Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Representation Matters in Device Placement

Modern Neural Network (NN) models require more data and parameters to perform ever more complicated tasks. One approach to train a massive NN is to distribute it across multiple devices. This approach raises a problem known as the device placement problem. Most of the state-of-the-art solutions that tackle this problem leverage graph embedding techniques. In this work, we assess the impact of different graph embedding techniques on the quality of device placement, measured by (i) the execution time of partitioned NN models, and (ii) the computation time of the graph embedding technique. In particular, we expand Placeto, a state-of-the-art device placement solution, and evaluate the impact of two graph embedding techniques, GraphSAGE and P-GNN, compared to the original Placeto graph embedding model, Placeto-GNN. In terms of the execution time improvement, we achieve an increase of 23.967% when using P-GNN compared to Placeto-GNN, while GraphSAGE produces 1.165% better results than Placeto-GNN. Regarding computation time, GraphSAGE has a gain of 11.569% compared to Placeto-GNN, whereas P-GNN is 6.95% slower than it.
Combining the top-down propagation and bottom-up enumeration for inductive program synthesis

We present an effective method for scalable and general-purpose inductive program synthesis. There have been two main approaches for inductive synthesis: enumerative search, which repeatedly enumerates possible candidate programs, and the top-down propagation (TDP), which recursively decomposes a given large synthesis problem into smaller subproblems. Enumerative search is generally applicable but limited in scalability, and the TDP is efficient but only works for special grammars or applications. In this paper, we synergistically combine the two approaches. We generate small program subexpressions via enumerative search and put them together into the desired program by using the TDP. Enumerative search enables to bring the power of TDP into arbitrary grammars, and the TDP helps to overcome the limited scalability of enumerative search. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a broad class of inductive synthesis problems. We have implemented our approach in a tool called Duet and evaluate it on SyGuS benchmark problems from various domains. We show that Duet achieves significant performance gains over existing general-purpose as well as domain-specific synthesizers.
Rapid Syntheses of Oligo(2,5-thiophene ethynylene)s with Thioester Termini: Potential Molecular Scale Wires with Alligator Clips

The syntheses of soluble oligo(3-ethyl-2,5-thiophene ethynylene)s via an iterative divergent/convergent approach starting from 3-ethyl-2-(trimethylsilylethynyl)thiophene are described. The monomer, dimer, tetramer, octamer, and 16-mer were synthesized. The 16-mer is 100 A long in its minimized extended zigzag conformation. At each stage in the iteration, the length of the framework doubles. Only three sets of reaction conditions are needed for the entire iterative synthetic sequence: an iodination, a protodesilylation, and a Pd/Cu-catalyzed cross coupling. The oligomers were characterized spectroscopically and by mass spectrometry. The optical properties are presented which show that at the octamer stage, the optical absorbance maximum is nearly saturated. The size exclusion chromatography values for the number average weights, relative to polystyrene, illustrate the tremendous differences in the hydrodynamic volume of these rigid rod oligomers verses the random coils of polystyrene. These differences be.
Paradoxes of probabilistic programming: and how to condition on events of measure zero with infinitesimal probabilities

Abstract Probabilistic programming languages allow programmers to write down conditional probability distributions that represent statistical and machine learning models as programs that use observe statements. These programs are run by accumulating likelihood at each observe statement, and using the likelihood to steer random choices and weigh results with inference algorithms such as importance sampling or MCMC. We argue that naive likelihood accumulation does not give desirable semantics and leads to paradoxes when an observe statement is used to condition on a measure-zero event, particularly when the observe statement is executed conditionally on random data. We show that the paradoxes disappear if we explicitly model measure-zero events as a limit of positive measure events, and that we can execute these type of probabilistic programs by accumulating infinitesimal probabilities rather than probability densities. Our extension improves probabilistic programming languages as an executable notation for probability distributions by making it more well-behaved and more expressive, by allowing the programmer to be explicit about which limit is intended when conditioning on an event of measure zero.
MAS-SEIR-II Simulation on COVID-19 in China

The spread of COVID-19 around the world has profoundly affected the process of world development, making significant changes in people's behavior, government operations, and business operations. The spread of COVID-19 is a complex process, closely related to isolation patterns, population risk perception, geospatial and other factors, and simulation of the spread of covid-19 under the prevention and control policy is an important method to test the effectiveness of prevention and control measures. Using the MAS-SEIR-II model to carry out scenario analysis, the results we got show that establishing a cubicle hospital for rapid isolation of susceptible populations, flattening grassroots prevention and control organizations, and establishing a modern emergency logistics system are effective prevention and control measures.
A passive controlled hand rehabilitation instrument

The number of people with upper and lower limb disabilities is very large in our country. Many patients with upper and lower limb motor dysfunction due to various natural or man-made disasters, in addition to the use of traditional physical therapy in hospitals, also has applied in the rehabilitation treatment limb training to the rehabilitation robot technology. Rehabilitation robot can help patients to restore limb flexibility and self-control, thus speeding up the process of rehabilitation. Therefore, research and development of more advanced functional rehabilitation machine than the market for patients with Limb Movement disorders is of great significance, but also for the health of the elderly and help the development of public welfare disability is very useful.
Web Attack Detection through Network-Traffic-Based Feature Engineering and Machine Learning

The design of feature set is a basic research direction of anomaly detection technology. The characterization ability of feature set directly affects the accuracy and generalization ability of intrusion detection algorithm. KDD CUP 99 is the most widely used data set in anomaly detection related research papers, but the feature set needs to analyze the payload content characteristics of packets, so the feature set cannot be applied to the encrypted traffic. CICIDS2017 is also a widely used data set. However, because data in PCAP format can only be used as input, the generalization ability of the model trained by this data set is poor when it is used for real-time attack detection. Based on the above problems, a feature set based on network flow characteristics is designed and implemented in this paper, which includes the time-window-based statistical features related to network traffic and host-related statistical features. Experiments show that the anomaly detection model trained by the feature set can meet the requirements of both real-time and classification accuracy.
Research on Text Error Correction Algorithm after Automatic Speech Recognition Based on Pragmatic Information

Error correction for automatic speech recognition text is an indispensable part of artificial intelligence. At present, speech to text (STT) has widely requirements for the processing of pragmatic information. The text correct rate in STT is the foundation for NLP. Aiming at the text error problems of traditional error correction methods that cannot understand semantics and sentence meanings well. The proposed method used the long and short-term memory neural network (LSTM) algorithm with monte-carlo tree search in this paper. The text error leads to mistake in semantic slot filling for NLP. Therefore, the proposed combined algorithm and optimization method solved the problem by experiments. The results verified the accuracy increased 25% for the telephone inquiry by text error correction.
A Two Dimensional Performance Measurement Approach for Modeling Technological Strategic Decisions in Organisations

There is always a concern among strategic managers working in the technological areas of services and applications of how to satisfy their customers and users for the benefit of their organisations. This may be a challenge that they have to consider however it may be a limited focus. The more challenging is how to integrate a missing dimension that may be overlooked. This dimension is the level of satisfaction of their employees who design, develop and implement these needed technologies that the customer will benefit from. It is, though, that strategic managers need to focus on the satisfaction level that their technological employees require to produce a high value technological service. Thus in order to achieve a high competitive advantage technological organisations strategic managers have to focus on achieving a balance between their customers' satisfaction and their technological employees' satisfaction and in the same time define long term technological strategies that achieve this goal and ultimately reach a continuous win-win situation between both dimensions. This paper suggests the design and implementation of a two dimensional performance measurement approach based on satisfying the needs of the technological employees (Es), as the internal dimension of the organisation, and the customers and technology users (Cs), as the external dimension, and shows its impact on modeling and selecting the appropriate performance technological strategic decisions in the organisation. The concept used in this performance approach employs the multi criteria Simple Multi-Attribute Rating Technique [SMART] as a multi decision making modeling technique. This technique is applied in a case study for improving the technological strategic decisions and performance measurement of a manufacturing company operating in the middle east region.
Ion Exchange Resin Bags: Factors Affecting Estimates of Nitrogen Availability1

Soil ion concentration, water regime, and microbial competition for N were examined for their importance in regulating the adsorption of ammonium and nitrate by ion-exchange resin bags. Resin nitrate values were largely dependent on soil nitrate contents, whereas resin ammonium values were strongly affected by soil water regime. Increasing microbial competition for N by additions of cellulose markedly decreased N accumulation in the resin bags. Recovery of "NH4 added to resin bags prior to incubation in the soil showed no indication of removal or transformation of N once adsorbed in the resin bags. Resin bags should be appropriate for field estimation of N availability when both mineralization and transport are of interest. Additional Index Words: mineralization, nitrification. Binkley, D. 1984. Ion exchange resin bags: factors affecting estimates of nitrogen availability. Soil Sci. Soc. Am. J. 48:1181-1184. T availability of nitrogen to tree roots in forest soils is a function of mineralization, immobilization, and transport processes. Recently, Binkley and Matson (1983) used ion-exchange resin bags to examine soil N availability under controlled environmental conditions and under field conditions. In the greenhouse, the ammonium plus nitrate accumulated by the resin bags correlated well with chemical and biological indexes of N availability. In the field, the resin bags appeared to respond to soil morphological and microenvironmental conditions that did not apply in the assays in a controlled environment. The study reported here was designed to examine three factors affecting the accumulation of nitrogen ions by the resin bags: (i) ion charge and concentrations, (ii) water regimes, and (iii) competition with plants and microbes for mineralized N.
Performance Analysis With Different Types of Visual Stimuli in a BCI-Based Speller Under an RSVP Paradigm

Brain-Computer Interface (BCI) systems enable an alternative communication channel for severely-motor disabled patients to interact with their environment using no muscular movements. In recent years, the importance of research into non-gaze dependent brain-computer interface paradigms has been increasing, in contrast to the most frequently studied BCI-based speller paradigm (i.e., row-column presentation, RCP). Several visual modifications that have already been validated under the RCP paradigm for communication purposes have not been validated under the most extended non-gaze dependent rapid serial visual presentation (RSVP) paradigm. Thus, in the present study, three different sets of stimuli were assessed under RSVP, with the following communication features: white letters (WL), famous faces (FF), neutral pictures (NP). Eleven healthy subjects participated in this experiment, in which the subjects had to go through a calibration phase, an online phase and, finally, a subjective questionnaire completion phase. The results showed that the FF and NP stimuli promoted better performance in the calibration and online phases, being slightly better in the FF paradigm. Regarding the subjective questionnaires, again both FF and NP were preferred by the participants in contrast to the WL stimuli, but this time the NP stimuli scored slightly higher. These findings suggest that the use of FF and NP for RSVP-based spellers could be beneficial to increase information transfer rate in comparison to the most frequently used letter-based stimuli and could represent a promising communication system for individuals with altered ocular-motor function.
Organotypic Brain Slice Culture Microglia Exhibit Molecular Similarity to Acutely-Isolated Adult Microglia and Provide a Platform to Study Neuroinflammation

Microglia are central nervous system (CNS) resident immune cells that have been implicated in neuroinflammatory pathogenesis of a variety of neurological conditions. Their manifold context-dependent contributions to neuroinflammation are only beginning to be elucidated, which can be attributed in part to the challenges of studying microglia in vivo and the lack of tractable in vitro systems to study microglia function. Organotypic brain slice cultures offer a tissue-relevant context that enables the study of CNS resident cells and the analysis of brain slice microglial phenotypes has provided important insights, in particular into neuroprotective functions. Here we use RNA sequencing, direct digital quantification of gene expression with nCounter® technology and targeted analysis of individual microglial signature genes, to characterize brain slice microglia relative to acutely-isolated counterparts and 2-dimensional (2D) primary microglia cultures, a widely used in vitro surrogate. Analysis using single cell and population-based methods found brain slice microglia exhibited better preservation of canonical microglia markers and overall gene expression with stronger fidelity to acutely-isolated adult microglia, relative to in vitro cells. We characterized the dynamic phenotypic changes of brain slice microglia over time, after plating in culture. Mechanical damage associated with slice preparation prompted an initial period of inflammation, which resolved over time. Based on flow cytometry and gene expression profiling we identified the 2-week timepoint as optimal for investigation of microglia responses to exogenously-applied stimuli as exemplified by treatment-induced neuroinflammatory changes observed in microglia following LPS, TNF and GM-CSF addition to the culture medium. Altogether these findings indicate that brain slice cultures provide an experimental system superior to in vitro culture of microglia as a surrogate to investigate microglia functions, and the impact of soluble factors and cellular context on their physiology.
Applying Deep Learning Techniques to Estimate Patterns of Musical Gesture

Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to extract the spatiotemporal dynamics of each gesture. Applying state-of-the-art deep neural networks, we implemented and compared different architectures where convolutional neural networks (CNN) models demonstrated recognition rates of 97.147%, 3DMultiHeaded_CNN models showed rates of 98.553%, and rates of 99.234% were demonstrated by CNN_LSTM models. The collected data (quaternion of the bowing arm of a violinist) contained sufficient information to distinguish the bowing techniques studied, and deep learning methods were capable of learning the movement patterns that distinguish these techniques. Each of the learning algorithms investigated (CNN, 3DMultiHeaded_CNN, and CNN_LSTM) produced high classification accuracies which supported the feasibility of training classifiers. The resulting classifiers may provide the foundation of a digital assistant to enhance musicians' time spent practicing alone, providing real-time feedback on the accuracy and consistency of their musical gestures in performance.
Targeting Mitochondrial Impairment in Parkinson's Disease: Challenges and Opportunities

The underlying pathophysiology of Parkinson's disease is complex, but mitochondrial dysfunction has an established and prominent role. This is supported by an already large and rapidly growing body of evidence showing that the role of mitochondrial (dys)function is central and multifaceted. However, there are clear gaps in knowledge, including the dilemma of explaining why inherited mitochondriopathies do not usually present with parkinsonian symptoms. Many aspects of mitochondrial function are potential therapeutic targets, including reactive oxygen species production, mitophagy, mitochondrial biogenesis, mitochondrial dynamics and trafficking, mitochondrial metal ion homeostasis, sirtuins, and endoplasmic reticulum links with mitochondria. Potential therapeutic strategies may also incorporate exercise, microRNAs, mitochondrial transplantation, stem cell therapies, and photobiomodulation. Despite multiple studies adopting numerous treatment strategies, clinical trials to date have generally failed to show benefit. To overcome this hurdle, more accurate biomarkers of mitochondrial dysfunction are required to detect subtle beneficial effects. Furthermore, selecting study participants early in the disease course, studying them for suitable durations, and stratifying them according to genetic and neuroimaging findings may increase the likelihood of successful clinical trials. Moreover, treatments involving combined approaches will likely better address the complexity of mitochondrial dysfunction in Parkinson's disease. Therefore, selecting the right patients, at the right time, and using targeted combination treatments, may offer the best chance for development of an effective novel therapy targeting mitochondrial dysfunction in Parkinson's disease.
Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “. all the impressive achievements of deep learning amount to just curve fitting. ” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality. ” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all.
Long-Term Exercise Reduces Formation of Tubular Aggregates and Promotes Maintenance of Ca2+ Entry Units in Aged Muscle

Tubular aggregates (TAs) in skeletal muscle fibers are unusual accumulation of sarcoplasmic reticulum (SR) tubes that are found in different disorders including TA myopathy (TAM). TAM is a muscular disease characterized by muscle pain, cramping, and weakness that has been recently linked to mutations in STIM1 and ORAI1. STIM1 and ORAI1 are the two main proteins mediating store-operated Ca2+ entry (SOCE), a mechanism activated by depletion of intracellular Ca2+ stores (e.g., SR) that allows recovery of Ca2+ from the extracellular space during repetitive muscle activity. We have recently shown that exercise triggers the formation of unique intracellular junctions between SR and transverse tubules named Ca2+ entry units (CEUs). CEUs promote colocalization of STIM1 with ORAI1 and improve muscle function in presence of external Ca2+. TAs virtually identical to those of TAM patients are also found in fast-twitch fibers of aging male mice. Here, we used a combination of electron and confocal microscopy, Western blotting, and ex vivo stimulation protocols (in presence or absence of external Ca2+) to evaluate the presence of TAs, STIM1-ORAI1 localization and expression and fatigue resistance of intact extensor digitorum longus (EDL) muscles in wild-type male adult (4-month-old) and aged (24-month-old) mice and in mice trained in wheel cages for 15 months (from 9 to 24 months of age). The results collected indicate that (i) aging causes STIM1 and ORAI1 to accumulate in TAs and (ii) long-term exercise significantly reduced formation of TAs. In addition, (iii) EDL muscles from aged mice exhibited a faster decay of contractile force than adult muscles, likely caused by their inability to refill intracellular Ca2+ stores, and (iv) exercise in wheel cages restored the capability of aged EDL muscles to use external Ca2+ by promoting maintenance of CEUs. In conclusion, exercise prevented improper accumulation of STIM1 and ORAI1 in TAs during aging, maintaining the capability of aged muscle to refill intracellular Ca2+ stores via SOCE.