Miracle-dz/UniFAR-FTUs huggingface dataset:
To grasp or not to grasp: an end-to-end deep-learning approach for predicting grasping failures in soft hands

This paper tackles the challenge of predicting grasp failures in soft hands before they happen, by combining deep learning with a sensing strategy based on distributed Inertial Measurement Units. We propose two neural architectures, which we implemented and tested with an articulated soft hand - the Pisa/IIT SoftHand - and a continuously deformable soft hand - the RBO Hand. The first architecture (Classifier) implements a-posteriori detection of the failure event, serving as a test-bench to assess the possibility of extracting failure information from the discussed input signals. This network reaches up to 100% of accuracy within our experimental validation. Motivated by these results, we introduce a second architecture (Predictor), which is the main contribution of the paper. This network works on-line and takes as input a multidimensional continuum stream of raw signals coming from the Inertial Measurement Units. The network is trained to predict the occurrence in the near future of a failure event. The Predictor detects 100% of failures with both hands, with the detection happening on average 1.96 seconds before the actual failing occurs - leaving plenty of time to an hypothetical controller to react.
Classification of components of affective touch using rapidly-manufacturable soft sensor skins

Affective touches play an important role in everyday communication because a significant amount of human interactions are through physical contacts. To facilitate robust, safe human-robot interactions (HRIs), we require robots with soft skins that can interpret affective touches. In this paper, we describe the fabrication of rapidly manufacturable, soft sensor skins using liquid metal embedded silicone elastomer as a resistive element and trained a recurrent neural network (RNN) to distinguish between a variety of pokes and rubs from human users. On a $2 \times 2$ sensor array, we obtained an average classification accuracy of 97% for ten different types of pokes and rubs, demonstrating that the combination of a soft sensor and machine learning can classify the interactions. Our approach is a step towards intelligent soft robots that can understand social interactions through touches.
Post-Silicon Validation of the IBM POWER9 Processor

Due to the complexity of designs, post-silicon validation remains a major challenge with few systematic solutions. We provide an overview of the state-of-the-art post silicon validation process used by IBM to verify its latest IBM POWER9 processor. During the POWER9 post-silicon validation, we detected and handled 30% more logic bugs in 80% of the time, as compared to the previous IBM POWER8 bring-up. This improvement is the result of lessons learned from previous designs, leading to numerous innovations. We provide bug analysis data and compare it to POWER8 results. We demonstrate our methodology by describing several bugs from fail detection to root cause.
PATS: A New Neural Network Activation Function with Parameter

Activation function is crucial to the recent successes of deep neural networks. In this paper, we propose a new activation function with parameters, named PATS. Specifically, PATS is a non-monotonic function which combines arctangent function and sigmoid function. In the process of network model training, the parameter of PATS is a random number from the uniform distribution, which improves the flexibility of network model and reduces the risk of over fitting. In addition, PATS can be widely used in existing deep network models. We use several classic deep network models to test the performance of PATS. The experimental results on CIFAR-10 and CIFAR-100 datasets show that compared with other activation functions, PATS has better performance in improving the learning ability and robustness of deep network model. The code is available at https://github.com/zbygithub/PATS
Impact of LER on Mismatch in Nanosheet Transistors for 5nm-CMOS

In this paper, we have reported the impact of line edge roughness (LER) on the electrical characteristics of Nanosheet field effect transistors (NSFET). We have generated a 3-D LER profile using 2-D Auto Covariance Function (ACVF) that considers two degrees of freedom for a realistic roughness in fin-type, non-planar MOSFETs. For a complete study of 3-D LER effect in NSFET, we have considered roughness along the nanosheet's sidewalls as well as its top and bottom surfaces. We have shown using 3D TCAD simulations that the sidewall roughness contributes to a negligible mismatch in NSFET characteristics. The mismatch performance of NSFET is compared with that of the nanowire field effect transistor (NWFET) for 5nm technology node. NSFET appears to be more immune to mismatch in ON current than NWFETs considered in this work. On the other hand, as compared with NSFET, owing to its superior gate all-around control, the NWFET achieves lower mismatch in drain induced barrier lowering (DIBL) and subthreshold slope (SS) in presence of LER.
MIND: Message Classification Based Controller Scheduling Method for Resisting DDoS Attack in Software-Defined Networking

Distributed Denial of Service (DDoS) is quite a serious security issue existing in Software-Defined Networking (SDN). In order to mitigate DDoS attack, we present MIND, a message classification based controller scheduling method, where how to store and process messages is designed based on analyzing and classifying the OpenFlow messages received by SDN controller. The experiment results validate that the proposed controller scheduling method can significantly improve the availability of SDN controller under DDoS attack.
Saving Power by Converting Flip-Flop to 3-Phase Latch-Based Designs

Latches are smaller and lower power than flip-flops (FFs) and are typically used in a time-borrowing master-slave configuration. This paper presents an automatic flow for converting arbitrarily-complex single-clock-domain FF-based RTL designs to efficient 3-phase latch-based designs with reduced number of required latches, saving both register and clock-tree power. Post place-and-route results demonstrate that our 3-phase latch-based designs save an average of 15.5% and 18.5% power on a variety of ISCAS, CEP, and CPU benchmark circuits, compared to their more traditional FF and master-slave based alternatives.
CELL-SIZE DISTRIBUTIONS OF ZOOXANTHELLAE IN CULTURE AND SYMBIOSIS

Zooxanthellae maintained in batch culture at 27°C with a 12 h:l2 h light/dark cycle exhibited different cell-size distributions and morphology with growth phase. Mean cell diameter of cultured zooxanthellae isolated from Zoanthus sociatus increased with time of culture and growth phase from 6 µm(day 3) to 12.5 µm (day 11, late log-phase growth). Maximum specific growth rates (µmax units) ranged from 0.3 to 0.4 day-1. Within three hours after the onset of the light period on the third day of culture (early log-phase growth), large numbers of motile cells appeared. Subsequently, the dominant cell type was non-motile, vegetative, and possessed an accumulation body which increased in size with culture time. Zooxanthellae freshly isolated from a variety of hosts in their natural environment exhibited different cell-size distributions on an inter- and intra-host basis. Based on the laboratory results with cultured zooxanthellae, varying size distributions in fresh isolates of zooxanthellae from natural hosts ma.
Mathematical Model of Pitch Channel of Multi-rotor Aircraft

The multi-rotor aircraft is more and more widely used. In this paper, the mathematical model of Transfer Function is established for the pitching channel model of multi-rotor aircraft. The Transfer Function of the system is an unstable function. Then PID controller is applied to the system to establish the Closed Loop Transfer Function of the system. The stability control of the system is realized by using Routh-Hurwitz criterion and selecting proper PID parameters. Finally, MATLAB is used for simulation.
MiniCoRe: A Miniature, Foldable, Collision Resilient Quadcopter

Collision management strategies are an integral part of micro air vehicle (MAV) operation for flight sustainability. Among them, collision avoidance strategies require enhanced environmental and situational awareness for generating evasive maneuvers and collision-free trajectories. Simpler and more adaptable option is to prepare for collisions and design the physical system with predicted collision patterns in mind. In this work, a mechanically compliant quadcopter design using origami-inspired foldable robotics methods with protective shock absorbing elements has been proposed for a collision resilient quad-rotor MAV. 2D design of the foldable structure and the manufacturing process, including electronic hardware elements and software has been discussed. Our results show that in low speed collisions, the flight of the quadcopter is uninterrupted. The compliant quadcopter can continue flight after impact in near-hover conditions because of the reduction of impact forces due to the increased impact time.
Multi-Source Data Stream Online Frequent Episode Mining

Online frequent episode mining is more complicated than the traditional static frequent episode mining due to the continuous, unbounded and time-varying data stream. Especially in the multiple data streams, online frequent episode mining is more difficult than the single-source stream, due to the concurrency, global clock loss, and uncertainty of delay caused by the distributed environment. To cope with these problems, we propose a new algorithm. Firstly, the data stream with “happen-before” relationship among multiple sources is combined on the global data lattice. Next, the traversal on global data lattice generates effective parallel and serial candidate data streams, which guarantee the accuracy of subsequent mining and reduce the number of global sequences during searching process. Then, we use the frequent episode tree to detect the expanding online serial episodes and parallel episodes. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.
PSB-RNN: A Processing-in-Memory Systolic Array Architecture using Block Circulant Matrices for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are widely used in Natural Language Processing (NLP) applications as they inherently capture contextual information across spatial and temporal dimensions. Compared to other classes of neural networks, RNNs have more weight parameters as they primarily consist of fully connected layers. Recently, several techniques such as weight pruning, zero-skipping, and block circulant compression have been introduced to reduce the storage and access requirements of RNN weight parameters. In this work, we present a ReRAM crossbar based processing-in-memory (PIM) architecture with systolic dataflow incorporating block circulant compression for RNNs. The block circulant compression decomposes the operations in a fully connected layer into a series of Fourier transforms and point-wise operations resulting in reduced space and computational complexity. We formulate the Fourier transform and point-wise operations into in-situ multiply-and-accumulate (MAC) operations mapped to ReRAM crossbars for high energy efficiency and throughput. We also incorporate systolic dataflow for communication within the crossbar arrays, in contrast to broadcast and multicast communications, to further improve energy efficiency. The proposed architecture achieves average improvements in compute efficiency of 44x and 17x over a custom FPGA architecture and conventional crossbar based architecture implementations, respectively.
A Novel 3-D Image Retargeting by using Stereo Seam Carving with Disparity Map Acquisition (DMA) Algorithm

Gaining popularity in digital world imposes the challenge to preserve the semantic significances in the original image while display on any arbitrary device irrespective of its size or aspect ratio is the well-known ‘Image Retargeting’. When the image is to be tailored by focusing on its objectives tenacity, then the insignificant portions of the images are identified and wipe out. The historical method envisages the due respect to the pixels by considering its bottom to top style. On the contrary, the projected method builds by using top-down tactic. This appraisal is fused by ‘Classification guided Fusion Network (CFN). The feature is widely applied on 3D images which fuses left as well as right eye images which are having different viewpoints and differently designed. The disparity map acquisition algorithm fuses the images with semantic collage of the images.
Synthesis of $\mathbf{MoS}_{\mathbf{2}(\mathbf{1}-\mathbf{x})}\mathbf{Te}_{\mathbf{2x}}$ by Sputtering and the Change in the Physical Properties and Structure Depending on the Chalcogen Composition

The research on 2D layered transition metal dichalcogenides (TMDs) is a topic of rapidly growing field owing to its attractive physical properties and variety in applications. In order to expand the application possibility, alloying is carried out resulting in tunability of the bandgap and the band alignment. In this study, $\mathbf{MoS}_{\mathbf{2}(\mathbf{1}-\mathbf{x})}\mathbf{Te}_{\mathbf{2x}}$, alloy of MoS2and MoTe2, is fabricated by simultaneous sputtering of Mos2 and MoTe2. The physical properties and the structure change depending on the sputtering conditions are examined. It was revealed that in order to obtain layer structure and prevent phase separation, patches of Mos2,MoTe2 and $\mathbf{MoS}_{\mathbf{2}(\mathbf{1}-\mathbf{x})}\mathbf{Te}_{\mathbf{2x}}$ forming rather than having a uniformly mixed film, the control of substrate temperature and the amount of chalcogen deficit is critical. That is, when the film shows chalcogen ratio close to that of stoichiometric ratio, the film can be formed with the layer structure and without phase segregation even at relatively high substrate temperature. (Abstract)
Cooperative Memory Expansion via OS Kernel Support for Networked Computing Systems

The growing popularity of in-memory computing for bigdata analytics often causes performance bottlenecks to memory subsystem resided in operating systems (OS). This article purposes cooperative memory expansion (COMEX), an OS kernel extension. COMEX establishes a stable pool of memory collectively across nodes in a cluster and enhances OS's memory subsystem for memory aggregation from connected machines by allowing process's page table to track remote memory page frames without programmer effort or modifications to application codes. COMEX employs Remote Direct Memory Access (RDMA) for low-latency data transfer with destination kernel bypassed and does not rely on an old design of the I/O block subsystem usually adopted by all known remote paging. COMEX fits soundly in the emerging system design approach of resource disaggregation which breaks hard walls between server-centric machines into a new design paradigm of separated resource pools. The new architecture facilitates both system scaling-up and scaling-out, also eliminates imbalance resources existing in datacenters. We have implemented COMEX based on Linux kernel 3.10. 87 and deployed on our 32 networked servers. Performance evaluation results under ten applications from two benchmark suites reveal the speedup of up to 170 times when application execution footprints are 10 times larger than available system memory.
Study of Clinical Profile of CAPD Patients in Eastern India – A Clinical Observation

Background: The prevalence of end-stage renal disease (ESRD) requiring renal replacement therapy is rising steadily in developed and developing countries. Currently, continuous ambulatory peritoneal dialysis (CAPD) and in-center hemodialysis (HD) are the two major modes of dialysis used to treat ESRD. Only 8% of PD patients are initiated on PD directly, 92% are shifted from HD There are several approaches to measuring peritoneal membrane function like peritoneal equilibration test (PET), the standard permeability analysis (SPA). Successful treatment with PD encompasses several important quality targets like Patient survival, Technique survival, Quality of life, Nutrition status. Complications includes Catheter malfunction, Exit site infection, Peritonitis, Fluid leaks. In this observational study of clinical profile of CAPD patients 40 patients were taken. Of the 40 patients included in study 72.5% were male and 27.5% were female. Various factors for preference of CAPD are remote places, expenditure (35%), cardiovascular morbidity (25%). The observations are out of 52 % had well maintained nutrition, KT/V score ranges from 1.2 to 7 with mean (SD) 3.88 ± 1.59, only 22.5% have peritonitis and catheter exit site infections. None had catheter removal. All our outcome (such as correction of anemia, nutritional status, clinical well-being, absence of edema) and biochemical parameters (for e.g. Peritoneal Equilibrium Test, weekly KT/V) will used to knowing the adequacy which is consistent to results of various national and international studies.
High Accuracy Discrimination of Parkinson’s Disease from Healthy Controls by Hand Movements Analysis Using LeapMotion Sensor and 1D Convolutional Neural Network

This study is devoted to the detection of hand motor impairment in Parkinson’s disease (PD) using ID convolutional neural network (CNN). The data were obtained from the LeapMotion sensor when performing hand motor tasks in the group of patients with PD and the control group. CNN consists of2 blocks: three parallel ID convolution blocks and a common fully connected block. CNN was trained on a data set of each hand fo r three motor tasks: finger tapping, finger opening-closing, pronation-supination of the hands. Binary classification (PD veisus non-PD) was performed be the CNN itself, as well as by several conventional classifieis (knearest neighbors, SVM, Decision Tree, and Random Forest) that were used instead of the output neuron of fully connected block of the CNN. Additionally, the best feature set was selected using logistic regression. The testing was conducted in the 8-fold cross-validation mode; the best obtained accuracy of the correct classification was S5.1 % using CNN with the SVM classifier.
Investigation on modification of cassava starch using active components of ginger roots (Zingiber officinale Roscoe)

Effects of ginger grates at concentrations of l, 5 and 20 g per 150 g of cassava starch suspension in 200 ml of water at room temperature were investigated in order to evaluate the potentials of active components of the ginger in modifying pasting and some physicochemical properties of the starch. Pasting properties of the ginger modified cassava starch showed high peak viscosities (487.58 - 420.25 RVU) compared to low peak viscosity (392.33 RVU) of the native starch. The modified starch was characterized with low set back viscosities (64.14 - 57.05 RVU) in relation to native starch with high set back viscosity (66.58 RVU). Similarly, the ginger modified starch was characterized with low gelatinization time (3.45 - 3.36 min) compared to the native starch with high gelatinization time (3.5 min). Ginger modified starch showed high swelling power (12.9 - 9.5) and solubility indices (11.3 - 15.6%) compared to native starch with low swelling power (8.9) and solubility index (3.1%). Therefore active components of ginger root can be used as natural modifying agents for cassava starch.
Fledge: Flexible Edge Platforms Enabled by In-memory Computing

The proliferation of advanced analytics and artificial intelligence has been driven by huge volumes of data that are mostly generated at the edge. Simultaneously, there is a rising demand to perform analytics on edge platforms (i.e., near-sensor data analytics). However, conventional architectures of such platforms may not execute the targeted applications in an energy-efficient manner. Emerging near and in-memory computing paradigms can increase the energy efficiency of edge platforms by relying on emerging logic and memory devices. More importantly, these paradigms enable the possibility of performing computations on unconventional platforms, namely flexible computing systems. In this paper, we explore the benefits of in-memory computing at the edge on a flexible substrate enabled by thin-film transistors (TFTs) and resistive RAM (RRAM). As a case study, we consider bio-signal processing application workloads, i.e., compressive sensing and anomaly detection. We model the device, circuit, and architecture of our targeted platform and evaluate the corresponding system-level performance. Preliminary results indicate that in-memory computing enabled by flexible electronic devices enables a new class of edge platforms with lower power consumption, compared to that of rigid TFT devices.
Notizen: The Pheromone Receptor System of Male Eulia ministrana L., with Notes on Other Cnephasiini Moths

Abstract The antennal Sensilla trichodea of male Eulia ministrana (Tortricidae: Cnephasiini) contain specialist receptor cells for the sex-attractant components (Z)-9-and (Z)-11-tetra-decenyl acetate and the attraction-inhibitors (E)-11 -tetra-decenyl acetate, (Z)-7-dodecenyl acetate, and (Z)-11 -hexa-decenyl acetate. The significance of the latter two compounds as potential sex pheromone components in Tortricidae is considered.
Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

Emotions play an important role in human interaction and decision-making processes. Frontal asymmetry in brain activity is a promising neurophysiological indicator of emotion. Emotions are psychologically explained by the valence-arousal model, but as yet, frontal asymmetry has not been fully explained by this model. In this study, we explored frontal asymmetry of emotions based on the valence-arousal model using the same auditory stimulus. Changes in emotional states using self-report questionnaires were investigated before and after the auditory stimulus. Spectral power and weighted phase lag index were calculated in the delta, theta, alpha, beta, and gamma bands. Phase-amplitude coupling was also measured to explore communication among different frequency bands associated with emotions. After the auditory stimulus, alpha power decreased in both left and right frontal regions and the delta-weighted phase lag index in the left-right regions was increased. However, no frontal asymmetry was identified after the auditory stimulus. Additionally, we explored the brain changes according to the valence-arousal model based on emotional states. After the auditory stimulus, frontal asymmetry of alpha power was clearly observed only for negative valence. This finding was possible because subjective emotions were considered despite listening to the same stimulus. Finally, phase-amplitude coupling identified left-hemisphere dominance after the auditory stimulus, regardless of subjective emotions. These results may help us understand frontal asymmetry associated with emotional mechanisms. In addition, these findings can be used directly in the brain-computer interface to improve emotion recognition performance for real-world practical applications.