Miracle-dz/UniFAR-FTUs huggingface dataset:
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation

This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our core idea is to train a rendering-invariant state-prediction (RISP) network that transforms image differences into state differences independent of rendering configurations, e.g., lighting, shadows, or material reflectance. To train this predictor, we formulate a new loss on rendering variances using gradients from differentiable rendering. Moreover, we present an efficient, second-order method to compute the gradients of this loss, allowing it to be integrated seamlessly into modern deep learning frameworks. We evaluate our method in rigid-body and deformable-body simulation environments using four tasks: state estimation, system identification, imitation learning, and visuomotor control. We further demonstrate the efficacy of our approach on a real-world example: inferring the state and action sequences of a quadrotor from a video of its motion sequences. Compared with existing methods, our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations.
Investigation of Electrical Characteristics of Sputtered IGZO TFTs

A study on the gas ambient (varying percent 02 flow) and gap spacing (distance between the target and wafer) during the sputter deposition of indium-gallium-zinc-oxide (IGZO) for a TFT channel material was executed. The effect these parameters have on electrical characteristics was investigated to better understand the role 02 vacancies play on device perfor mance. This experiment was performed to enable investigations on IGZO TFTs without the use of external resources. Devices fabricated exhibited differences between treatment combinations, however results show a lower on-off current ratio, larger subthreshold swing (SS) and higher threshold voltage (Vth) than observed from devices fabricated with IGZO deposited through collaboration with Corning Incorporated.
Face Detection on Mobile: Five Implementations and Analysis

In many practical cases face detection on smartphones or other highly portable devices is a necessity. Applica-tions include mobile face access control systems, driver sta-tus tracking, emotion recognition, etc. Mobile devices have limited processing power and should have long-enough bat-tery life even with face detection application running. Thus, striking the right balance between algorithm quality and complexity is crucial. In this work we adapt 5 algorithms to mobile. These algorithms are based on handcrafted or neural-network-based features and include: Viola-Jones (Haar cascade), LBP, HOG, MTCNN, BlazeFace. We analyze inference time of these algorithms on different devices with different input image resolutions. We provide guidance, which algorithms are the best ﬁt for mobile face access control systems and potentially other mobile appli-cations. Interestingly, we note that cascaded algorithms perform faster on scenes without faces, while BlazeFace is slower on empty scenes. Exploiting this behavior might be useful in practice.
Unifying Language Learning Paradigms

Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a uniﬁed framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives – two concepts that are commonly conﬂated. Next, we present a generalized and uniﬁed perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. We furthermore introduce a notion of mode switching, wherein downstream ﬁne-tuning is associated with speciﬁc pre-training schemes. We conduct extensive ablative experiments to compare multiple pre-training objectives and ﬁnd that our method pushes the Pareto-frontier by outperforming T5 and/or GPT-like models across multiple diverse setups. Finally, by scaling our model up to 20B parameters, we achieve SOTA performance on 50 well-established supervised NLP tasks ranging from language generation (with automated and human evaluation), language understanding, text classiﬁcation, question answering, commonsense reasoning, long text reasoning, structured knowledge grounding and information retrieval. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization. We release Flax-based T5X model checkpoints for the 20B model at https:
Determinantal Representations and the Image of the Principal Minor Map

In this paper we explore determinantal representations of multiaffine polynomials and consequences for the image of various spaces of matrices under the principal minor map. We show that a real multiaffine polynomial has a definite Hermitian determinantal representation if and only if all of its so-called Rayleigh differences factor as Hermitian squares and use this characterization to conclude that the image of the space of Hermitian matrices under the principal minor map is cut out by the orbit of finitely many equations and inequalities under the action of $(\textrm{SL}_{2}(\mathbb{R}))^{n} \rtimes S_{n}$. We also study such representations over more general fields with quadratic extensions. Factorizations of Rayleigh differences prove an effective tool for capturing subtle behavior of the principal minor map. In contrast to the Hermitian case, we give examples to show for any field $\mathbb{F}$, there is no finite set of equations whose orbit under $(\textrm{SL}_{2}(\mathbb{F}))^{n} \rtimes S_{n}$ cuts out the image of $n\times n$ matrices over ${\mathbb{F}}$ under the principal minor map for every $n$.
Components and Cycles of Random Mappings

Each connected component of a mapping { 1 , 2 , . , n } → { 1 , 2 , . , n } contains a unique cycle. The largest such component can be studied probabilistically via either a delay diﬀerential equation or an inverse Laplace transform. The longest such cycle likewise admits two approaches: we ﬁnd an (apparently new) density formula for its length. Implications of a constraint – that exactly one component exists – are also examined. For instance, the mean length of the longest cycle is (0 . 7824 . ) √ n in general, but for the special case, it is (0 . 7978 . ) √ n , a diﬀerence of less than 2%.
Review on Panoramic Imaging and Its Applications in Scene Understanding

With the rapid development of high-speed communication and artificial intelligence technologies, human perception of real-world scenes is no longer limited to the use of small Field of View (FoV) and low-dimensional scene detection devices. Panoramic imaging emerges as the next generation of innovative intelligent instruments for environmental perception and measurement. However, while satisfying the need for large-FoV photographic imaging, panoramic imaging instruments are expected to have high resolution, no blind area, miniaturization, and multidimensional intelligent perception and can be combined with artificial intelligence methods toward the next generation of intelligent instruments, enabling deeper understanding and more holistic perception of 360° real-world surrounding environments. Fortunately, recent advances in freeform surfaces, thin-plate optics, and metasurfaces provide innovative approaches to address the human perception of the environment, offering promising ideas beyond conventional optical imaging. In this review, we begin by introducing the basic principles of panoramic imaging systems and then describe the architectures, features, and functions of various panoramic imaging systems. Afterward, we discuss, in detail, the broad application prospects and great design potential of freeform surfaces, thin-plate optics, and metasurfaces in panoramic imaging. We then provide a detailed analysis of how these techniques can help enhance the performance of panoramic imaging systems. We further offer a detailed analysis of applications of panoramic imaging in scene understanding for autonomous driving and robotics, spanning panoramic semantic image segmentation, panoramic depth estimation, panoramic visual localization, and so on. Finally, we cast a perspective on future potential and research directions for panoramic imaging instruments.
Extracting phase coupling functions between collectively oscillating networks directly from time-series data

Many real-world systems are often regarded as weakly coupled limit-cycle oscillators, in which each oscillator corresponds to a dynamical system with many degrees of freedom that have collective oscillations. One of the most practical methods for investigating the synchronization properties of such a rhythmic system is to statistically extract phase coupling functions between limit-cycle oscillators directly from observed time-series data. In Particular, using a method that combines phase reduction theory and Bayesian inference, the phase coupling functions can be extracted from the time-series data of even just one variable in each oscillatory dynamical system with many degrees of freedom. However, it remains unclear how the choice of the observed variables aﬀects the statistical inference for the phase coupling functions. In this study, we examine the inﬂuence of observed variable types on the extraction of phase coupling functions using some typical dynamical elements under various conditions. We demonstrate that our method can consistently extract the macroscopic phase coupling functions between two phases representing collective oscillations in a fully locked state, regardless of the observed variable types; for example, even using one variable of any element in one system and the mean-ﬁeld value over all the elements in another system. We also study the case of globally coupled phase oscillators in a partially locked state. Our results reveal directional asymmetry in the robustness of extracting the macroscopic phase coupling function between two networks. For instance, when an asynchronous oscillator in network A and the macroscopic collective oscillation of network B is observed, the macroscopic phase coupling function from network A to network B can be extracted more robustly than in the opposite direction.
Axiomatizing the existential theory of
𝔽q((t))

We study the existential theory of equicharacteristic henselian valued fields with a distinguished uniformizer. In particular, assuming a weak consequence of resolution of singularities, we obtain an axiomatization of - and therefore an algorithm to decide - the existential theory relative to the existential theory of the residue field. This is both more general and works under weaker resolution hypotheses than the algorithm of Denef and Schoutens, which we also discuss in detail. In fact, the consequence of resolution of singularities our results are conditional on is the weakest under which they hold true.
Skema Pengendali Motor BLDC Tanpa Sensor Posisi Rotor dengan Metode Deteksi Back EMF Berbasis Mikrokontroler Arduino

Brushless DC Motor (BLDC) is one of synchronous motors which are increasingly applied in many applications due to effective, free-maintenance and high power to weight ratio. However, the operation of a BLDC motor requires rotor position sensor. This research has developed arduino microcontroller-based BLDC motor control scheme without rotor position sensor by using back EMF method. The back EMF signal in motor terminal is compared to the virtual ground to detect the rotor position. The proposed control scheme was verified in simulation using Proteus software and in experimental hardware on a lab scale. The research shown that the proposed scheme has been successfully developed based on arduino microcontroller and work properly. Keyword s : BLDC Motor Controller, BLDC Sensorless, Back EMF, Arduino Nano
QUANTUM DOT CELLULAR AUTOMATA BINARY COMPARATOR USING REVERSIBLE GATES

: - Quantum dot cellular automata (QCA) is one of the few alternative computing platform, has the potential to be a promising technology due to high speed, small size, and low power consumption in comparison with CMOS technology. The proposed comparator is designed and simulated using QCA designer tool. Two different comparator are designed to exhibit higher performance and to reduced the overall area. The binary comparator is designed to reduced the area, majority cell and power. Here reversible gates [Feynman gate] are used to reduced the energy losses and information losses.
Low power range estimation with DSSS technique in underwater acoustics

LOW POWER RANGE ESTIMATION WITH DSSS TECHNIQUE IN UNDERWATER ACOUSTICS Mustafa Ozan Güleryüz M.S. in Electrical and Electronics Engineering Supervisor: Prof. Dr. Hayrettin Köymen August, 2013 Performance of direct sequence spread spectrum modulation (DSSS) in underwater acoustical range finding is investigated in this thesis. Range estimation using low power DSSS codes is both analyzed theoretically and implemented at 400 kHz for experimental assessment, where the chip duration is 20 μs and sequence type is maximal. The effect of sequence length on the required transmit power is measured for sequence lengths from 7 chips to 127 chips. The performance of this method is compared to the widely used tone burst pulse range estimation technique. It is found that at a sequence of 127 chips and a pulse sequence length of 2,54 ms, range is estimated with 1,5 cm resolution using source level of 132,8 dB re 1 μParms @ 1 m source level , while it is 190,5 cm for the same length and magnitude tone burst modulation, at a reference test range of 4.5 m. Moreover, spectral height of received DSSS signal is well below the ambient noise level so that signal to noise ratio (SNR) for received DSSS signal is -14,8 dB, while it is 12,2 dB for received tone burst pulse
Cogging force and frequency bandwidth of a vibration energy harvester with nonlinear electromechanical resonance

The paper analyses the open-circuit frequency characteristics of a mechanical vibration energy harvester comprising of a single-coil cored armature and two vibrating yokes with permanent magnets connected to a plate spring. The cogging force, developed by action of the magnetic flux on the core brings a nonlinear component into system kinematics and causes complex variation of the frequency characteristics. It is shown that depending on dimensions of the generator parts, the system is capable of developing the frequency characteristics whose resonant frequency decreases or increases with the magnitude of external force. Through analysis supported by computer simulations using the finite element models it is shown that the systems with decreasing resonant frequency are more practical due to wider frequency bandwidth and higher amplitude of the generated voltage. Streszczenie. W artykule analizowane są właściwości układu przetwarzania drgań mechanicznych w energię elektryczną zbudowanego z generatora liniowego z pojedynczą cewką z rdzeniem ferromagnetycznym oraz dwóch ruchomych jarzm z magnesami trwałymi połączonymi z metalową sprężyną paskową. Pokazano, że układ o takiej budowie może wytwarzać dwa rodzaje charakterystyk częstotliwościowych, tj. takie w których częstotliwość rezonansowa maleje lub rośnie wraz z amplitudą zewnętrznej siły wymuszającej drgania. Za pomocą symulacji komputerowych z wykorzystaniem modeli siatkowych, pokazano, że w układach praktycznych powinno dążyć się do osiągnięcia pierwszego typu charakterystyk ze względu na szersze pasmo częstotliwości oraz większą amplitudę generowanego napięcia. Siła wewnętrzna i charakterystyki częstotliwościowe układu pozyskiwania energii z drgań mechanicznych z nieliniowym rezonansem elektromechanicznym
Estimation of image scale variations in monocular visual odometry systems

Reliable estimation of the vehicle position is the main prerequisite in all autonomous mobile robotic applications. Image‐scale uncertainty in correlation‐based monocular visual odometry systems negatively affects the accuracy of the vehicle motion estimation. This paper presents the development of a new technique and algorithm to estimate image‐scale variations due to camera height fluctuations when the vehicle is driven on uneven terrains or when the height of vehicle from ground changes as a result of changes in load or number of passengers in the vehicle. This technique depends on marking the image frames by two red laser points, as independent reference points, which have a certain distance between them. The image‐scale variations can be estimated by monitoring the variations in the distance between these two reference points. The proposed technique eliminates the need for camera recalibration and the use of sensors to measure the variations of camera height from ground, such as laser range finders and acceleration sensors. The developed system uses a single downward‐facing monocular camera supported by a lighting module and installed underneath the test vehicle to avoid the negative effect of directional sunlight and shadows, which can disturb the correlation. Indoor and outdoor experiments have proven the efficiency of the suggested technique in resolving image‐scale uncertainty and ensuring an image‐scale‐invariant correlation‐based matching, with only less than 5% additional computational time. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Current research and perspectives on microalgae-derived biodiesel

ABSTRACT Biofuels have gained much attention in the recent years. Microalgae hold tremendous potential for production of biofuels as a suitable alternative to conventional fossil fuels. However, in order to ensure sustainability of the biofuel production from microalgae, many issues needs to be considered. Large-scale production of such biofuels is often limited by high cost of production, various environmental concerns including water footprint, and low yield. The algal biorefinery strategy, which can integrate several different conversion technologies to produce biofuels, holds potential for reducing the overall production cost. This paper is a review on various aspects associated with algal cultivation, lipid extraction and biodiesel production. Different factors reported to affect the process of algal cultivation and advancements in transesterification process and product recovery are summarized here. A brief on biorefinery approach is also discussed.
Early detection of fraudulent COVID-19 products from Twitter chatter

Social media have served as lucrative platforms for misinformation and for promoting fraudulent products for the treatment, testing and prevention of COVID-19. This has resulted in the issuance of many warning letters by the United States Food and Drug Administration (FDA). While social media continue to serve as the primary platform for the promotion of such fraudulent products, they also present the opportunity to identify these products early by employing effective social media mining methods. In this study, we employ natural language processing and time series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We utilized an anomaly detection method on streaming COVID-19-related Twitter data to detect potentially anomalous increases in mentions of fraudulent products. Our unsupervised approach detected 34/44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6/44 (13.6%) within a week following the corresponding FDA letters. Our proposed method is simple, effective and easy to deploy, and do not require high performance computing machinery unlike deep neural network-based methods.
Capsid Inhibition with Lenacapavir in Multidrug-Resistant HIV-1 Infection.

BACKGROUND Patients with multidrug-resistant human immunodeficiency virus type 1 (HIV-1) infection have limited treatment options. Lenacapavir is a first-in-class capsid inhibitor that showed substantial antiviral activity in a phase 1b study. METHODS In this phase 3 trial, we enrolled patients with multidrug-resistant HIV-1 infection in two cohorts, according to the change in the plasma HIV-1 RNA level between the screening and cohort-selection visits. In cohort 1, patients were first randomly assigned in a 2:1 ratio to receive oral lenacapavir or placebo in addition to their failing therapy for 14 days; during the maintenance period, starting on day 15, patients in the lenacapavir group received subcutaneous lenacapavir once every 6 months, and those in the placebo group received oral lenacapavir, followed by subcutaneous lenacapavir; both groups also received optimized background therapy. In cohort 2, all the patients received open-label oral lenacapavir with optimized background therapy on days 1 through 14; subcutaneous lenacapavir was then administered once every 6 months starting on day 15. The primary end point was the percentage of patients in cohort 1 who had a decrease of at least 0.5 log10 copies per milliliter in the viral load by day 15; a key secondary end point was a viral load of less than 50 copies per milliliter at week 26. RESULTS A total of 72 patients were enrolled, with 36 in each cohort. In cohort 1, a decrease of at least 0.5 log10 copies per milliliter in the viral load by day 15 was observed in 21 of 24 patients (88%) in the lenacapavir group and in 2 of 12 patients (17%) in the placebo group (absolute difference, 71 percentage points; 95% confidence interval, 35 to 90). At week 26, a viral load of less than 50 copies per milliliter was reported in 81% of the patients in cohort 1 and in 83% in cohort 2, with a least-squares mean increase in the CD4+ count of 75 and 104 cells per cubic millimeter, respectively. No serious adverse events related to lenacapavir were identified. In both cohorts, lenacapavir-related capsid substitutions that were associated with decreased susceptibility developed in 8 patients during the maintenance period (6 with M66I substitutions). CONCLUSIONS In patients with multidrug-resistant HIV-1 infection, those who received lenacapavir had a greater reduction from baseline in viral load than those who received placebo. (Funded by Gilead Sciences; CAPELLA ClinicalTrials.gov number, NCT04150068.).