerability in enclave code nor a proof-of-concept attack scenario has been demonstrated. Thus, a fundamental question remains: What are the consequences and dangers of having a memory corruption vulnerability in enclave code? To answer this question, we comprehensively analyze the exploitation technique against vulnerabilities inside enclaves. We demonstrate practical exploitation techniques, called Dark-ROP, which can completely disarm the security guarantees of SGX. Dark-ROP exploits a memory corruption vulnerability in the enclave software through return-oriented programming (ROP), but it differs significantly in a sense that the target enclave runs under a solid hardware protection. We overcome this problem by exploiting SGX-specific properties and obstacles by formulating a novel ROP attack scheme against SGX under practical assumptions. Specifically, we have built several oracles that tell the status of enclave execution to the attacker in order to enable launching of ROP attack while both code and data are hidden. Additionally, we exfiltrate the enclave’s code and data into a shadow application to fully control the execution environment while satisfying all security requirements of SGX. This shadow application emulates the enclave under the complete control of the attacker, using the enclave (through ROP calls) only to perform SGX operations such as reading the enclave’s SGX crypto keys. The consequences of Dark-ROP are alarming; the attacker can completely breach the enclave’s memory protections and trick the SGX hardware into disclosing the enclave’s encryption keys and producing measurement reports that defeat remote attestation. This result strongly suggests that traditional security mitigation should be taken more seriously than common directions that communities are actively taking for convince (e.g., Haven or Graphene), which essentially increase the trust computing base and the attack surface.
Prevalence and clinical characteristics of mental rituals in a longitudinal clinical sample of obsessive–compulsive disorder

Background: Obsessive–compulsive disorder (OCD) is a chronic and debilitating anxiety disorder associated with significant impairment in quality of life and functioning. Research examining the differences in clinical correlates and treatment response associated with different obsessions in OCD has yielded important findings underscoring the heterogeneous nature of this disorder. To date, most of this research has focused on differences associated with primary obsessions, and little attention has been paid to the clinical utility of studying how compulsive symptoms affect clinical course. Virtually no systematic research has explored the clinical characteristics of one understudied symptom presentation, mental rituals, and what impact this primary symptom has on severity and course of illness. Mental rituals, or compulsions without overt signs, represent unique clinical challenges but often go understudied for numerous methodological and clinical reasons. Methods: In this study, we explored the impact of primary mental rituals on clinical severity and chronicity in a large, longitudinal sample of OCD patients (N = 225) over 4 years. Results: Mental rituals were a primary presenting symptom for a sizable percentage of the sample (12.9%). Primary mental rituals were associated with greater clinical severity and lower functioning at intake, as well as a more chronic course of illness, as participants with primary mental rituals spent nearly 1 year longer in full DSM‐IV criteria episodes over the 4‐year follow‐up interval than OCD patients without mental rituals. Conclusions: These results suggest that mental rituals are uniquely impairing and highlight the need for further empirical exploration and consideration in treatment. Depression and Anxiety, 2011. © 2011 Wiley‐Liss, Inc.
Mouse models of Rett syndrome: from behavioural phenotyping to preclinical evaluation of new therapeutic approaches

Rett syndrome (RTT) is a neurodevelopmental disorder, primarily affecting girls. RTT causes severe cognitive, social, motor and physiological impairments and no cure currently exists. The discovery of a monogenic origin for RTT and the subsequent generation of RTT mouse models provided a major breakthrough for RTT research. Although the characterization of these mutant mice is far from complete, they recapitulate several RTT symptoms. This review provides an overview of the behavioural domains so far investigated in these models, including the very few mouse data concerning the developmental course of RTT. Both clinical and animal studies support the presence of early defects and highlight the importance of probing the presymptomatic phase for both the precocious identification of biomarkers and the early assessment of potential therapies. Preclinical evaluations of pharmacological and nonpharmacological interventions so far carried out are also illustrated. In addition, genetic manipulations are reported that demonstrate rescue from the damage caused by the absence of the methyl-CpG-binding protein 2 (MeCP2) gene even at a mature stage. Given the rare occurrence of RTT cases, transnational collaborative networks are expected to provide a deeper understanding of aetiopathology and the development of new therapeutic approaches.
Time Reversal Imaging of Deeply Buried Targets Under Moderately Rough Surfaces Using Approximate Transmitted Fields

In this paper, for time-reversal (TR) imaging of buried targets under moderate rough ground surfaces, the asymptotic half-space Green's function (AGF) is utilized. For the forward problem, a parallel finite-difference time-domain technique is used, in which the excitation is an array of rectangular apertures and scattered fields are probed by an array very near the ground surface. Using the scattering matrix of the TR operator at a frequency range of 0.5-2.5 GHz, multiple signal classification (MUSIC) images are calculated. Assuming both dielectric and metallic targets and the Gaussian random rough surfaces, TR images using the conventional Green's function of a homogeneous medium with an averaged permittivity of free space and the ground are compared with those using AGF. It is shown that two different targets, at the same range but different depths, can be successfully detected once the AGF is used. Furthermore, using five receiving arrays at different heights from the ground, a good depth resolution is achieved. In this regard, transverse and depth resolutions are studied for ten random realizations of rough surfaces and two different root-mean-square heights (5 and 10 mm), leading to the mean depth resolution of about 0.9 m.
Social Network and Job Quality: Evidence from Cameroon

Using Cameroonian data, this paper investigates the effects of social network on job quality. Social network is measured in terms of using friends and relatives while looking for a job. As regards to job quality, two aspects have been taken into consideration: monetary job quality which is captured by wage and non monetary job quality which is captured by a combination of social protection and job security. In order to evaluate the effectiveness of social network as regard to job quality, we make use of both Heckman selection model and Ordered Logit model. Our findings contrast with previous studies. Job seekers who make use of social network do not necessarily get a good job. More precisely, while the users of social networks exhibit a wage premium, they also exhibit a low job quality in terms of social protection and job security. We also find that social network contributes to explain job quality differential according to gender and institutional sectors (formal versus informal).