The advent of novel organic and inorganic nanomaterials in recent years, particularly nanostructured carbons, conducting polymers, and metal oxides, has enabled the fabrication of various energy ...devices with enhanced performance. In this paper, we review in detail different nanomaterials used in the fabrication of electrochemical capacitor electrodes and also give a brief overview of electric double-layer capacitors, pseudocapacitors, and hybrid capacitors. From a materials point of view, the latest trends in electrochemical capacitor research are also discussed through extensive analysis of the literature and by highlighting notable research examples (published mostly since 2013). Finally, a perspective on next-generation capacitor technology is also given, including the challenges that lie ahead.
Abstract
In-memory computing is an attractive alternative for handling data-intensive tasks as it employs parallel processing without the need for data transfer. Nevertheless, it necessitates a ...high-density memory array to effectively manage large data volumes. Here, we present a stacked ferroelectric memory array comprised of laterally gated ferroelectric field-effect transistors (LG-FeFETs). The interlocking effect of the α-In
2
Se
3
is utilized to regulate the channel conductance. Our study examined the distinctive characteristics of the LG-FeFET, such as a notably wide memory window, effective ferroelectric switching, long retention time (over 3 × 10
4
seconds), and high endurance (over 10
5
cycles). This device is also well-suited for implementing vertically stacked structures because decreasing its height can help mitigate the challenges associated with the integration process. We devised a 3D stacked structure using the LG-FeFET and verified its feasibility by performing multiply-accumulate (MAC) operations in a two-tier stacked memory configuration.
Recently, Graphic Processing Units (GPUs) have been widely used for general purpose applications such as machine learning applications, acceleration of cryptographic applications (especially, ...blockchains), etc. The development of CUDA makes this General-Purpose computing on GPU possible. In particular, currently GPU technology has been widely used for server-side applications so as to provide fast and efficient service to a number of clients. In other words, servers need to process a large amount of user data and execute authentication process. Verifying the integrity of transmitted data is essential for ensuring that the data is not modified during transmission. Hash functions are the cryptographic algorithm which can verify the integrity of data and there are SHA-1, SHA-2, and SHA-3 standard hash functions. In 2015, Keccak algorithm was selected for SHA-3 competition by NIST. However, until now, software implementations of SHA-3 have not provided enough performance for various applications. In addition, SHA-3 and SHAKE using SHA-3 are being used in many Post-Quantum Cryptosystems (PQC) submitted to NIST PQC competition. Therefore, SHA-3 optimization research is required in the software environment. We propose an optimized SHA-3 software implementation on GPU environment. For performance efficiency, we propose several techniques including optimization of SHA-3 internal process, inline PTX optimization, optimized memory usage, and the application of asynchronous CUDA stream. As a result of applying the proposed optimization method, our SHA-3(512) (resp. SHA-3(256)) implementation without CUDA stream provides a maximum throughput of 88.51 Gb/s (resp. 171.62 Gb/s) on RTX2080Ti GPU. Furthermore, without the application of CUDA stream, our SHA-3(512) software on GTX1070 provides about 49.73% improved throughput compared with the previous best work on GTX1080, which shows the superiority of our proposed optimization methods. Our optimized SHA-3 software on GPU can be efficiently used for block-chain applications and several PQCs (especially, key generation process in Lattice-based cryptosystems).
Background
With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers ...such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a “virtual kiosk test” for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment.
Objective
We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features.
Methods
A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test—developed by our group—where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls.
Results
In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=–3.44; P=.004), and a greater number of errors (t49=–3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score.
Conclusions
Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Data encryption is essential for securely managing clients' data in servers in data-centric ICT environment. Clients must encrypt the data before transmitting it to severs or other clients. ...Encrypting a large volumne of data requires a lot of time. Therefore, in order for servers and clients to not only secure but also smoothly communicate each other, the optimization of data encryption is necessary on both the server-side and the client-side. Especially, the server environment is responsible for managing/processing lots of data from clients. In this paper, we present two kinds of highly optimized PIPO cipher software in CPU and GPU environment, respectively. PIPO was proposed in ICISC'19 as a lightweight block cipher. For optimization, we take full advantage of two parallel processing technologies: AVX-related instructions in CPU and NVIDIA CUDA platform in GPU. Regarding the optimization in CPU environment, we process several plaintext blocks such as 32 and 64 blocks with the proper use of AVX2 and AVX-512 instruction sets and the proposed arithmetic techniques, respectively. Regarding the optimization on GPU environment, we propose a data alignment/data combining methods, and PTX inline assembly utilization method considering the characteristics of GPU architecture. In Intel Core i9-11900K (3.50GHz) architecture, our PIPO software utilizing AVX-2 has a performance improvement on 839.64% (resp. 985.46% AVX-512) compared to the existing reference code (Regarding AVX-512, this is the first PIPO software using AVX-512 instructions as far as we know). Finally, in RTX 2080Ti, our PIPO GPO software shows throughput of up to 1110.08 Gbps.
Password-Based Key Derivation Function 2 (PBKDF2) is widely used cryptographic algorithm in order to generate secure keys to a password in various occasions. For example, it is used for file ...encryption and implementation of authentication systems, and so on. However, the generated derived key has a lower entropy than a general cryptography key, so its use is limited. To compensate for this the number of iteration counts of PBKDF2 should be increased. As the number of repetitive tasks increases, the entropy of the derived key increases, but it takes more time to generate the derived key. We present various optimization methods of PBKDF2. The main idea of our proposed method is reducing redundant block operations and optimizing the internal process of underlying Pseudo Random Function (PRF). In other words, we integrate several redundant operations and make full use of constant values used in PBKDF2. We use two HMAC algorithms: one using SHA-2 family and one using LSH family as the PRF of PBKDF2 (SHA-2 family is the most widely used hash functions, and LSH family is the latest hash function recently developed in South Korea). With our techniques, our implementations outperform Korea Internet & Security Agency (KISA) implementation by 121.26%, 325.91%, and 231.89% for using SHA256, LSH256, and LSH512 respectively; and also outperform OpenSSL implementation by 39.59% using SHA512. In addition, we show that the internal process of PBKDF2 can be computed independently. With our multi thread technique, our PBKDF2 implementations outperform KISA implementation by 2,152.66%, 1,986.85%, and 1,591.36% for using SHA256, LSH256, and LSH512 respectively; and our PBKDF2-HMAC-SHA512 implementation outperforms OpenSSL implementation by 523.57%. With our proposed implementation techniques, higher security can be achieved with more iteration operations. Furthermore, our optimization techniques can be easily expanded to optimize the performance of PBKDF2 on GPGPU and embedded devices.
The objective of our study was to scrutinize the learning experiences of Korean neurology residents, with an emphasis on the implications of the novel competency-based curriculum implemented in 2021. ...We hypothesized that this revised curriculum could modulate residents' cognitive conduct, primarily the manifestation of overconfidence, in distinctive ways across different stages of training. Our investigative framework was three-fold. Initially, we began with a qualitative inquiry involving in-depth interviews with a purposively selected cohort of eight residents from four training sites. This approach facilitated comprehensive insight into their perceptions of their competence and confidence across the continuum of a four-year residency program. Subsequently, we incorporated the K-NEPA13 assessment instrument, administered to the residents and their overseeing supervisors. This stage aimed to dissect potential cognitive biases, particularly overconfidence and consistency, within the resident population. The final study involved a comprehensive survey administered to a group of 97 Korean neurology residents, allowing us to consolidate and validate our preceding findings. Our findings revealed that junior residents portrayed heightened confidence in their clinical capabilities compared to their senior peers. Intriguingly, junior residents also displayed a stronger inclination towards reevaluating their clinical judgments, a behavior we hypothesize is stimulated by the recently introduced competency-based curriculum. We identified cognitive divergence between junior and senior residents, with the latter group favoring more consistent and linear cause-and-effect reasoning, while the former demonstrated receptiveness to introspection and reconsideration. We speculate this adaptability might be engendered by the supervisor assignment protocol intrinsic to the new curriculum. Our study highlights the essentiality of incorporating cognitive behaviors when devising medical education strategies. Acknowledging and addressing these diverse cognitive biases, and instilling a spirit of adaptability, can nurture a culture that persists in continuous learning and self-reflection among trainee doctors.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Cryptographic algorithms are widely used as the foundation of various security systems and applications (e.g., secure communication, blockchain systems, and cloud services). A block cipher is an ...essential cryptographic algorithm to achieve confidentiality. This paper proposes parallel implementations of Korean block ciphers using Advanced Vector eXtension (AVX)-512, which is a new Single instruction, multiple data (SIMD) instruction set that has recently been integrated into several high-end Intel central processing unit (CPU). Target algorithms are LEA, HIGHT, and CHAM block ciphers. Additionally, this paper also proposes applicable implementing techniques, which are designed for each algorithm. These enable to use of parallel processing instructions in AVX-512 properly for each algorithm. The proposed LEA-128 (192, 256 resp.)implementation demonstrates a performance improvement of 506.09% (323.31%, 386.76% resp.) compared to the reference code, and our HIGHT implementation exhibits a performance improvement of 520.88% compared to the reference code. In addition, CHAM-64/128 (128/256 resp.) implementation shows a performance improvement of 1,325.81% (833.61% resp.) compared to the reference code. In addition, we measured the performance with a 32MB long message. LEA-128 (192, 256 resp.) implementation showed an improvement of 556.32% (594.74%, 615.38% resp.) compared with the reference code. Also, HIGHT implementation showed 834.40%, and CHAM showed 1,332.40% (832.86% resp.) for CHAM-64/128 (CHAM-128/256 resp.), compared by the reference code. To the best of our knowledge, this is the first result of the study to optimize Korean cryptographic algorithms using the AVX-512 instruction set. The proposed methods can effectively be used in Addition, Rotation, and XOR (ARX)-based cryptographic algorithms, enabling efficient cryptographic algorithm processing in various environments such as hash-based signatures, service environments, gateway, and edge computing.
Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) ...biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.
We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.
The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T
-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.
The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F
-score (93.3%).
The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Questionnaires or computer-based tests for assessing activities of daily living are well-known approaches to screen for mild cognitive impairment (MCI). However, questionnaires are subjective and ...computerized tests only collect simple performance data with conventional input devices such as a mouse and keyboard. This study explored the validity and discriminative power of a virtual daily living test as a new diagnostic approach to assess MCI. Twenty-two healthy controls and 20 patients with MCI were recruited. The virtual daily living test presents two complex daily living tasks in an immersive virtual reality environment. The tasks were conducted based on subject body movements and detailed behavioral data (i.e., kinematic measures) were collected. Performance in both the proposed virtual daily living test and conventional neuropsychological tests for patients with MCI was compared to healthy controls. Kinematic measures considered in this study, such as body movement trajectory, time to completion, and speed, classified patients with MCI from healthy controls, F(8, 33) = 5.648, p < 0.001, η2 = 0.578. When both hand and head speed were employed in conjunction with the immediate free-recall test, a conventional neuropsychological test, the discrimination power for screening MCI was significantly improved to 90% sensitivity and 95.5% specificity (cf. the immediate free-recall test alone has 80% sensitivity and 77.3% specificity). Inclusion of the kinematic measures in screening for MCI significantly improved the classification of patients with MCI compared to the healthy control group, Wilks' Lambda = 0.451, p < 0.001.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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