Miracle-dz/UniFAR-FTUs huggingface dataset:
Jacobian hits circuits: hitting-sets, lower bounds for depth-D occur-k formulas & depth-3 transcendence degree-k circuits

We present a single common tool to strictly subsume all known cases of polynomial time blackbox polynomial identity testing (PIT), that have been hitherto solved using diverse tools and techniques, over fields of zero or large characteristic. In particular, we show that polynomial time hitting-set generators for identity testing of the two seemingly different and well studied models - depth-3 circuits with bounded top fanin, and constant-depth constant-read multilinear formulas - can be constructed using one common algebraic-geometry theme: Jacobian captures algebraic independence. By exploiting the Jacobian, we design the first efficient hitting-set generators for broad generalizations of the above-mentioned models, namely: - depth-3 (Ω Π Ω) circuits with constant transcendence degree of the polynomials computed by the product gates (no bounded top fanin restriction), and - constant-depth constant-occur formulas (no multilinear restriction). Constant-occur of a variable, as we define it, is a much more general concept than constant-read. Also, earlier work on the latter model assumed that the formula is multilinear. Thus, our work goes further beyond the related results obtained by Saxena & Seshadhri (STOC 2011), Saraf & Volkovich (STOC 2011), Anderson et al. (CCC 2011), Beecken et al. (ICALP 2011) and Grenet et al. (FSTTCS 2011), and brings them under one unifying technique. In addition, using the same Jacobian based approach, we prove exponential lower bounds for the immanant (which includes permanent and determinant) on the same depth-3 and depth-4 models for which we give efficient PIT algorithms. Our results reinforce the intimate connection between identity testing and lower bounds by exhibiting a concrete mathematical tool - the Jacobian - that is equally effective in solving both the problems on certain interesting and previously well-investigated (but not well understood) models of computation.
BREAKUP: a preprocessing algorithm for satisfiability testing of CNF formulas

An algorithm called BREAKUP, which processes CNF formulas by separating them into "connected components," is introduced. BREAKUP is then used to speed up the testing of some first-order formulas for satisfi- ability using Iwama's IS Algorithm. The complexity of this algorithm is shown to be on the order of O(nc-nv), where nc is the number of clauses and nv is the number of variables. 1 Introduction Connectedness is an attribute of graphs that can be useful in deciding the satisfiability of propositional formulas in conjunctive normal form (CNF). Preprocessing to separate a formula into "connected components" is generally fast and easy, and can significantly speed up testing of the formula for satisfiability. BREAKUP, the algorithm introduced here, will be used in con- junction with Iwama's IS Algorithm (4),(5) and will be shown to significantly speed up (as compared to using IS alone) the processing of some formulas from Gilmore (3) and Davis and Putnam (2). A CNF formula is a conjunction of clauses where each clause is a disjunc- tion of literals. A literal is a propositional variable or the negation of a propo- sitional variable. Given a pair of literals {p9-*p}9 each will be called the mate of the other (cf. Davis (1)). Clearly, we can assume that no clause contains both a literal and its mate. There are two ways to think of clauses in a formula as con- nected. First, two clauses c,d, are connected if there exists a chain of clauses starting with c and ending with d such that for any two adjacent clauses x,y, either y contains a literal that occurs in x or y contains the mate of a literal that occurs in x. Alternatively, c and d can be said to be connected if there is a chain of clauses starting with c and ending with d such that if x and y are adjacent then y contains the mate of a literal that occurs in x. In either case, a set of clauses is said to be connected if every pair in the set is connected and a component is a maximally connected set of the clauses of the formula.
A Robotic System with Force Feedback for Micro-Surgery

A robotic system with force feedback for micro-surgery is developed (named “Microhand”). The system designed is based on a master-slave operation mode. The slave manipulators are designed by using macro-micro frames. The PHANToM Desktop developed by the SensaAble Technology Company is used as the master device. The interactive force/torque information of the slave manipulators and the surgical environments measured by the six-dimension force/torque sensors are fed back to the master device, so that the system is capable of providing force feedback to the surgeon during operations. The validity of the system has been proved through animal experiments of vas suturing a rabbit’s 3mm in diameter neck artery and its 1mm in diameter leg artery.
2009 2nd International Conference on Power Electronics and Intelligent Transportation System Knn-fcm Hybrid Algorithm for Indoor Location in Wlan

As a fingerprint match method, k-nearest neighbors (KNN) has been widely applied for indoor location in Wireless Local Area Networks (WLAN), but its performance is sensitive to number of neighbors k and positions of reference points (RPs). So fuzzy c-means (FCM) clustering algorithm is applied to improve KNN, which is the KNN-FCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of FCM based on received signal strength (RSS). Then, the k RPs are classified into different clusters through FCM based on RSS and the position coordinates. According to the rules proposed in this paper, some RPs are reselected for indoor location in order to improve the location precision. Simulation results indicate that the proposed KNN-FCM hybrid algorithm generally outperforms KNN when the location error is less than 2m. phase, many fingerprint match methods are applied to estimate the position ofMT [4]. K-nearest neighbors (KNN) is a popular method for its simplicity and performance. But parameter k and positions ofRPs have great effect on the performance ofKNN. In this paper, Fuzzy c-means (FCM) clustering algorithm, as a kind of fuzzy clustering algorithm, is combined with KNN to solve the problems. The rest of the paper is organized as follows: In the second section, the theories of KNN and FCM are firstly introduced and the KNN-FCM hybrid algorithm is presented subsequently. The experimental process is described in detail and experimental results are given and analyzed in the third section. Conclusion and future work are included in the fourth section. II. KNN-FCM HYBRID ALGORITHM A. K-nearest neighbors algorithm In KNN, the distances between measured RSS samples in on-line phase and the location fingerprints in radio-map are computed. Assuming the number of RPs and APs is m and n respectively. The distances are defined as follows Where Sij is the RSS sample from jth AP at ith RP in radio-map, Sj is the RSS sample from jth AP measured in on-line phase [5]. There are k RPs are chosen according to the first k minimum distances by (1), then the estimated location is calculated by (2) (1) Where (xi,yJ is coordinate of ith RP, (x,y) is the estimated coordinate of the test point (TP). For KNN algorithm, parameter k and q must be considered. Parameter k denotes the number of neighbors, which is the number of RPs for the coordinate estimation. …
Adaptive Control of Nonlinear Systems with Uncertain Dead-zone Nonlinearity

In this paper, we present a new scheme to design an adaptive controller for uncertain nonlinear systems with unknown dead-zone nonlinearity. The control design is achieved by introducing smooth approximate dead-zone model and certain well defined functions and by using backstepping technique. For the design and implementation of the controller, no knowledge is assumed on the uncertain system parameters and dead-zone parameters. It is shown that the proposed controller not only can guarantee global stability, but also can achieve excellent transient performance.
CONTRACTION OF EXCESS FIBRES BETWEEN THE MCKAY CORRESPONDENCES IN DIMENSIONS TWO AND THREE

Les singularites quotients de dimensions deux et trois obtenues par des groupes polyedraux et les groupes polyedraux binaires correspondants admettent des resolutions de singularites naturelles par les schemas de Hilbert d'orbites regulieres, dont les fibres exceptionnelles au-dessus de l'origine revelent des proprietes similaires. Nous construisons un morphisme entre ces deux resolutions, contractant, exactement la partie excedentaire de la fibre exceptionnelle. Cette construction est motivee par l'etude de certains pinceaux de surfaces K3 apparaissant comme resolutions minimales de quotients de surfaces nodales a grandes symetries.
Time Series Characterization of Gaming Workload for Runtime Power Management

Runtime power management using dynamic voltage and frequency scaling (DVFS) has been extensively studied for video processing applications. But there is only a little work on game power management although gaming applications are now widely run on battery-operated portable devices like mobile phones. Taking a cue from video power management, where PID controllers have been successfully used, they were recently applied to game workload prediction and DVFS. However, the use of hand-tuned PID controller gains on relatively short game plays left open questions on the robustness of the controller and the sensitivity of prediction quality on the choice of the gain values. In this paper, we try to systematically answer these questions. We first show that from the space of PID controller gain values, only a small subset leads to good game quality and power savings. Further, the choice of this set highly depends on the scene and the game application. For most gain values the controller becomes unstable, which can lead to large oscillations in the processor's frequency setting and thereby poor results. We then study a number of time series models, such as a Least Mean Squares (LMS) Linear Predictor and its generalizations in the form of Autoregressive Moving Average (ARMA) models. These models learn most of the relevant model parameters iteratively as the game progresses, thereby dramatically reducing the complexity of manual parameter estimation. This makes them deployable in real setups, where all game plays and even game applications are not a priori known. We have evaluated each of these models (PID, LMS, and ARMA) for a variety of games-ranging from Quake II to more recent closed-source games such as Crysis, Need for Speed-Shift and World in Conflict-with very encouraging results. To the best of our knowledge, this is the first work that systematically explores (a) the feasibility of manually tuning PID controller parameters for power management, (b) time series models for workload prediction for gaming applications, and (c) power management for closed-source games.
Message-efficient omission-tolerant consensus with limited synchrony

We study the problem of consensus in the general omission failure model, i.e., in systems where processes can crash and omit messages while sending or receiving. This failure model is motivated from a smart card-based security framework in which certain security problems can be reduced to consensus in that model. We propose an algorithm that solves consensus based on very weak timing assumptions. More precisely, we show that consensus is solvable using an eventual bisource and a majority of fault-free processes. An eventual bisource is a fault-free process that can eventually communicate with all other processes in a timely manner. In contrast to previous work, we use timing assumptions directly in the algorithm and do not employ the notion of a failure detector. We argue that this is helpful in reducing the message complexity, a critical aspect of algorithms which run on smart cards.
In vivo XRF measurement of lead in bone: theory and important progress

Lead is a leading environmental toxicant and Lead exposure has effects on many systems. Bone lead measured in vivo, has been shown to reflect cumulative lead exposure. Objective: To introduce and study in vivo XRF Measurements of Lead in Bone. Methods: Important progress and technology were summarized and introduced. Results: In vivo XRF is a safe and effective tool, which has enabled skeletal lead concentration measurement. X-ray spectrometry in vivo has contributed to the understanding of long-term human lead metabolism in ways that would not otherwise have happened. Conclusion: Efforts to introduce further improvements to the XRF technique are ongoing in order to lower its measurement uncertainty and enhance reproducibility. This may become especially important as the method becomes more widely used in the study of non-occupationally exposed populations, whose bone lead concentrations are generally quite low and thus comparable with the MDL of the standard XRF technique.
Flow Allocation with Path Protection in Next Generation Internet Networks

In this work, we study the flow allocation problem in Next Generation Internet (NGI) networks. Given a capacitated network, flow allocation consists of a simultaneous routing and bandwidth allocation problem for flows realizing a set of source-destination demands. We consider the case where demands are elastic and the flow realizing one demand can be arbitrary split over multiple paths. Moreover, we ensure that the allocated flows are survivable to network failures using path protection. We investigate the possibility of sharing backup capacity, thus enabling more efficient resource consumption. Depending on the optimization objective, we provide two different approaches to the problem. The first approach maximizes the total allocated volume and the second one maximizes the minimum allocated volume. We build mathematical formulations based on generalizations of the maximum multicommodity flow and the maximum concurrent flow problems. Moreover, we provide new approximation algorithms based on a primal-dual approach. These algorithms compute a solution that is within a guaranteed factor of the optimal. Numerical results enable to get deeper insights on the quality and compared performance of the different approaches.
Using flickr geotags to predict user travel behaviour

We propose a method to predict a user's favourite locations in a city, based on his Flickr geotags in other cities. We define a similarity between the geotag distributions of two users based on a Gaussian kernel convolution. The geotags of the most similar users are then combined to rerank the popular locations in the target city personalised for this user. We show that this method can give personalised travel recommendations for users with a clear preference for a specific type of landmark.
New ways of seeing: Health social work leadership and research capacity building

ABSTRACT Building research capacity amongst social work practitioners is critically important for leaders in the social work profession. To reverse an apparent reluctance to use evidence and engage in research, strong social work leadership in practice organisations is needed. The literature on leadership in health social work is relatively silent regarding research capacity building as a leadership attribute but it is argued in this paper that leadership is crucial. A programme of research capacity building and its outcomes in a health social work department is described, identifying key principles guiding its establishment and tasks undertaken. A transformational leadership style characterised this approach to research capacity building which delivered benefits to the staff and the service.
A Virtual Timing Device for Program Performance Analysis

Functional virtual platforms have been popularly used to support system development without needing the actual hardware. While the emulation process is fast enough to model the behaviors of complex systems, performance assessment cannot be done accurately due to the lack of timing models for the simulated systems. To tackle the problem, we proposed a virtual timing device (VTD) for a functional virtual platform to advance simulated clock time based on the hardware/software events observed during the emulation process. As a case study, we implemented the VTD in QEMU, an open-source virtual platform, with a variety of timing algorithms offering trade-offs between the accuracy and speed of timing estimation. With a fast, but less accurate timing algorithm, quick performance analysis can be done on QEMU at approximately 67 million instruction per second and reported execution time for the MiBench with an average of 15.7% error. Highly accurate performance profiles can be obtained by elaborating the timing model, e.g. with the addition of cache simulation, at the cost of simulation speed.
Effect of oral calcium disodium EDTA on iron absorption in a human model of iron overdose.

OBJECTIVE Anecdotal case reports and animal models have suggested that the administration of CaNa2EDTA (EDTA) may be effective in reducing the absorption of iron after an oral iron overdose. We designed this study to determine the effect of orally administrated EDTA with or without activated charcoal (AC) on iron absorption after a mild iron ingestion in healthy human volunteers. METHODS A randomized, crossover study was conducted in eight healthy human volunteers. All subjects ingested 5 mg/kg of elemental iron in the form of ferrous sulfate. One hour post ingestion, subjects were randomized to receive 35 mg/kg EDTA, EDTA plus 50 grams of AC, or water. Serial iron levels were obtained at baseline and every hour for the first 6 hours, then at 8, 12, and 24 hours. A 2-week washout was used between study arms. The Kruskal-Wallis test was used for the following comparisons between treatment groups: baseline serum iron levels, area under time-concentration curves (AUCs) from baseline to 12 hours and baseline to 24 hours, and peak iron levels. RESULTS Baseline serum iron levels did not differ among the three treatment groups (p = 0.844). AUCs were not different among groups (p = 0.746 for 12 hr, p = 0.925 for 24 hr). AUC medians (with 95% binomial confidence bounds) for control, EDTA, EDTA + AC groups, respectively, for 12 hr were: 2813 (2298, 3561), 2570 (1669, 3476), and 2654 (2125, 3600); and for 24 hr were: 4083 (3488,5314), 4139 (2666, 5547), and 4274 (3336, 5577). Peak serum iron levels did not differ among treatment groups (p = 0.481). Peak iron level medians in microg/dL (with 95% binomial confidence bounds) were for control: 329 (253, 382), for EDTA: 271 (184, 375), and for EDTA + AC: 285 (229, 352). CONCLUSION Orally administered EDTA did not significantly reduce iron absorption when administered 1 hour post iron ingestion during the 12 or 24-hour period following the ingestion of 5 mg/kg of elemental iron in healthy human volunteers.
Prognostic Value of Lead V1 ST Elevation During Acute Inferior Myocardial Infarction

Background— Lead V1 directly faces the right ventricle and may exhibit ST elevation during an acute inferior myocardial infarction when the right ventricle is also involved. Leads V1 and V3 indirectly face the posterolateral left ventricle, and ST depression (“mirror-image” ST elevation) in V1 through V3 may reflect concomitant posterolateral infarction. The prognostic significance of V1 ST elevation during an acute inferior myocardial infarction may therefore be dependent on V3 ST changes. Methods and Results— In 7967 patients with acute inferior myocardial infarction in the Hirulog and Early Reperfusion or Occlusion-2 (HERO-2) trial, V1 ST levels were analyzed with adjustment for lead V3 ST level for predicting 30-day mortality. V1 ST elevation at baseline, analyzed as a continuous variable, was associated with higher mortality. Unadjusted, each 0.5-mm-step increase in ST level above the isoelectric level was associated with ≈25% increase in 30-day mortality; this was true whether V3 ST depression was present or not. The odds ratio for mortality was 1.21 (95% confidence interval, 1.07 to 1.37) after adjustment for inferolateral ST elevation and clinical factors and 1.24 (95% confidence interval, 1.09 to 1.40) if also adjusted for V3 ST level. In contrast, lead V1 ST depression was not associated with mortality after adjustment for V3 ST level. V1 ST elevation ≥1 mm, analyzed dichotomously in all patients, was associated with higher mortality. The odds ratio was 1.28 (95% confidence interval, 1.01 to 1.61) unadjusted, 1.51 (95% confidence interval, 1.19 to 1.92) adjusted for V3 ST level, and 1.35 (95% confidence interval, 1.04 to 1.76) adjusted for ECG and clinical factors. Persistence of V1 ST elevation ≥1 mm 60 minutes after fibrinolysis was associated with higher mortality (10.8% versus 5.5%, P=0.001). Conclusion— V1 ST elevation identifies patients with acute inferior myocardial infarction who are at higher risk.
The Research on Method of Regional Urban Design Oriented by Gene Recombination Concept

Urban development is a process of continuous growth, in which historic context as a contact clues, has determined the regional expression of the city. Based on the regional context, taking regional city as research object, through analyzing the regional urban design theory development trend and practical problems, this paper tries to introduce the concepts of “gene recombination” into urban design of regional city, and takes the urban design of waterfront district of Yuanjiang county, Yunnan province for case study. It applies the method which expresses the context of “extraction-repair-integration” to reconstruct the regional context from three aspects of natural condition and physical space, social and cultural environment, economic and technical support. And it promotes integrating among regional natural, humanistic environment and material space form, and then achieves the goal of expression and continuation of cities with regional characteristics. At the same time, it would give a inspiring thinking way for the research on regional urban design.
Global optimization of factor models and dictionary learning using alternating minimization

Learning new representations in machine learning is often tackled using a factorization of the data. For many such problems, including sparse coding and matrix completion, learning these factorizations can be difficult, in terms of efficiency and to guarantee that the solution is a global minimum. Recently, a general class of objectives have been introduced, called induced regularized factor models (RFMs), which have an induced convex form that enables global optimization. Though attractive theoretically, this induced form is impractical, particularly for large or growing datasets. In this work, we investigate the use of a practical alternating minimization algorithms for induced RFMs, that ensure convergence to global optima. We characterize the stationary points of these models, and, using these insights, highlight practical choices for the objectives. We then provide theoretical and empirical evidence that alternating minimization, from a random initialization, converges to global minima for a large subclass of induced RFMs. Finally, we provide an extensive investigation into practical optimization choices for using alternating minimization for induced RFMs, for both batch and stochastic gradient descent.