1,750 research outputs found
Managing open innovation projects: an evidence-based framework for SMEs and large companies cooperation
No full textPurpose – How can joint open innovation (OI) projects between small and medium-sized enterprises (SMEs)
and large companies (LCs) be effectively managed? This study aims to try to answer this research question
with a focus on the critical success factors (CSFs) of such cooperation.
Design/methodology/approach – Based on 40 semi-structured interviews with Italian SMEs and LCs
engaged in various industries, 20 open OI projects involving SMEs and LCs are investigated using a reflexive
thematic analysis, a methodology involving both deductive and inductive approaches.
Findings – Fifteen CSFs grouped into seven categories emerge from the analysis of joint OI projects
between SMEs and LCs. Among them, shared leadership, dynamic decision-making and priority setting
emerge as essential elements at the basis of the proposed SMEs–LCs cooperation in joint OI projects that were
not sufficiently addressed by prior studies.
Originality/value – To the best of the authors’ knowledge, this study is the first to provide an evidence-
based framework for managing joint OI projects between SMEs and LCs. Relatedly, this study links the
practices and most recurring CSFs that facilitate such cooperation.
Keywords Decision-making, Open innovation, Critical success factors,
Small and medium-sized enterprises, Strategic flexibility, Large companies,
Knowledge and innovation management, Project managemen
Double-Bundle Medial Patellofemoral Ligament Reconstruction With a Single Patellar Tunnel
No full textMedial patellofemoral ligament (MPFL) reconstruction is an established method to prevent patellofemoral instability. Nevertheless, the anatomy and the biomechanical behavior of native MPFL are still under investigation, but in recent years they have become more defined. We propose a technique for MPFL reconstruction based on the results of recent anatomic studies regarding the patellar insertion of the MPFL. A double-bundle MPFL is reconstructed by use of the semitendinosus tendon passed through a single patellar tunnel, which crosses the patella from the midpoint of its medial border until its superolateral corner is reached. This method permits a strong patellar fixation, potentially reducing the risk of patellar fracture compared with double-patellar tunnel techniques. Moreover, it requires no fixation devices at the patella and only a single interference screw on the femoral side. © 2013 Arthroscopy Association of North America
Supraspinatus rupture at the musculotendinous junction in a young woman
No full textThe vast majority of rotator cuff tears occur within the tendon or as an avulsion from the greater tuberosity. Supraspinatus injury at the musculotendinous junction is a very uncommon event. We describe a case of supraspinatus rupture at the musculotendinous junction, with successful conservative treatment. It occurred in a 23-year-old woman, the youngest patient with this uncommon type of injury. To our knowledge, this is the first case of rupture of the supraspinatus muscle at the musculotendinous junction in a young woman and the second in a woman
Framing Apache Spark in life sciences
No full textAdvances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
No full textAmongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientifc community developed strategies to order the examples
according to their estimated complexity, to distil knowledge
from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently
proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process
of a neural classifer. The transformation progressively fadesout as long as training proceeds, until it completely vanishes.
In this work we revisit and extend this idea, introducing a
radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial
Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make
them easier to be handled by the classifer at the current stage
of the training procedure. The auxiliary network is trained
jointly with the neural classifer, thus intrinsically increasing
the “depth” of the classifer, and it is expected to spot general regularities in the data alteration process. The effect of
the auxiliary network is progressively reduced up to the end
of training, when it is fully dropped and the classifer is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure
involving several datasets and different neural architectures
shows that Neural Friendly Training overcomes the originally
proposed Friendly Training technique, improving the generalization of the classifer, especially in the case of noisy data
Continual learning of conjugated visual representations through higher-order motion flows
No full textLearning with neural networks from a continuous stream of visual information presents several challenges due to the non-i.i.d. nature of the data. However, it also offers novel opportunities to develop representations that are consistent with the information flow. In this paper we investigate the case of unsupervised continual learning of pixel-wise features subject to multiple motion-induced constraints, therefore named motion-conjugated feature representations. Differently from existing approaches, motion is not a given signal (either ground-truth or estimated by external modules), but is the outcome of a progressive and autonomous learning process, occurring at various levels of the feature hierarchy. Multiple motion flows are estimated with neural networks and characterized by different levels of abstractions, spanning from traditional optical flow to other latent signals originating from higher-level features, hence called higher-order motions. Continuously learning to develop consistent multi-order flows and representations is prone to trivial solutions, which we counteract by introducing a self-supervised contrastive loss, spatially-aware and based on flow-induced similarity. We assess our model on photorealistic synthetic streams and real-world videos, comparing to pre-trained state-of-the art feature extractors (also based on Transformers) and to recent unsupervised learning models, significantly outperforming these alternatives
Lateral ankle instability in high-demand athletes: reconstruction with fibular periosteal flap
No full textPurpose: Fibular periosteal flaps have been used to address chronic lateral ankle instability, but there are no studies in the literature reporting functional outcomes after this particular procedure in high-demand athletes. We postulated that for chronic instability, nonanatomical reconstruction of the lateral ankle ligament with a fibular periosteal flap will return high-demand athletes to their previous levels of activity. Methods: Forty patients who had grade III ankle sprain and experienced no success after a course of supervised conservative management lasting at least six months and who had a preinjury Tegner score of ≥6 underwent a lateral compartment reconstruction with a fibular periosteal flap. Each patient was given the Tegner and Karlsson questionnaire and was evaluated by the Zwipp method, Foot and Ankle Outcome Score (FAOS) and the American Orthopaedic Foot and Ankle Society (AOFAS) score at the six-month, one, two and three-year time points. Range of motion (ROM) of the affected ankle was assessed, and stress X-rays were performed. Mean patient age was 24.5 (range17-30) years, and no patient was lost to follow-up. Results: Mean follow-up was 36 (minimum 18) months, mean Tegner scores at the one, two and three-year time points were 8.8, 8.9 and 8.9, respectively, and mean Karlsson scores were 93 ± 5.2, 95 ± 3.1 and 94.9, respectively. AOFAS and FAOS scores improved from a mean of 69.4 and 71.4, respectively, in the preoperative group to a mean of 97.2 and 94.4, respectively, at the last follow-up. The ROM was equal to the contralateral ankle in all but two patients at the two-year follow-up. No major complications were found. Conclusion: Nonanatomical ligament reconstruction with a fibular periosteal flap for chronic lateral ankle instability was effective in returning high-demand athletes to their preinjury functional levels
TUTELA DEL LAVORO E LIBERTA' D'IMPRESA NEI PROCESSI DI ESTERNALIZZAZIONE
No full textL’elaborato analizza le conseguenze lavoristiche della successione fra imprenditori, muovendo da una ricognizione delle varie tipologie di esternalizzazione con le relative esigenze e principali criticità.
L’indagine si concentra in primo luogo sul trasferimento d’azienda, esaminando la normativa e la giurisprudenza europee per passare poi alla disciplina di diritto interno, alle procedure sindacali e a uno specifico focus sul trasferimento delle aziende in crisi.
Successivamente l’autore si sofferma sull’appalto, prendendone in particolare considerazione gli indici di genuinità, i criteri di distinzione dalla somministrazione illecita di manodopera e la tutela delle maestranze in caso di avvicendamento fra imprese.
Da ultimo, la ricerca approfondisce le c.d. “clausole sociali”, sia di prima che di seconda generazione, valutandone la compatibilità con il diritto eurounitario e con la costituzione nonché riflettendo sui possibili rimedi in caso di loro violazione.The author analyzes the labour consequences of the succession between entrepreneurs, starting from a recognition of the various types of outsourcing with the related needs and main critical issues.
The survey focuses primarily on the transfer of businesses, examining European legislation and case-law and then moving on to internal legislation, trade union procedures and a specific focus on the transfer of companies in crisis.
The author then dwells on the contract, taking into account in particular the indications of authenticity, the criteria of distinction from the illicit administration of labour and the protection of workers in the event of turnover between companies.
Finally, the research deepens the "social clauses", both first and second generation, assessing their compatibility with European law and with the constitution and reflecting on possible remedies in case of their violation
Archaeology of Inhabited Ruins / Cerberus: The Three-Headed Monster
No full textTwo projects for the Kuwait Pavilion, 15th International Architectural Exhibition, La Biennale di Venezi
Continual Learning with Pretrained Backbones by Tuning in the Input Space
No full textThe intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, incrementally fine-tuning the whole model to better adapt to new tasks usually results in catastrophic forgetting, with decreasing performance over the past experiences and losing valuable knowledge from the pretraining stage. In this paper, we propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters that are responsible for transforming the input data. This process allows the network to effectively leverage the pre-training knowledge and find a good trade-off between plasticity and stability with modest computational efforts, thus especially suitable for on-the-edge settings. Our experiments on four image classification problems in a continual learning setting confirm the quality of the proposed approach when compared to several fine-tuning procedures and to popular continual learning methods
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