Miracle-dz/UniFAR-FTUs huggingface dataset:
Propranolol treatment during repetitive mild traumatic brain injuries induces transcriptomic changes in the bone marrow of mice

Introduction There are 1.5 million new mild traumatic brain injuries (mTBI) annually in the US, with many of the injured experiencing long-term consequences lasting months after the injury. Although the post injury mechanisms are not well understood, current knowledge indicates peripheral immune system activation as a causal link between mTBI and long-term side effects. Through a variety of mechanisms, peripheral innate immune cells are recruited to the CNS after TBI to repair and heal the injured tissue; however, the recruitment and activation of these cells leads to further inflammation. Emerging evidence suggests sympathetic nervous system (SNS) activity plays a substantial role in the recruitment of immune cells post injury. Methods We sought to identify the peripheral innate immune response after repeated TBIs in addition to repurposing the nonselective beta blocker propranolol as a novel mTBI therapy to limit SNS activity and mTBI pathophysiology in the mouse. Mice underwent repetitive mTBI or sham injury followed by i.p. saline or propranolol. Isolated mRNA derived from femur bone marrow of mice was assayed for changes in gene expression at one day, one week, and four weeks using Nanostring nCounter® stem cell characterization panel. Results Differential gene expression analysis for bone marrow uncovered significant changes in many genes following drug alone, mTBI alone and drug combined with mTBI. Discussion Our data displays changes in mRNA at various timepoints, most pronounced in the mTBI propranolol group, suggesting a single dose propranolol injection as a viable future mTBI therapy in the acute setting.
Coherent diffraction of hydrogen through the 246 pm lattice of graphene

We study the diffraction of neutral hydrogen atoms through suspended single-layer graphene using molecular dynamics simulations based on density functional theory. Although the atoms have to overcome a transmission barrier, we find that the de Broglie wave function for H at 80 eV has a high probability to be coherently transmitted through about 18% of the graphene area, contrary to the case of He. We propose an experiment to realize the diffraction of atoms at the natural hexagon lattice period of 246 pm, leading to a more than 400-fold increase in beam separation of the coherently split atomic wave function compared to diffraction experiments at state-of-the art nano-machined masks. We expect this unusual wide coherent beam splitting to give rise to novel applications in atom interferometry.
SARS-CoV-2 infection as a potential risk factor for the development of cancer

The COVID-19 pandemic has a significant impact on public health and the estimated number of excess deaths may be more than three times higher than documented in official statistics. Numerous studies have shown an increased risk of severe COVID-19 and death in patients with cancer. In addition, the role of SARS-CoV-2 as a potential risk factor for the development of cancer has been considered. Therefore, in this review, we summarise the available data on the potential effects of SARS-CoV-2 infection on oncogenesis, including but not limited to effects on host signal transduction pathways, immune surveillance, chronic inflammation, oxidative stress, cell cycle dysregulation, potential viral genome integration, epigenetic alterations and genetic mutations, oncolytic effects and reactivation of dormant cancer cells. We also investigated the potential long-term effects and impact of the antiviral therapy used in COVID-19 on cancer development and its progression.
Megacity Challenges: Safe Food and Education– : an exploration of challenges and possible ICT solutions

In 2012, 50 % of the world’s citizens lived in urban areas. By the year of 2050 that number is estimated to have increased to 70 % with the biggest growth of cities seen in Asia and Africa. Megacities, urban areas with an excess of 10 million inhabitants, are increasing in number and will bring new challenges as well as opportunities. One of humans’ basics needs of survival is food. Today more and more food scandals about dangerous additives or chemicals, fake food, and untruthful listing of ingredients are surfacing in the media. At the same time knowledge about food and where it comes from is decreasing. This report takes a closer look at today’s status of food safety in a megacity context, what this will meanfor all the stakeholders in the food chain, and if and how an ICT concept could be created for supporting the situation. Investigating this makes it possible to learn more about if, why, and in what context an ICT concept could serve a megacity’s inhabitants. The context of the megacity was defined as Beijing due to many food scandals and the fast-paced urbanization trend in China. Information and feedback have been collected during literature research, a survey answered by persons living in Beijing, and brainstorming and ideation sessions that resulted in two examples of possible concepts: FoodBook and FarmConnect. The concepts target problems in transparency, education and corruption for the food industry in Beijing from the consumer’s point of view. The FoodBook is about making safe choices while shopping and being able to get instant and relevant information. As a complement to everyday shopping, FarmConnect is about connecting the consumer with the farmer in an attempt of increasing trust and education. The concepts were discussed during interviews with students from Beijing, now studying in Stockholm, to learn more about potential user’s thoughts on the possibilities and problems with the created ICT solutions. The study has a qualitative approach focusing on the research and conceptualizing stages of the process and does not claim to present any statistically defensible data. The intention of it is merely to show tendencies and raise a discussion. Keywords: Megacity, ICT, Safe food, Education, Beijing, Socioeconomic maturity level
An Optical Aerosol Sensor for Process Monitoring of Aerosol-Jet Printing

Aerosol-Jet Printing (AJP) is a fabrication technology for 3D printed electronic applications ranging from conformal metallization to system-in-package hybrid devices. The desire to achieve improved control of the AJP process has spurred the development of strategies for in-line estimation of key AJP process variables. This paper describes the design and initial testing of a novel optical light scattering sensor for enabling in-line process monitoring and control of aerosolized ink properties for future process control strategies. The sensor measures optical scattering by the AJP aerosol at nine angular positions using silicon photodetectors and LED light sources, providing a path for a compact, cost-effective in-line monitoring solution. An experimental evaluation of the sensor response to varied AJP ink compositions and process parameters was performed. Finally, the optical response of the system was modeled, and a general framework for extracting quantitative measurements of the scattering and absorption coefficients of the AJP transport mist was provided.
Kinematics of a Novel Serial-Parallel, Compliant, Three-Legged Robot

In this article, the solution to the inverse and forward kinematics of a novel three-legged, serial-parallel, compliant robot are presented. The design of the robot aims to combine properties from serial and parallel architectures, thereby targeting an agile, yet precisely controllable robot that possibly allows both for dynamic locomotion and accurate manipulation tasks. The robot embodies the series connection of two parallel architectures, a planar and a spherical mechanism, realized in a highly dense mechanical assembly, allowing for lightweight, functionally redundant and compliant 4-DOF legs. A hybrid compliance behaviour is achieved, serving as a threshold between a stiff and compliant systemic state of the robot. Based on the mechanism design, the study involves the derivation of an alternative, yet complete solution for the inverse and forward kinematics of the spherical parallel manipulator (SPM). The approach utilizes spherical trigonometry and spatial vector geometry and yields a unique solution, while being both easy to implement and numerically efficient, thereby being applicable to real time implementations. Conceptually, a reduction of the mechanism assembly and working modes was directly integrated into the solution terms, drastically simplifying the expressions as they only represents the mechanically meaningful configuration relevant for actual physical systems. In addition to the active joint coordinates, the solution yields a unique set of all passively driven joints. The derived robot kinematics are furthermore verified through a 3D simulation model, showing the robot performing several motions. Thereby, the simulations portray the characteristics of the motor units by means of their corresponding phase profiles, revealing a balanced utilisation.
S-HASH: A Crack Towards Cryptographic Hash Functions

S-HASH is tool based on Perl, is used to hack/crack the cryptographic hash function, which is a combination of message which is defined to be of arbitrary length and a message digest defined of fixed length. For cracking the hash values two techniques namely: dictionary attack and the brute-force attack are used. This paper presents the reviews on several hashing function (like MD5, and various types of SHA) along their cracks.
A Wideband High-Gain Planar Monopulse Array Antenna for Ka-Band Radar Applications

In this article, a wideband planar monopulse array antenna operating at Ka-band fed by the substrate integrated coaxial line (SICL) comparator network is proposed. The broadband SICL comparator is compactly deployed in the center of the feeding layer and integrated with the wideband stacked patch antennas on a multilayer printed circuit board (PCB), contributing to improved bandwidth, gain, and total efficiency. Moreover, the millimeter-wave (mmWave) front-end circuits can also be seamlessly integrated on the same board. Prototypes of <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$16\times16$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$32\times32$ </tex-math></inline-formula> monopulse array antennas are designed and fabricated. The measured results show a good agreement with the simulated ones. The <inline-formula> <tex-math notation="LaTeX">$32\times32$ </tex-math></inline-formula> array antenna occupies a compact footprint of only <inline-formula> <tex-math notation="LaTeX">$21.76\lambda _{0} \times 21.76\lambda _{0} \times 0.303\lambda _{0}$ </tex-math></inline-formula> while achieving an impedance bandwidth of 28.8% (28.8–38.5 GHz), a 3-dB gain bandwidth of 25% (29.25–37.6 GHz), and a peak gain of 28.6 dBi for the sum beam. The measured null depth is −56.49 dB, which indicates that excellent amplitude and phase balance is achieved by the proposed SICL comparator network. Finally, the gain variation with increasing array scales and the insertion losses of the comparator feeding networks are also investigated. The demonstrated performance merits of the proposed monopulse array antenna make it a competitive candidate for various Ka-band radar applications, such as low-cost seekers and autonomous driving.
JPEG Compliant Compression for DNN Vision

Conventional image compression techniques are mostly developed for the human visual system. However, with the extensive use of deep neural networks (DNNs), more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we first propose a new distortion measure, dubbed the sensitivity weighted error (SWE). Then, we develop OptS, a DNN-oriented compression algorithm with full JPEG compatibility, which designs optimal quantization tables for DNN models based on SWE. To test the performance of our algorithm, experiments of image classification are conducted on the ImageNet dataset for two prevailing DNN models. Results demonstrate that our algorithm achieves better rate-accuracy (R-A) performance than the default JPEG. For some DNN model, the compression ratio of our algorithm can reach 8.3×1, reducing the compression rate (bits per pixel, bpp) of the default JPEG by 57.4% with no accuracy loss. Our source code is available at https://github.com/zkxufo/OptS.git.
High-Fidelity and Pitch-Controllable Neural Vocoder Based on Unified Source-Filter Networks

We introduce unified source-filter generative adversarial networks (uSFGAN), a waveform generative model conditioned on acoustic features, which represents the source-filter architecture in a generator network. Unlike the previous neural-based source-filter models in which parametric signal process modules are combined with neural networks, our approach enables unified optimization of both the source excitation generation and resonance filtering parts to achieve higher sound quality. In the uSFGAN framework, several specific regularization losses are proposed to enable the source excitation generation part to output reasonable source excitation signals. Both objective and subjective experiments are conducted, and the results demonstrate that the proposed uSFGAN achieves comparable sound quality to HiFi-GAN in the speech reconstruction task and outperforms WORLD in the <inline-formula><tex-math notation="LaTeX">$\text{F}_{0}$</tex-math></inline-formula> transformation task. Moreover, we argue that the <inline-formula><tex-math notation="LaTeX">$\text{F}_{0}$</tex-math></inline-formula>-driven mechanism and the inductive bias obtained by source-filter modeling improve the robustness against unseen <inline-formula><tex-math notation="LaTeX">$\text{F}_{0}$</tex-math></inline-formula> in training as shown by the results of experimental evaluations. Audio samples are available at our demo site at <uri>https://chomeyama.github. io/PitchControllableNeuralVocoder-Demo/</uri>.
Adiabatic quantum-flux-parametron with π Josephson junctions

Superconductor/ferromagnet/superconductor Josephson junctions across which the phases of superconducting order parameters are shifted by π in the ground state are called π Josephson junctions (π-JJs). Recently, several groups have established the fabrication technology of π-JJs so that superconductor logic families exploit π-JJs to improve circuit performance. In this paper, we investigate the advantages of π-JJs in adiabatic quantum-flux-parametron (AQFP) logic, which is an adiabatic superconductor logic based on the quantum-flux-parametron. First, we show that AQFP logic with π-JJs (π-AQFP) can operate using only π-JJs and can be powered and clocked in the same way as the conventional AQFP logic. Then, we show that π-AQFP logic can realize inverters without using signal transformers, which generally occupy a large area in AQFP gates and are difficult to miniaturize. We demonstrate π-AQFP inverters using numerical simulation to establish that π-AQFP logic operates adiabatically and robustly. Our results show that π-JJs can be easily incorporated into AQFP logic and enhance the circuit density of AQFP logic.Superconductor/ferromagnet/superconductor Josephson junctions across which the phases of superconducting order parameters are shifted by π in the ground state are called π Josephson junctions (π-JJs). Recently, several groups have established the fabrication technology of π-JJs so that superconductor logic families exploit π-JJs to improve circuit performance. In this paper, we investigate the advantages of π-JJs in adiabatic quantum-flux-parametron (AQFP) logic, which is an adiabatic superconductor logic based on the quantum-flux-parametron. First, we show that AQFP logic with π-JJs (π-AQFP) can operate using only π-JJs and can be powered and clocked in the same way as the conventional AQFP logic. Then, we show that π-AQFP logic can realize inverters without using signal transformers, which generally occupy a large area in AQFP gates and are difficult to miniaturize. We demonstrate π-AQFP inverters using numerical simulation to establish that π-AQFP logic operates adiabatically and robustly. Our results .
Long Hours and Longings: Australian Children's Views of Fathers' Work and Family Time

Using two waves of paired data from a population sample of 10 ‐ to 13 ‐ year ‐ old Australian children (5,711 father–child observations), the authors consider how the hours, schedules, intensity, and flexibility of fathers' jobs are associated with children's views about fathers' work and family time. A third of the children studied considered that their father works too much, one eighth wished that he did not work at all, and one third wanted more time with him or did not enjoy time together. Logistic regression modeling revealed that working on weekends, being time pressured, being unable to vary start and stop times, and working long hours generated negative views in children about fathers' jobs and time together. The time dilemmas generated by fathers' work devotions and demands are salient to and subjectively shared by their children.
Performance analysis of attention mechanism and teacher forcing ratio based on machine translation

The Seq2seq model has the characteristics that the encoder is used to analyze the input sequence, and the decoder is used to generate the output sequence, which is very suitable for the characteristics of machine translation tasks, making it achieve advanced performance in the field of machine translation. In addition, the attention mechanism has achieved remarkable results in many kinds of natural language processing tasks. The combination of seq2seq model and attention mechanism is the critical research direction in machine translation. To compare the effects of different attention mechanisms on the performance of machine translation models, this paper selects two classical attention mechanisms, including Synthetic Attention and Muti-head Attention, to test machine translation tasks. In addition, for another critical set of the sequence model, teacher-forcing, this paper also tests the influence of different teacher-forcing thresholds on the model performance. The experimental results show that Muti-head Attention has apparent advantages over Synthetic Attention. In addition, the reasonable choice of teacher-forcing also affects the stability of the model in the real environment.
Performance comparison among VGG16, InceptionV3, and resnet on galaxy morphology classification

This article introduces the structures of three classical convolutional neural networks: VGG16, InceptionV3, and ResNet50, and compares their performance on galaxy morphology classification. The different structures of these networks give them some distinct features, leading to different results. The dataset used was Galaxy10 DECals created by the Galaxy Zoo project. The models were compared in the same way, using one fully-connected layer only without modifying its original architecture, evaluated by accuracy, precision, recall rate, and f1 score. The experimental results show that InceptionV3 gave the best classification results from all aspects. The inception module performs well on the galaxy morphology classification problem.
The gap between theoretical computational theories and application models

This paper examined two key generalization assumptions in the theoretical and computational fields and discussed three dominant pre-training models from multiple computer science concentrations. Then, this paper illustrated the increasingly severe gap between theoretical research and model applications for current machine learning studies. This paper discussed four advanced concepts in academia regarding possible solutions for the gap. The researchers developed these concepts to redirect future machine learning work by helping the academia re-understand the concept of “learning” and its appropriate implementation in the machine learning field. Finally, one possible reason for such a gap to exist is explained, and based on the interpretation of advanced concepts, possible solutions the academia can try to implement are also discussed.
A layer-based random SGD for distributed training of RNNs

Since frequent data communication leads to increased time consumption in distributed training, compression of communication data is one of the effective ways to speed up the whole training process. Many existing compression methods introduce extra time overhead in data compression and decompression, for instance, Top-K and Random-k. There are also methods that can reduce the extra time overhead but suffer from the shortcoming of insufficient generalization. For example, the layer-based compression LR-SGD (Layer-based Random Stochastic Gradient Descent) cannot be applied to the training of recurrent neural networks. In this paper, an improved method based on LR-SGD is proposed. By being divided into virtual layers for compression, this improved method is able to train various networks efficiently. To validate the proposed method, experiments were conducted using different neural networks on a stimulative cluster. The results demonstrate that the proposed method applies well on different networks with low communication overhead and a nice convergence effect.
The advance of underdetermined linear regression optimization based on implicit bias

The gradient descent method has the characteristics of easy realization and simple structure. The traditional gradient descent method has many advantages, especially in solving convex optimization problems. In recent years, some researchers have noticed that the gradient descent algorithm is helpful to solve the problem of underdetermined linear regression optimization. Therefore, in order to explore the specific relationship between gradient descent and under-determined linear regression optimization, this paper focuses on a case with a unique finite root loss function and discusses in detail the working principles of the natural-gradient-descent, mirror-descent, and gradient-descent in this case, and proves the auxiliary effect of gradient descent on under-determined linear regression optimization theoretically. How to use the characteristics of the gradient descent algorithm to design a suitable model, and how do we use the idea of the gradient descent algorithm to find an answer for problem of under determined linear regression optimization in the practical application will be the future research work to continue to advance.