Miracle-dz/UniFAR-FTUs huggingface dataset:
Multihop Transmission for Time Correlation in Energy Harvesting Networks

We consider a system of IoT nodes powered completely by energy harvesting. This work focuses on achieving the time correlation of data measurements in a network of energy harvesting sensor nodes. Time correlation is achieved by hopping a message through the whole network. This message wakes up all the nodes and lets them perform a measurement. Measurement data is added to the transmitted message and is collected by a gateway at the end. The nodes harvest energy from a Radio Frequency (RF) source and store it in a capacitor. When the capacitor has suﬃcient energy, the nodes can turn on their system. A low power Wake-Up Receiver (WURx) is turned on and the nodes fall asleep while waiting for the incoming request message. Communication is done using active transmissions and the ultra-low-power WURx for data reception. Nodes consume a continuous power of less than 2 µ W in sleep mode while the WURx is turned on. The receiving sensitivity is -40 dBm, which limits the communication range. The request message hops through the network to overcome distance limitation. Collisions are avoided with Clear Channel Assessment (CCA) using the WURx. The hidden node problem is overcome by toggling an operational ampliﬁer during CCA. Distance limitation is overcome by a novel network layer algorithm. The network layer algorithm ﬁnds a directed acyclic graph (DAG) based on all nodes, starting in a single special source node and ending in a gateway. Data from all the nodes are gathered in a round, where each node can transmit one message around. The timing interval between the data collection is chosen to be bigger than the required energy divided by the minimal harvested power. In this way, all nodes will have suﬃcient energy in every time interval. The found DAG represents all important links where the nodes should wait for before measuring and transmitting. Other data from previous nodes are added to the transmission of the nodes. In this way, the gateway will receive data from all nodes with the minimal time diﬀerence between their measurements. Simulations show that a correct gateway oriented DAG solution is always found for random networks. In > 92 % of the cases, all nodes are taken into account in this solution and in 6% of the cases, just one node is missing. Nodes have been designed and evaluated. We can power the nodes with a minimal RF input of
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN

The power and the potential of deep learning models attract many researchers to design advanced and sophisticated architectures. Nevertheless, the progress is sometimes unreal due to various possible reasons. In this work, through an astonishing example we argue that more efforts should be paid to ensure the progress in developing a new deep learning method. For a highly influential multi-label text classification method XML-CNN, we show that the superior performance claimed in the original paper was mainly due to some unbelievable coincidences. We re-examine XML-CNN and make a re-implementation which reveals some contradictory findings to the claims in the original paper. Our study suggests suitable baselines for multi-label text classification tasks and confirms that the progress on a new architecture cannot be confidently justified without a cautious investigation.
Towards Broad Generalization in Robotic Manipulation: Data, Algorithms, and Supervision

From general object grasping [13] to in-hand manipulation [20], learning has enabled a number of exciting robotic manipulation capabilities in recent years. Despite this, the quintessential home robot that can enter a previously unseen home environment and complete a wide range of tasks like humans can is far from a reality. While there are many problems to solve in accomplishing this goal, one of the central bottlenecks lies in learning control policies that can generalize to new tasks, objects, and environments. For example, a robot cooking in a home cannot afford to re-learn from scratch for each new dish, nor is it feasible to hard-code state features for every new kitchen a robot might encounter. One potential route to accomplishing this generalization is to train the robot on a wide distribution of data that contains many tasks, objects, and environments. Indeed, this recipe of large, diverse datasets combined with scalable offline learning algorithms (e.g. self-supervised or cheaply supervised learning) has been the key behind recent successes in NLP [7, 2] and vision [6, 21]. However, directly extending this recipe to robotics is nontrivial, as we neither have sufficiently large and diverse datasets of robot interaction, nor is it obvious what types of learning algorithms or sources of supervision can enable us to scalably learn useful skills from these datasets. The goal of my research lies in tackling these challenges, and replicating the recipe of large-scale data and learning in the context of robotic manipulation. Towards this goal my research has focused on three key questions. First, how do we scalably collect large and diverse datasets of robots interacting in the physical world? Second, how do we design self-supervised reinforcement learning algorithms that can consume such broad data, which may come from non-experts and lack reward labels, and from it learn to reach unseen goals. Third, how might we unlock the broad sources of data that exist on the web, like videos of humans and natural language to enable more effective learning in our robots? Scaling Robotic Manipulation Datasets. The first challenge lies in acquiring large amounts of useful robotic interaction data, a particularly difficult problem in robotic manipulation. One approach to tackling this is having humans explicitly collect meaningful interaction via teleoperation [14, 15, 12], however this can be difficult to do at scale due to the immense burden it places on the human operator. As an alternative to human collected data, in our first paper on the topic we collected a large, 15 million frame dataset of robot interaction through autonomous data collection [5]. This dataset spanned multiple universities and robots, and < 3 min human supervision Autonomous exploration with BEE < 3 min human supervision Autonomous exploration with BEE < 3 min human supervision Autonomous exploration w th BEE
PECULIARITIES OF PHOTOLUMINESCENCE EXCITATION IN ZnO CERAMICS DOPED WITH GROUP-I ELEMENTS

Extrinsic luminescence, excitation, and absorption spectra of ZnO ceramics doped with acceptors (lithium, copper, or silver), as well as undoped ZnO ceramics sintered in various atmospheres, have been studied. It is shown that the acceptor doping leads to the appearance of luminescence bands in the visible spectral interval, and their intensity significantly exceeds the intensity of the corresponding emission from undoped specimens. A selective maximum at 390–400 nm, which is usually absent in the excitation spectra of self-activated luminescence bands in undoped ZnO specimens, is found to dominate in the excitation spectra of those bands. It is supposed to be caused by the interaction between the emitting centers and defects arising near the impurities, with the Auger process being the most probable mechanism of energy transfer from these defects to the emitting centers. By sintering ZnO ceramics in the presence of carbon, it is shown that the appearance of the selective maximum in the excitation spectra occurs due to the extraction of oxygen from ZnO ceramics. An assumption has been done concerning the nature of the centers responsible for the excitation of extrinsic luminescence.
Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design

A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid transactional and analytical processing (HTAP) database systems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant losses in transactional (up to 74.6%) and/or analytical (up to 49.8%) throughput compared to performing only transactional or only analytical queries in isolation, due to (1) data movement be-tween the CPU and memory, (2) data update propagation from transactional to analytical workloads, and (3) the cost to main-tain a consistent view of data across the system. We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases that avoids the large throughput losses of traditional HTAP systems. Polynesia (1) di-vides the HTAP system into transactional and analytical pro-cessing islands, (2) implements new custom hardware that un-locks software optimizations to reduce the costs of update prop-agation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement overheads. Our evaluation shows that Polynesia outperforms three state-of-the-art HTAP systems, with average transactional/analytical throughput improvements of 1.7×/3.7×, and reduces energy consumption by 48% over the prior lowest-energy HTAP sys-tem.
A Modified ResNeXt for Android Malware Identification and Classification

It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. Identifying and categorizing dangerous applications into categories that are similar to one another are especially important in the development of a safe Android app ecosystem. The categorization of malware families may be used to improve the efficiency of the malware detection process as well as to systematically identify malicious trends. In this study, we proposed a modified ResNeXt model by embedding a new regularization technique to improve the classification task. In addition, we present a comprehensive evaluation of the Android malware classification and detection using our modified ResNeXt. The nonintuitive malware's features are converted into fingerprint images in order to extract the rich information from the input data. In addition, we applied fine-tuned deep learning (DL) based on the convolutional neural network (CNN) on the visualized malware samples to automatically obtain the discriminatory features that separate normal from malicious data. Using DL techniques not only avoids the domain expert costs but also eliminates the frequent need for the feature engineering methods. Furthermore, we evaluated the effectiveness of the modified ResNeXt model in the classification process by testing a total of fifteen different combinations of the Android malware image sections on the Drebin dataset. In this study, we only use grayscale malware images from a modified ResNeXt to analyze the malware samples. The experimental results show that the modified ResNeXt successfully achieved an accuracy of 98.25% using Android certificates only. Furthermore, we undertook extensive trials on the dataset in order to confirm the efficacy of our methodology, and we compared our approach with several existing methods. Finally, this article reveals the evaluation of different models and a much more precise option for malware identification.
δ 13 C, CO 2 / 3 He and 3 He / 4 He ratios reveal the presence of mantle gas in the CO 2 -rich groundwaters of the Ardennes massif (Spa, Belgium)

Although natural CO 2 -rich groundwaters of eastern Belgium have been known for centuries, the exact origin of their gas is still unclear. This paper presents the results of a sampling campaign in Belgium (Spa, Stoumont, Malmedy): 30 samples of both carbogaseous and non-carbogaseous groundwaters were analyzed for major elements, CO 2 content and carbon isotopic composition. Among them, 13 samples were also analyzed for 3 He / 4 He and 4 He / 20 Ne ratios. The combination of δ 13 C (between ca. − 9‰ VPDB 1 and − 2‰ VPDB), CO 2 / 3 He ratio (between 1 . 9 × 10 8 and 2 . 9 × 10 9 ) and 3 He / 4 He (between 0.92 and 2.70 Ra) shows with a high level
The classification of the finger frames method in violin playing

From the first stage of learning new repertoire, string pedagogy aims to present violinists with effective strategies to achieve accurate intonation in performance. The search for new teaching and learning strategies, whilst running the risk of being seen as unconventional compared to more tried and tested methods (i.e. Suzuki), can on the other hand provide the tutor with new tools for enhancing their teaching practice. Western Australian violinist Fleur Challen has developed a method titled Finger Frames; a learning strategy that uses a colour coding system to prepare a violinist for changes in left hand position, reducing the cognitive load during the process of sight-reading new repertoire. In the field of psychology, research has been undertaken on artificial synaesthesia to indicate that when two particular senses are linked together, it eliminates a stage of processing between them, therefore reducing cognitive load. This research paper aims to document Challen’s Finger Frames method, and to argue how related research on artificial synaesthesia and Cognitive Load Theory can help to explain why such a system may work. This research involves a qualitative discussion of the Finger Frames method with respect to string pedagogy, motor learning and muscle memory, and psychology, and includes an interview with Challen.
Learning in the open: considering MOOCs, openness and social media for learning

The emergence of Massive Open Online Courses (MOOCs) has captured the attention of higher education academics and managers. Ongoing debates about MOOCs, however, demonstrate the need to critically evaluate the phenomenon. One of the key ideas associated with MOOCs is openness and while MOOCs are emergent and of the moment, ideas about openness and its relationship to learning at university have a history that can be evaluated through the lens of learner engagement. The concurrent emergence of social media provides a useful way of understanding openness, innovative learning environments and potentially transformative pedagogies. In conclusion it is suggested that effective emergent open learning environments are not massive, nor necessarily online or coursebased. Instead it is more useful to think of them as being learner-centred, social spaces for open learning.
Analysis of Six-Legged Walking Robots

In the present paper, an attempt has been made to carryout kinematic and dynamic examination of a six-legged robot. A three-revolute (3R) kinematic chain has been chosen for each leg instrument with a particular ultimate objective to imitate the leg structure of a bug. Denavit–Hartenberg (D-H) conventions are used to perform kinematic examination of the six-legged robot. The prompt and invert kinematic examination for each leg has been considered remembering the ultimate objective to develop a general kinematic model of a six-legged robot, when it takes after a straight way. The issues related to heading time of legs have been settled for both the swing and support times of the robot. Specify that heading period issue in the midst of the support organize has been nitty gritty as a change issue and comprehended using the base squared method. Lagrange-Euler arrange has been utilized to choose the joint torques. The made kinematic and element models have been examined for tripod step time of the six-legged robot
POTENTIAL USE OF BENGKULU COASTAL SAND AS CAPILLARY-GRAVITATIONAL (GRAPILAR) SLOW SAND FILTER MEDIA

Two-mini standart SSF were joined length side by length side and equiped with inflow container for one side and outflow container for the other side to be operated as media for capillary force, for the inlet side of sand media, and media for gravitational force, for the outlet side of sand media. Filtration tests were conducted by using clear water by varying the pressure head on the system with no head media with head media for the purpose to identify the performance of Bengkulu coastal sand as capillary-gravitational SSF media. Result of test showed that with no head media, there was no water flow through the system, whenever pressure head applied lower than the pressure head level. With head media, on the other hand; the flow would take place eventhough the pressure head much less then the pressure head level. Characteristic of capillary flow was also identified.
Feasibility study of the non-invasive estimation of the b+ arterial input function for human PET imaging

This work deals with the estimation of the concentration of molecules in arterial blood which are labelled with positron-emitting radioelements. This concentration is called “ B+ arterial input function”. This concentration has to be estimated for a large number of pharmacokinetic analyses. Nowadays it is measured through series of arterial sampling, which is an accurate method but requiring a stringent protocol. Complications might occur during arterial blood sampling because this method is invasive (hematomes, nosocomial infections). The objective of this work is to overcome this risk through a non-invasive estimation of B+ input function with an external detector and a collimator. This allows the reconstruction of blood vessels and thus the discrimination of arterial signal from signals in other tissues.Collimators in medical imaging are not adapted to estimate B+ input function because their sensitivity is very low. During this work, they are replaced by coded-aperture collimators, originally developed for astronomy.New methods where coded apertures are used with statistical reconstruction algorithms are presented. Techniques for analytical ray-tracing and for the acceleration of reconstructions are proposed. A new method which decomposes reconstructions on temporal sets and on spatial sets is also developped to efficiently estimate arterial input function from series of temporal acquisitions.This work demonstrates that the trade-off between sensitivity and spatial resolution in PET can be improved thanks to coded aperture collimators and statistical reconstruction algorithm; it also provides new tools to implement such improvements.
Pertumbuhan Ikan Nila (Oreochromis niloticus) Yang Diberi Pakan Mengandung Ikan Asin Bawah Standar (IABS) Dengan Kesegaran Berbeda

Penelitian ini bertujuan untuk mengetahui pengaruh tingkat kesegaran IABS dalam formulasi pakan terhadap pertumbuhan ikan nila dibandingkan dengan formulasi pakan standar (pakan berbasis tepung ikan). Rancangan percobaan yang digunakan adalah Rancangan Acak Lengkap (RAL) dengan 3 perlakuan dan 3 ulangan. Ikan nila yang digunakan memiliki berat rata-rata 5,23±0,02 gram dengan padat tebar 20 ekor per akuarium. Perlakuan pakan yang diberikan yakni pakan berbasis tepung ikan tanpa IABS, pakan mengandung IABS segar dan pakan mengandung IABS kurang segar. Parameter yang diukur adalah laju pertumbuhan spesifik (LPS), jumlah konsumsi pakan (JKP), efisiensi pakan (EP), tingkat kelangsungan hidup (SR) dan parameter kualitas air. Hasil penelitian menunjukkan bahwa ada perbedaan nyata (P 0,05) untuk parameter JKP, SR dan kualitas air. Tingkat kesegaran bahan ikan asin sangat berpengaruh terhadap pertumbuhan dan efisiensi pakan untuk ikan nila, tetapi tidak untuk kelangsungan hidup ikan nila ( Oreochromis niloticus ). Pemberian IABS segar menghasilkan pertumbuhan dan efisiensi pakan yang sama baiknya dengan pakan standar dan lebih baik daripada pemberian IABS kurang segar. Kata kunci : IABS, protein, ika nila, pertumbuhan, tepung ikan.
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (feed-forward) which is trained to directly predict raw vision outputs, guided by a learned discrete code and (II) a language model (autoregressive) that is trained to generate the guiding code. These components complement each other: the language model is well-suited to modeling structured interdependent data, while the base model is efficient at dealing with high-dimensional outputs. We demonstrate the effectiveness of UViM on three diverse and challenging vision tasks: panoptic segmentation, depth prediction and image colorization, where we achieve competitive and near state-of-the-art results. Our experimental results suggest that UViM is a promising candidate for a unified modeling approach in computer vision.
Is Thermohaline Mixing the Full Story? Evidence for Separate Mixing Events near the Red Giant Branch Bump

The abundances of mixing-sensitive elements including lithium, [C/N], and 12C/13C are known to change near the red giant branch bump. The explanation most often offered for these alterations is double diffusive thermohaline mixing in the stellar interior. In this analysis, we investigate the ability of thermohaline mixing to explain the observed timing of these chemical depletion events. Recent observational measurements of lithium and [C/N] show that the abundance of lithium decreases before the abundance of [C/N], whereas numerical simulations of the propagation of the thermohaline-mixing region computed with MESA show that the synthetic abundances drop simultaneously. We therefore conclude that thermohaline mixing alone cannot explain the distinct events of lithium depletion and [C/N] depletion, as the simultaneity predicted by simulations is not consistent with the observation of separate drops. We thus invite more sophisticated theoretical explanations for the observed temporal separation of these chemical depletion episodes as well as more extensive observational explorations across a range of masses and metallicities.
Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI

Entity Resolution ( ER ) in voice assistants is a prime component during run time that resolves entities in user’s request to real world entities. ER involves two major functionalities 1 . Relevance generation and 2 . Ranking . In this paper we propose a low cost relevance generation framework by generating features using customer implicit and explicit feedback signals. The generated relevance datasets can serve as test sets to measure ER performance. We also introduce a set of metrics that accurately measures the performance of ER systems in various dimensions. They provide great interpretability to deep dive and identifying root cause of ER issues, whether the problem is in relevance generation or ranking.