Miracle-dz/UniFAR-FTUs huggingface dataset:
Through-Silicon-Via assignment for 3D ICs

Three-dimensional integrated circuits (3D ICs) can alleviate the interconnect problem coming with the decreasing feature size and increasing integration density, and promise a solution to heterogeneous integration. The inter-layer connection, which is generally implemented by the Through-Silicon-Via (TSV), is a key technology for 3D ICs. In this paper, we propose a unified simulated annealing technology to tackle the TSV assignment problem, including the signal TSV assignment of 3D nets and 3D buses. The experiment results show the effective of the method.
Perceptual Fusion Tendency of Speech Sounds

To discriminate and to recognize sound sources in a noisy, reverberant environment, listeners need to perceptually integrate the direct wave with the reflections of each sound source. It has been confirmed that perceptual fusion between direct and reflected waves of a speech sound helps listeners recognize this speech sound in a simulated reverberant environment with disrupting sound sources. When the delay between a direct sound wave and its reflected wave is sufficiently short, the two waves are perceptually fused into a single sound image as coming from the source location. Interestingly, compared with nonspeech sounds such as clicks and noise bursts, speech sounds have a much larger perceptual fusion tendency. This study investigated why the fusion tendency for speech sounds is so large. Here we show that when the temporal amplitude fluctuation of speech was artificially time reversed, a large perceptual fusion tendency of speech sounds disappeared, regardless of whether the speech acoustic carrier was in normal or reversed temporal order. Moreover, perceptual fusion of normal-order speech, but not that of time-reversed speech, was accompanied by increased coactivation of the attention-control-related, spatial-processing-related, and speech-processing-related cortical areas. Thus, speech-like acoustic carriers modulated by speech amplitude fluctuation selectively activate a cortical network for top–down modulations of speech processing, leading to an enhancement of perceptual fusion of speech sounds. This mechanism represents a perceptual-grouping strategy for unmasking speech under adverse conditions.
Perspectives on the non-stationarity of the relationship between Indian and East Asian summer rainfall variations

Abstract The relationship between Indian and East Asian summer rainfall variations is non-stationary in observations as well as in historical simulations of climate models. Is this non-stationarity due to changes in effects of external forcing or internal atmospheric processes? Whilst ENSO is an important oceanic forcing of Indian and East Asian summer rainfall variations, its impacts cannot explain the observed long-term changes in the Indian–East Asian summer rainfall relationship. Monte Carlo test indicates that the role of random processes cannot be totally excluded in the observed long-term changes of the relationship. Analysis of climate model outputs shows that the Indian–North China summer rainfall relationship displays obvious temporal variations in both individual and ensemble mean model simulations and large differences among model simulations. This suggests an important role played by atmospheric internal variability in changes of the Indian–East Asian summer rainfall relationship. This point of view is supported by results from a 100-years AGCM simulation with climatological SST specified in the global ocean. The correlation between Indian and North China or southern Japan summer rainfall variations displays large fluctuations in the AGCM simulation.
On the achievable rate of the fading dirty paper channel with imperfect CSIT

The problem of dirty paper coding (DPC) over the (multi-antenna) fading dirty paper channel (FDPC) Y = H(X + S) + Z is considered when there is imperfect knowledge of the channel state information H at the transmitter (CSIT). The case of FDPC with positive definite (p.d.) input covariance matrix was studied by the authors in a recent paper, and here the more general case of positive semi-definite (p.s.d.) input covariance is dealt with. Towards this end, the choice of auxiliary random variable is modified. The algorithms for determination of inflation factor proposed in the p.d. case are then generalized to the case of p.s. d. input covariance. Subsequently, the largest DPC-achievable high-SNR (signal-to-noise ratio) scaling factor over the no-CSIT FDPC with p.s. d. input covariance matrix is derived. This scaling factor is seen to be a non-trivial generalization of the one achieved for the p.d. case. Next, in the limit of low SNR, it is proved that the choice of all-zero inflation factor (thus treating interference as noise) is optimal in the ‘ratio’ sense, regardless of the covariance matrix used. Further, in the p.d. covariance case, the inflation factor optimal at high SNR is obtained when the number of transmit antennas is greater than the number of receive antennas, with the other case having been already considered in the earlier paper. Finally, the problem of joint optimization of the input covariance matrix and the inflation factor is dealt with, and an iterative numerical algorithm is developed.
Task-Specific Stability of Multifinger Steady-State Action

ABSTRACT The authors explored task-specific stability during accurate multifinger force production tasks with different numbers of instructed fingers. Subjects performed steady-state isometric force production tasks and were instructed not to interfere voluntarily with transient lifting-and-lowering perturbations applied to the index finger. The main results were (a) intertrial variance in the space of finger modes at steady states was larger within the subspace that had no effect on the total force (the uncontrolled manifold [UCM]); (b) perturbations caused large deviations of finger modes within the UCM (motor equivalence); and (c) deviations caused by the perturbation showed larger variance within the UCM. No significant effects of the number of task fingers were noted in any of the 3 indicators. The results are discussed within the frameworks of the UCM and referent configuration hypotheses. The authors conclude, in particular, that all the tasks were effectively 4-finger tasks with different involvement of task and nontask fingers.
CHARACTERIZING X-RAY AND RADIO EMISSION IN THE BLACK HOLE X-RAY BINARY V404 CYGNI DURING QUIESCENCE

We present results from multi-wavelength simultaneous X-ray and radio observations of the black hole X-ray binary V404 Cyg in quiescence. Our coverage with NuSTAR provides the very first opportunity to study the X-ray spectrum of V404 Cyg at energies above 10 keV. The unabsorbed broadband (0.3–30 keV) quiescent luminosity of the source is 8.9 × 1032 erg s−1 for a distance of 2.4 kpc. The source shows clear variability on short timescales (an hour to a couple of hours) in the radio, soft X-ray, and hard X-ray bands in the form of multiple flares. The broadband X-ray spectra obtained from XMM-Newton and NuSTAR can be characterized with a power-law model having a photon index of Γ = 2.12 ± 0.07 (90% confidence errors); however, residuals at high energies indicate spectral curvature significant at a 3σ confidence level with the e-folding energy of the cutoff as 20 − 7 + 20 keV. Such curvature can be explained using synchrotron emission from the base of a jet outflow. Radio observations using the VLA reveal that the spectral index evolves on very fast timescales (as short as 10 minutes), switching between optically thick and thin synchrotron emission, possibly due to instabilities in the compact jet or stochastic instabilities in the accretion rate. We explore different scenarios to explain this very fast variability.
Continuous-flow analysis: the Auto-Analyzer

An increase in the work-load of clinical and analytical chemistry laboratories in the 1940s resulted in a drive towards automation. The repetitive nature of analyses, with the associated tedium, provided further incentive toward developing automated systems. To these ends, Skeggs conceived Continuous-Flow analysis (CFA), introduced commercially by the Technicon Instruments Corporation in 1957 in the form of an automated system under the name ’Auto-Analyzer’ [1 and 2].
Content-based query of image databases, inspirations from text retrieval: inverted les, frequency-based weights and relevance feedback

In this paper we report the application of techniques inspired by text retrieval research to the content-based query of image databases. In particular, we show how the use of an inverted le data structure permits the use of a feature space of O(10) dimensions, by restricting search to the subspace spanned by the features present in the query. A suitably sparse set of colour and texture features is proposed. A scheme based on the frequency of occurrence of features in both individual images and in the whole collection provides a means of weighting possibly incommensurate features in a compatible manner, and naturally extends to incorporate relevance feedback queries. The use of relevance feedback is shown consistently to improve system performance, as measured by precision and recall.
Increasing the Effectiveness of Prediction in Recommendation Engines Based on Collaborative Filtering

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations. This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall. The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups. As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences.
Temporally coherent completion of dynamic shapes

We present a novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances. Because of occlusions and low surface albedo, scanned mesh sequences typically exhibit large holes that persist over extended periods of time. Most conventional dynamic shape reconstruction techniques rely on template models or assume slow deformations in the input data. Our framework sidesteps these requirements and directly initializes shape completion with topology derived from the visual hull. To seal the holes with patches that are consistent with the subject's motion, we first minimize surface bending energies in each frame to ensure smooth transitions across hole boundaries. Temporally coherent dynamics of surface patches are obtained by unwarping all frames within a time window using accurate interframe correspondences. Aggregated surface samples are then filtered with a temporal visibility kernel that maximizes the use of nonoccluded surfaces. A key benefit of our shape completion strategy is that it does not rely on long-range correspondences or a template model. Consequently, our method does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes. We illustrate the effectiveness of our method on several high-resolution scans of human performances captured with a state-of-the-art multiview 3D acquisition system.
Quantification of cell surface proteins with bispecific antibodies

Flow cytometry is an established method for fast and accurate quantitation of cellular protein levels and requires fluorescently labeled antibodies as well as calibration standards. A critical step for quantitation remains the production of suitable detection antibodies with a precisely defined ratio of antigen-binding sites to fluorophores. Problems often arise as a consequence of inefficient and unspecific labeling which can influence antibody properties. In addition, the number of incorporated fluorophores necessitates a special normalization step for quantitation. To address these problems, we constructed different mono- and bivalent bispecific antibodies with binding site(s) for the cell surface antigens, cMET, EGFR1/HER1, ErbB2/HER2 or ErbB3/HER3 and with an additional digoxigenin-binding single-chain Fv fusion. The fluorophore Cy5 was covalently coupled to digoxigenin and quantitatively bound by the bispecific antibody. A panel of tumor cell lines was assessed under different culture conditions for absolute receptor expression levels of the indicated antigens and the data were set in relation to mRNA, gene count and immunoblot data. We could reproducibly quantify these receptors, omit the otherwise required normalization step and demonstrate the superiority of a 1 + 1 bispecific antibody. The same antibodies were also used to quantify the number of proteins in intracellular vesicles in confocal microscopy. The antibodies can be stored like regular antibodies and can be coupled with different digoxigenin-labeled fluorophores which makes them excellent tools for FACS and imaging-based experiments.
Energy optimization of distributed embedded processors by combined data compression and functional partitioning

Transmitting compressed data can reduce inter-processor communication traffic and create new opportunities for DVS (dynamic voltage scaling) in distributed embedded systems. However, data compression alone may not be effective unless coordinated with functional partitioning. This paper presents a dynamic programming technique that combines compression and functional partitioning to minimize energy on multiple voltage-scalable processors running pipelined data-regular applications under performance constraints. Our algorithm computes the optimal functional partitioning, CPU speed for each node, and their respective compression ratios. We validate the algorithm's effectiveness on a real distributed embedded system running an image processing algorithm.
Chronic liver disease and active hepatitis C virus infection in patients with antibodies to this virus.

AIMS--To assess the association between active hepatitis C virus (HCV) infection and liver damage in randomly selected patients with antibodies to the virus. METHODS--Thirty three consecutive subjects with serologically confirmed positivity for antibodies to HCV were studied for the presence of liver and circulating viral sequences by using the reverse transcription polymerase chain reaction (RT-PCR) and specific primers for the 5'-untranslated region (5'-UTR) of the HCV genome. Parallel clinical, biochemical, and histological investigations were carried out in all cases. RESULTS--A comparative virological and histological investigation showed the presence of molecular signs of active viral replication and different degrees of liver damage in all cases. Baseline values of liver and plasma samples from all the patients showed (with one exception) the presence of detectable HCV RNA sequences, despite alanine amino transferase activities being within normal values or within 1.5 times the upper limit of normal in 13 of them. Examination of percutaneous liver biopsy specimens showed the presence of confirmed liver damage (ranging from chronic persistent hepatitis to cirrhosis) in all 33 patients. CONCLUSIONS--Circulating HCV RNA sequences (a direct sign of active HCV infection) are associated with liver damage, even in the absence of clinical or biochemical signs of overt liver disease. Parallel molecular, histological, and clinical follow up of these patients is needed to understand precisely the natural history of HCV infection and for correct clinical management.
Plasticity of the cis-Regulatory Input Function of a Gene

The transcription rate of a gene is often controlled by several regulators that bind specific sites in the gene's cis-regulatory region. The combined effect of these regulators is described by a cis-regulatory input function. What determines the form of an input function, and how variable is it with respect to mutations? To address this, we employ the well-characterized lac operon of Escherichia coli, which has an elaborate input function, intermediate between Boolean AND-gate and OR-gate logic. We mapped in detail the input function of 12 variants of the lac promoter, each with different point mutations in the regulator binding sites, by means of accurate expression measurements from living cells. We find that even a few mutations can significantly change the input function, resulting in functions that resemble Pure AND gates, OR gates, or single-input switches. Other types of gates were not found. The variant input functions can be described in a unified manner by a mathematical model. The model also lets us predict which functions cannot be reached by point mutations. The input function that we studied thus appears to be plastic, in the sense that many of the mutations do not ruin the regulation completely but rather result in new ways to integrate the inputs.
Country of origin, social support and the risk of small for gestational age birth

Aims: This study investigates the risk of small for gestational age (SGA) in relation to country of origin of the mother. The role of psychosocial resources, socioeconomic and lifestyle factors was examined in different causal models. Methods: Among all pregnant nulliparous women in the city of Malmö, Sweden, who gave birth in 1991 - 92, 872 (87.7%) women completed a questionnaire during their first antenatal visit. The study was carried out among women whose pregnancies resulted in a singleton live birth (n=826); 22% (n=182) of these women were foreign-born. Results: Fifty-five (6.7%) of the infants were classified as SGA, 37 (5.7%) of mothers of Swedish origin and 18 (9.7%) of foreign origin. SGA deliveries were much more prevalent among Middle East- and North Africa-born women (22%) and sub-Saharan-born women (15%). In all, women of foreign origin had increased odds for delivering SGA babies (OR=1.8, 95% CI=1.0,3.2). In a multivariate analysis psychosocial and socioeconomic factors explained 30% and 40%, respectively, of the increased SGA risk. Psychosocial factors seemed to be more prominent risk factors for SGA among mothers of foreign origin. A possible synergistic relation was demonstrated between foreign origin of the mother and low social anchorage. Conclusions: This study showed that psychosocial factors, most probably linked to a disadvantaged social situation, could be the theoretically most important focus for preventing SGA in immigrant women. This could also further support a hypothesis of a link between psychosocial stress and SGA in general. However, this should not exclude the need for intervention in the antenatal care system in terms of specially tailored support and education.
When Neurons Fail - Technical Report

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks are now appealing solutions, cannot afford any additional learning at run-time. In this paper, we view a multilayer neural network as a distributed system of which neurons can fail independently, and we evaluate its robustness in the absence of any (recovery) learning phase. We give tight bounds on the number of neurons that can fail without harming the result of a computation. To determine our bounds, we leverage the fact that neural activation functions are Lipschitz-continuous. Our bound is on a quantity, we call the Forward Error Propagation, capturing how much error is propagated by a neural network when a given number of components is failing, computing this quantity only requires looking at the topology of the network, while experimentally assessing the robustness of a network requires the costly experiment of looking at all the possible inputs and testing all the possible configurations of the network corresponding to different failure situations, facing a discouraging combinatorial explosion. We distinguish the case of neurons that can fail and stop their activity (crashed neurons) from the case of neurons that can fail by transmitting arbitrary values (Byzantine neurons). In the crash case, our bound involves the number of neurons per layer, the Lipschitz constant of the neural activation function, the number of failing neurons, the synaptic weights and the depth of the layer where the failure occurred. In the case of Byzantine failures, our bound involves, in addition, the synaptic transmission capacity. Interestingly, as we show in the paper, our bound can easily be extended to the case where synapses can fail. We present three applications of our results. The first is a quantification of the effect of memory cost reduction on the accuracy of a neural network. The second is a quantification of the amount of information any neuron needs from its preceding layer, enabling thereby a boosting scheme that prevents neurons from waiting for unnecessary signals. Our third application is a quantification of the trade-off between neural networks robustness and learning cost.