Miracle-dz/UniFAR-FTUs huggingface dataset:
A new control strategy for DLR's multisensory articulated hand

Concerns a multisensory 12-DOF 4-finger hand in which all actuators are integrated in the palm or the fingers. It was necessary to use a specially designed miniaturized linear actuator. For telemanipulation our first approach was to make the hand follow the desired states commanded from higher levels, e.g., dataglove. This requires accurate tracking, compliance in the constrained environment, and smooth transition between these two modes. The tracking control problem is to design a control scheme which generates the appropriate control signal so as to ensure that the joint angle follows any specified reference trajectory as closely as possible. Compliance is needed for fine manipulation and also for the protection of the hand. Transition control is essentially the problem of making the energy conversion more effective and smooth. We propose a parallel torque/position control strategy for this task. The robust trajectory tracking in free space is implemented by sliding mode control; the compliant motion in the constrained environment is realized by using impedance control; and a parallel observer based on contact torques and system states has been built to determine the switch between these two kind of controllers for the transition control from or to contact motion.
Effects of growth hormone co-treatment on in vitro fertilization outcomes in women with expected normal ovarian response

Objective: This study aimed to evaluate the effects of adjuvant growth hormone (GH) therapy on in vitro fertilization outcomes in women with infertility with expected normal ovarian response who underwent gonadotropin-releasing hormone (GnRH) antagonist protocol with dual triggering. Materials and Methods: Records of women who underwent GnRH antagonist cycles with dual triggering in a single tertiary center between 2017 and 2020 were retrospectively analyzed. A total of 1054 women with expected normal ovarian response were evaluated, of which 131 were found to receive GH co-treatment (study group). Moreover, 950 women did not receive any adjuvant therapy (control group). Their cycle outcomes were compared. Results: The number of retrieved oocytes, oocyte maturation rates, quality of embryos, miscarriage rates, and multiple pregnancy rates were comparable among women who underwent GnRH antagonist cycles with and without GH co-treatment. The number of obtained 2PN embryos (5.68±2.46 vs 5.06±2.5; p=0.003), fertilization rates (0.84±0.16 vs 0.76±0.18; p<0.001), implantation rates (0.34 vs 0.25; p=0.006), clinical pregnancy rates (50.4% vs 38%; p=0.008), and live birth delivery rates (41.8% vs 32.2%; p=0.007) were significantly higher in women who received GH co-treatment. Conclusion: GH co-treatment significantly increased the clinical pregnancy rates and live birth delivery rates in women with infertility and expected normal ovarian response who underwent GnRH antagonist protocol with dual triggering for oocyte maturation, which was possibly due to the increasing endometrial receptivity or improving oocyte quality.
Facial Recognition Using Multispectral Images

- Facial recognition is a method of identifying or authenticating the identity of people through their faces. Nowadays, facial recognition systems that use multispectral images achieve better results compared to those that use only visible spectral band images. In this work, a skin detector is proposed to be applied in a forgery detection module and an architecture that uses multiple deep convolutional neural networks and multispectral images to perform facial recognition. A study is carried out with the objective of evaluating the performance of the adaptation of several layers of the neural network base. Additionally, a second study was conducted to evaluate the performance of Support Vector Machines (SVM) and k-Nearest Neighbour classifiers to classify the embeddings obtained through the proposed architecture. The experimental results in the Tufts and CASIA NIR-VIS 2.0 multispectral databases show a competitive performance in facial recognition obtaining a rank-1 score of 99.7% and 99.8% respectively.
CHEX-MIX: Combining Homomorphic Encryption with Trusted Execution Environments for Two-party Oblivious Inference in the Cloud

Data, when coupled with state-of-the-art machine learning models, can enable remarkable applications. But, there exists an underlying tension: users wish to keep their data private, and model providers wish to protect their intellectual property. Homomorphic encryption (HE) and multi-party computation (MPC) techniques have been proposed as solutions to this problem; however, both techniques require model providers to fully trust the server performing the machine learning computation. This limits the scale of inference applications since it prevents model providers from leveraging shared public cloud infrastructures. In this work, we present C HEX -M IX , a solution to the problem of privacy-preserving machine learning between two mutually distrustful parties in an untrusted cloud setting. C HEX -M IX relies on a combination of HE and trusted execution environments (TEEs) and leverages the beneﬁts of each to counter the drawbacks of the other. In particular, we use HE to provide clients with conﬁdentiality guarantees and TEEs to provide model providers with conﬁdentiality guarantees and protect the integrity of computation from malicious cloud adversaries. Unlike prior solutions to this problem, such as multi-key HE, single-key HE, MPC, or TEE-only techniques, our solution assumes that both clients and the cloud can be malicious, makes no collusion assumptions, and frees model providers from needing to maintain private online infrastructures. In this paper, we analyze our solution from a security perspective and detail the advantages that our solution provides over prior works, including its ability to allow model providers to maintain privacy of their software IP. We demonstrate the feasibility of our solution by deploying C HEX -M IX in an Azure conﬁdential computing machine. Our results show that C HEX -M IX can execute at high efﬁciency, with low communication cost, while providing security guarantees unaddressed by prior work.
Commitment types and influence of daily deals among boot camp participants

COMMITMENT TYPES AND INFLUENCE OF DAILY DEALS AMONG BOOT CAMP PARTICIPANTS by Linda K. Lund The sedentary lifestyle that many Americans exhibit reinforces the need for continued research into the factors associated with the adoption and maintenance of regular physical activity. Internet-based daily deal websites, such as Groupon® and LivingSocial®, feature deals and discounts on many products and services, including fitness programs. The purpose of this study was to determine the relationship between “daily deals” usage and the types of commitment exhibited by fitness program participants. The targeted program studied was a fitness boot camp. Boot camp participants (N=150), recruited from an indoor boot camp company, completed a questionnaire that included basic demographic information, membership status, and the Exercise Commitment Scale (ECS). Contrary to the hypothesis, the results showed that all participants had a greater “want to” commitment than a “have to” commitment but that regular paying members reported more personal investment.
Stronger NAS with Weaker Predictors

We compare the effect of using different architecture encodings in in Table 2. We found when combined with CATE embedding [3], the performance of WeakNAS can be further improved, compared to WeakNAS baseline with adjacency matrix encoding used in [4]. This also leads to stronger performance than cate-DNGO-LS baseline in CATE [3], which demonstrates that CATE embedding [3] is an orthogonal contribution to WeakNAS, and they are mutually compatible.
Transformer in Transformer Supplemental

Attention between Patches. In Figure 1, we plot the attention maps from each patch to all the patches. We can see that for both DeiT-S and TNT-S, more patches are related as layer goes deeper. This is because the information between patches has been fully communicated with each other in deeper layers. As for the difference between DeiT and TNT, the attention of TNT can focus on the meaningful patches in Block-12, while DeiT still pays attention to the tree which is not related to the pandas.
OPTIMAL MONEY MANAGEMENT CONTRACTS

I characterize the dynamics of incentives in an optimal contract with investment delegation, moral hazard, and uncertainty about the agent’s productivity. The principal increases the agent’s incentives after good performance in order to delegate more capital to an agent with higher perceived productivity, thus implementing a convex pay-for-performance scheme. Moreover, the principal commits to reduce the agent’s future incentives in order to mitigate ex-ante investment distortions. Methodologically, I provide a duality-based strategy to overcome technical challenges common to continuous-time contracting models with state variables. I also derive a sufficient condition to verify the validity of the first-order approach.
C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network.We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930 x faster than MLlib, 63 x faster than Petuum, and 14 x faster than MXNet.
Incremental Partitioning for Efficient Spatial Data Analytics

Big spatial data has become ubiquitous, from mobile applications to satellite data. In most of these applications, data is continuously growing to huge volumes. Existing systems for big spatial data organize records at either the record-level or block-level. Systems that use record-level structures include key-value stores and LSM-Tree stores, which support insert and delete operations and they are optimized for highly-selective queries. On the other hand, systems like GeoSpark that use block-level structures (e.g. 128 MB each) are more efficient for analytical queries, but they cannot incrementally maintain the partitioned data and do not support delete operations. This paper proposes a general framework that enables block-level systems to incrementally maintain spatial partitions, in the presence of bulk insertions and deletions, in distributed file system (DFS) blocks. We first formally study the incremental spatial partitioning problem for big data and demonstrate its NP-hardness. Then, we propose a cost model to estimate the performance of queries on the partitioned data and the effect of modifying it as the data grows. After that, we provide three different implementations of the incremental partitioning framework. Comprehensive experiments on large real datasets show that our proposed partitioning algorithms outperforms state-of-the-art spatial partitioning methods.
Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA-CAPF Framework

+e remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. +en, the Autoregressive IntegratedMoving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finally, a condition-activated particle filter (CAPF) is proposed to predict the system state and the bearing’s RUL. +e proposed prediction framework is verified by the hoist bearing life cycle data from a port in Shanghai collected by the NetCMAS system. +e prediction results by the ARIMA-CAPF framework in comparison with three other prediction strategies identify the effectiveness.
Phase and Amplitude Calibration of Rotating Equispaced Circular Array for Geostationary Microwave Interferometric Radiometers— Theory and Methods

The calibration strategy for synthetic aperture interferometric radiometers based on noise injection has been successfully demonstrated in the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) instrument onboard the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA). With the emerging demands of increasing both array size and frequency bands, the internal calibration hardware becomes more difficult to implement. Although several external calibration methods have been proposed, the so-called phase ambiguity problem was not carefully considered. In this paper, a novel calibration strategy for both phase and amplitude calibration based on redundant space calibration is proposed, which utilizes the unique structure of equispaced circular array and rotational sampling strategy. The original irregular phase ambiguity degenerates to the fixed π-ambiguity, where only two possible solutions to phases are introduced. Both phase and amplitude of visibility can be simultaneously calibrated by the proposed strategy, and no cooperative calibration target is needed.
DOES THE CURRENT CONCESSIONING REGIME OF BORDER POSTS INFRASTRUCTURE YIELD OPTIMAL TRADE EFFICIENCY FOR AFRICA? LESSONS FOR COMESA

Africa intra-trade has been generally low overtime. High trade costs emanating from complex border posts crossing has been cited to be one of the major causes. In an endeavour to promote seamless flow of trade private sector financed border posts modernisation projects are being implemented. These modernisation projects covers physical infrastructure alone and the concessionaire recoups the investment through charging facilities user fees. The justification is that the additional costs (user charges) are offset by the resultant improvement in trade efficiency, the reduction in time to cross a border. Following the argument that border crossing infrastructure is not the only source of trade efficiency, this study sought to demonstrate the differential effects of border posts infrastructure and other soft reforms on trade efficiency with the intention to show that border infrastructure user charges that do not account for the net effect on trade efficiency border infrastructure investment tax other reforms areas outside the concession. The intention is to stimulate debate leading to revision of current concession design as COMESA countries get ready to implement the African Continental Free Trade Area (AfCFTA) Agreement. The Poisson Pseudo Maximum Likelihood (PPML) estimator was employed as an analytical tool. The overall finding of this paper is that border infrastructure alone do not yield an optimal trade efficient solution for Africa. Border infrastructure projects need to be implemented as a bundle of solutions including streamlining procedures with the aid of information communication technology. The finding that high levels of corruption facilitate hedging against border crossing time uncertainty, speaks to the theoretical arguments that firms pay bribes to be facilitated. In that vein, African firms are willing to pay border user fees when a bundle of solutions are implemented that reduce clearance time. The major policy implication that COMESA member states can draw from this study is that governments should seriously consider private sector as an important financial source to implement trade facilitation reforms at border posts and these include physical infrastructure, procedures and ICT. However, this funding can be unlocked only if the solutions implemented induce trade efficiency. This can be achieved by revising the current concessions structure limited to border infrastructure, extending them to cover procedures and ICT solutions and making trade efficient the pith of the concession agreement.
Dynamic Mechanical Characteristics of Impact Rock under the Combined Action of Different Constant Temperatures and Static and Dynamic Loads

*e complex mechanical environment of deep coal and rock masses leads to obvious changes on their dynamic mechanical properties. However, there are few reports on the dynamic mechanical properties of rocks under the combined action of medium temperature (normal temperature ∼100°C) and static and dynamic loads. In this paper, a dynamic load and temperature combined action Hopkinson pressure bar experimental system is used to experimentally study the impact type of a fine sandstone under temperature conditions of 18°C, 40°C, 60°C, 80°C, and 100°C, an axial static load of 3MPa, a gas chamber pressure of 0.06MPa, and a constant temperature time of 4 h. *e dynamic characteristics of the change law of the fine sandstone and the energy dissipation characteristics of the load process are analyzed, and the characteristic law of the fine sandstone surface response is analyzed using digital image correlation technology. Our results indicate the following. (1) Under conditions in which the other experimental conditions remain unchanged, the dynamic stress-strain of the fine sandstone presents a bimodal shape with a “rebound” phenomenon. Increasing temperature causes the peak strength of the fine sandstone to increase; however, the relative strength can increase or decrease. *e relative increase in the strength is 1.14MPa (°C) when the temperature increases from 40°C to 60°C, 0.15MPa (°C) when the temperature increases from 60°C to 80°C, and 0.62MPa (°C) when the temperature increases from 80°C to 100°C. (2) *e digital image correlation results show that, under the action of a dynamic load stress wave, the fine sandstone experiences a displacement vector change on the sample surface; furthermore, under the combined action of the temperature and dynamic and static loads, the fine sandstone experiences macroscopic shear failure. *e surface strain in the propagation direction of the stress wave is obviously higher and can even reach values of more than 10 times that of the strain in other directions. (3) From the perspective of energy dissipation, the incident energy, reflected energy, and dissipated energy of the fine sandstone under an impact load have the same change law. After being affected by a dynamic load, the energy rapidly increases to a certain value and then remains relatively stable. *e transmitted energy is relatively small and can be approximated as a horizontal line. As the temperature increases, the incident energy, reflected energy, and dissipated energy tend to first decrease and then increase, and most of the incident energy in the fine sandstone is dissipated in the form of reflected waves.
Enzymes and pathways in microbial production of 2,3-butanediol and 3-acetoin isomers

Abstract 2,3-Butanediol (BD) and acetoin (AC) are products of the non-oxidative metabolism of microorganisms, presenting industrial importance due to their wide range of applications and high market value. Their optical isomers have particular applications, justifying the efforts on the selective bioproduction. Each microorganism produces different isomer mixtures, as a consequence of having different butanediol dehydrogenase (BDH) enzymes. However, the whole scene of the isomer bioproduction, considering the several enzymes and conditions, has not been completely elucidated. Here we show the BDH classification as R, S or meso by bioinformatics analysis uncovering the details of the isomers production. The BDH was compared to diacetyl reductases (DAR) and the new enoyl reductases (ER). We observed that R-BDH is the most singular BDH, while meso and S-BDHs are similar and may be better distinguished through their stereo-selective triad. DAR and ER showed distinct stereo-triads from those described for BDHs, agreeing with kinetic data from the literature and our phylogenetic analysis. The ER family probably has meso-BDH like activity as already demonstrated for a single sequence from this group. These results are of great relevance, as they organize BD producing enzymes, to our known, never shown before in the literature. This review also brings attention to nontraditional enzymes/pathways that can be involved with BD/AC synthesis, as well as oxygen conditions that may lead to the differential production of their isomers. Together, this information can provide helpful orientation for future studies in the field of BD/AC biological production, thus contributing to achieve their production on an industrial scale.
Role of lncRNA LIPE-AS1 in adipogenesis

ABSTRACT Recent studies have identified long non-coding RNAs (lncRNAs) as potential regulators of adipogenesis. In this study, we have characterized a lncRNA, LIPE-AS1, that spans genes CEACAM1 to LIPE in man with conservation of genomic organization and tissue expression between mouse and man. Tissue-specific expression of isoforms of the murine lncRNA were found in liver and adipose tissue, one of which, designated mLas-V3, overlapped the Lipe gene encoding hormone-sensitive lipase in both mouse and man suggesting that it may have a functional role in adipose tissue. Knock down of expression of mLas-V3 using anti-sense oligos (ASOs) led to a significant decrease in the differentiation of the OP9 pre-adipocyte cell line through the down regulation of the major adipogenic transcription factors Pparg and Cebpa. Knock down of mLas-V3 induced apoptosis during the differentiation of OP9 cells as shown by expression of active caspase-3, a change in the localization of LIP/LAP isoforms of C/EBPβ, and expression of the cellular stress induced factors CHOP, p53, PUMA, and NOXA. We conclude that mLas-V3 may play a role in protecting against stress associated with adipogenesis, and its absence leads to apoptosis.
Bit error rate performance for optical fiber systems with modal noise

A gamma distribution is used to model the received optical power fluctuation in a digital fiber-optic system affected by modal noise. New recursive formulas for the moments of the receiver output current are obtained which can be used in the Gauss quadrature rule (GQR) approximation of the error probability integral. Error rates have been calculated for varying degrees of modal noise disturbance. >